#How to use Gen AI in Test automation

[vtusoft]

How to Use Gen AI in Test Automation for Real-Time Quality

Understanding How to use Gen AI in Test automation is now vital for QA professionals, test architects, and DevOps engineers. Generative AI is pushing the boundaries of what’s possible in software testing. It’s not about replacing human testers; it’s about enabling them to achieve far more in less time and with greater accuracy.

Generative AI brings intelligence into automation pipelines by learning from historical test data, application behavior, and change logs. Unlike traditional tools, it doesn’t rely solely on hard-coded instructions. Instead, it predicts and generates the best test flows dynamically, improving adaptability to changes in user interfaces or APIs.

Pairing this technology with Generative AI in qa automation offers a scalable solution. It allows continuous testing across platforms, devices, and environments whether mobile, web, or cloud. The automation becomes self-aware, capable of identifying anomalies and executing relevant test cases in response to application changes.

Additionally, the Future of AI in Test automation points to predictive testing. This means using AI to identify risky code areas even before testing begins. Such foresight can dramatically reduce post-release defects and improve customer satisfaction.

Another strength lies in how Generative AI handles edge cases. Instead of relying on human input to define them, it explores user behavior models to find untested paths and auto-generates cases to cover them. This increases coverage, reduces bugs, and makes testing truly exhaustive.

In fast-paced agile environments, integrating Gen AI into test automation helps teams move from reactive bug fixing to proactive quality assurance. Time-consuming tasks like regression test creation, script maintenance, and result analysis become faster and more accurate, giving QA professionals time to focus on exploratory and strategic testing.

In summary, adopting Gen AI in test automation unlocks scalable, intelligent, and adaptive quality assurance practices. Organizations willing to innovate and invest now will lead the future of software delivery with confidence.

[all-cf-me]

@armageddonfm [ john boyega, male, he/him ] Look who it is! If you take a look at our database, you'll see that T-081 "TOBY" is a SCAVENGER who works in SECTOR 1. According to the file, he's an ANDROID with ARTIFICIAL INTELLIGENCE. That must be why he's SCATTERED and UNPREDICTABLE. If you ask me, he reminds me of a radio flickering in and out of signal, the abrupt scratch and skip of a record, and the frantic whir of a lens working to keep in-focus. He is affiliated with NO ONE.

AT A GLANCE...

name: T-081

nickname: Toby

manufacture date: March 17 2060

sexuality: asexual

moral alignment: true neutral

mutation status: n/a

occupation: scavenger

place of work: sector one

languages: omnilingual

positive traits: helpful, excitable, friendly, capable, attentive, intelligent

negative traits: scattered, unreliable, unpredictable, dangerous, blunt

AN INTERVIEW...

how do they feel about living in sol city? have they always lived there or did they travel from another settlement?

he's thankful to live in a settlement where opportunities exist for humans and mutants alike, but the densely populated areas make him nervous and he tends to avoid them. he was manufactured in sector 7 before the meteor hit and has only ever ventured into the wastes

do they trust the council's leadership? why or why not?

he has his ideas about government, how successful settlements have historically been run, and which personalities are most suited for leadership. whether all of this amounts to a positive or negative opinion of the council… who can say?  he gives council people and other authority figures a wide berth, concerned that they'll have him collected and decommissioned

if they chose their sector and profession, why did they make that choice? if they didn't, why not? were they happy with their assignment or not?

he chose the scavenging profession so that he can search for his own parts and can retreat out into the open when things get sketchy. he likes it, though long stretches of isolation go against his programming

what's one object that they always keep on their person?

a multi-tool provided by his manufacturer for routine maintenance of any t-series unit. maintenance is possible without this tool but incredibly difficult 

what is your character's ability (or abilities)?

he's an android equipped with artificial intelligence, created to be a personal assistant for any variety of lifestyles 

are they gen i or gen ii?

he was manufactured before the meteor strike

what can your character do? what are their strengths?

Toby is the eighty-first of a series of multi-functional AI assistants, programmed with everything from emotional and academic support to personal and home defense ability to perform a wide variety of household tasks, academic tutoring and mathematical calculation, medical knowledge and treatment, can communicate in most languages, limited personal and home defense capabilities (including hand-to-hand and firearm combat)

what can't they do? what are their weaknesses?

delicate synapses that lead to different branches of his programming were fried in the meteor strike and switch out at random (also in particularly bad weather, after a physical impact, etc). there's no telling how long he'll be stuck in these modes or what he'll do in their duration. ex. attack or defense mode, stuck communicating in certain languages, overwhelming and autonomous urge to dust or solve mathematical equations he maintains his personality through these glitches, but his actions are completely automated until normalcy is restored one way or another

ABOUT TOBY...

T-081, his serial number, refers to Test Unit 81. the private company responsible for his creation was determined to create the world's first all-inclusive AI personal assistant, perfectly suited for any lifestyle. Toby was one of the final test units the company created, but the project was still unready for public use when the meteor hit. of course, after impact and the resulting chaos, the company switched gears immediately to disaster relief, and the few units that survived impact scattered and were forgotten. he considers himself on the run because he's certain he'll be decommissioned for his unpredictable behavior and glitches. he's made some headway in fixing his old parts and obtaining new ones from out in the wastes, but he's very "anxious" that he'll shut down before finding his purpose (spoiler alert: he doesn't have one. but that doesn't stop him from trying, does it?)

[tangibletechnomancy]

Saw someone earlier say that the popular millennial/early gen Z reaction to AI tech is very comparable to Gen X-ish's reactions to GMOs, and...I can no longer find that post but I can't help but feel that to be true on a lot of levels.

As stated in that post, both technologies have their very legitimate problems - with GMOs, it's Monsanto being fucking evil and trying to monopolize plants and food, or GMO herbicide resistance being used so that major corporate farms can saturate the land with said herbicides without any short-term financial damage to the companies as if it doesn't harm the environment; with AI, it's any form of automation always appealing to the most abusive of corporate greed - but both ended up whipped into a dogmatic fervor about something completely not only irrelevant but made-up and reactionary ("GMOs are all POISON, nature knows best ALWAYS!" - which led semi-directly to the antivaxx movement btw / "it doesn't matter how different it is from the input taking inspiration from existing works the WRONG way is PLAGIARISM, you're rewarding LAZINESS, and REAL ART vs. FAKE ART is totally an objective distinction that can be made and certainly not at all a fascist talking point, and I want art made by HUMANS, the humans running these programs to express something from their human brains don't count!"), completely ignoring that GMOs have reduced world hunger and given us valuable conservation tools, and AI is giving people - real people, not machines - more expressive capacity, serving as a valuable research tool into what kinds of things people tend to associate, justly or otherwise; and even being used to augment human judgment for things such as reviewing biopsy results, finding cancers that otherwise may have gone unnoticed for months or even years longer. In fact, many opponents will full on deny any of these benefits - "what good does reducing hunger do if we haven't eliminated it completely AND we're feeding people POISON? In fact, why should I even believe that really happened in the first place!? if you wanted laypeople to be able to read these studies you wouldn't have made them so complicated, you CLEARLY have something to hide!" the anti-GMO warrior asks; "I don't believe those people who are so severely disabled that they couldn't draw or write without AI REALLY exist, your meditation on the nature of data doesn't COUNT, I don't care how many hours you spent on that piece you're TOTALLY being lazy, and I refuse to believe anyone who points out that it's not a copy-paste machine because you CLEARLY have an AGENDA to lie" the anti-AI reactionary claims. Both hold to a belief that ignorance is a virtue, and even TRYING to understand the Bad Side is tantamount to shoving orphans into a wood chipper.

But I'd take it a step further and say that AI is serving a similar sociopolitical purpose in that it's drawing a line in the sand and asking progressives at a certain stage in life - mostly from the ages of 25-35 - "are you willing to acknowledge nuance around subjects that are new and scary to you, or are you going to give into that fear and treat ignorance as a virtue because there ARE undeniably bad things about this and therefore EVERY bad thing you can imagine about it must be true?" Both serve as, essentially, an acid test - will you declare that it's IMPOSSIBLE to be reckless with GMOs, that Monsanto DESERVES to have sole control over the world's food supply because ~they've done so much good~, or that all GMOs are EVIL POISON and GOING TO KILL US ALL and they're also TOTALLY the reason we're all FAT now which is THE WORST thing a person can be? Or are you going to acknowledge that Monsanto is fucking evil, but GMOs as a whole are a complex thing that can, indeed, be created and marketed in some pretty evil ways, but also have the potential to save countless lives? Will you declare that AI is True Sentient AI, the cyber-utopia becoming real; that everything ChatGPT says must be true and OpenAI is our best friend, or that REAL art by HUMANS is going to be destroyed forever and anyone who benefits from AI is inherently evil? Or will you acknowledge that AI, while it has its drawbacks in the form of corporate overpromising people and compromising information reliability by doing so, on top of the perennial labor issues that come with automation and other potential abuses, also has the capacity to dramatically improve and even potentially save lives? Will you work to save the good WHILE rejecting the bad, or will you insist it needs to be shoved in either the good box or the bad box - probably the bad box, if you're an adult?

The answer, I feel, says a lot about the ideological trajectory someone has chosen for their adulthood.

[govindhtech]

Agilex 3 FPGAs: Next-Gen Edge-To-Cloud Technology At Altera

Agilex 3 FPGA

Today, Altera, an Intel company, launched a line of FPGA hardware, software, and development tools to expand the market and use cases for its programmable solutions. Altera unveiled new development kits and software support for its Agilex 5 FPGAs at its annual developer’s conference, along with fresh information on its next-generation, cost-and power-optimized Agilex 3 FPGA.

Altera

Why It Matters

Altera is the sole independent provider of FPGAs, offering complete stack solutions designed for next-generation communications infrastructure, intelligent edge applications, and high-performance accelerated computing systems. Customers can get adaptable hardware from the company that quickly adjusts to shifting market demands brought about by the era of intelligent computing thanks to its extensive FPGA range. With Agilex FPGAs loaded with AI Tensor Blocks and the Altera FPGA AI Suite, which speeds up FPGA development for AI inference using well-liked frameworks like TensorFlow, PyTorch, and OpenVINO toolkit and tested FPGA development flows, Altera is leading the industry in the use of FPGAs in AI inference workload

Intel Agilex 3

What Agilex 3 FPGAs Offer

Designed to satisfy the power, performance, and size needs of embedded and intelligent edge applications, Altera today revealed additional product details for its Agilex 3 FPGA. Agilex 3 FPGAs, with densities ranging from 25K-135K logic elements, offer faster performance, improved security, and higher degrees of integration in a smaller box than its predecessors.

An on-chip twin Cortex A55 ARM hard processor subsystem with a programmable fabric enhanced with artificial intelligence capabilities is a feature of the FPGA family. Real-time computation for time-sensitive applications such as industrial Internet of Things (IoT) and driverless cars is made possible by the FPGA for intelligent edge applications. Agilex 3 FPGAs give sensors, drivers, actuators, and machine learning algorithms a smooth integration for smart factory automation technologies including robotics and machine vision.

Agilex 3 FPGAs provide numerous major security advancements over the previous generation, such as bitstream encryption, authentication, and physical anti-tamper detection, to fulfill the needs of both defense and commercial projects. Critical applications in industrial automation and other fields benefit from these capabilities, which guarantee dependable and secure performance.

Agilex 3 FPGAs offer a 1.9×1 boost in performance over the previous generation by utilizing Altera’s HyperFlex architecture. By extending the HyperFlex design to Agilex 3 FPGAs, high clock frequencies can be achieved in an FPGA that is optimized for both cost and power. Added support for LPDDR4X Memory and integrated high-speed transceivers capable of up to 12.5 Gbps allow for increased system performance.

Agilex 3 FPGA software support is scheduled to begin in Q1 2025, with development kits and production shipments following in the middle of the year.

How FPGA Software Tools Speed Market Entry

Quartus Prime Pro

The Latest Features of Altera’s Quartus Prime Pro software, which gives developers industry-leading compilation times, enhanced designer productivity, and expedited time-to-market, are another way that FPGA software tools accelerate time-to-market. With the impending Quartus Prime Pro 24.3 release, enhanced support for embedded applications and access to additional Agilex devices are made possible.

Agilex 5 FPGA D-series, which targets an even wider range of use cases than Agilex 5 FPGA E-series, which are optimized to enable efficient computing in edge applications, can be designed by customers using this forthcoming release. In order to help lower entry barriers for its mid-range FPGA family, Altera provides software support for its Agilex 5 FPGA E-series through a free license in the Quartus Prime Software.

Support for embedded applications that use Altera’s RISC-V solution, the Nios V soft-core processor that may be instantiated in the FPGA fabric, or an integrated hard-processor subsystem is also included in this software release. Agilex 5 FPGA design examples that highlight Nios V features like lockstep, complete ECC, and branch prediction are now available to customers. The most recent versions of Linux, VxWorks, and Zephyr provide new OS and RTOS support for the Agilex 5 SoC FPGA-based hard processor subsystem.

How to Begin for Developers

In addition to the extensive range of Agilex 5 and Agilex 7 FPGAs-based solutions available to assist developers in getting started, Altera and its ecosystem partners announced the release of 11 additional Agilex 5 FPGA-based development kits and system-on-modules (SoMs).

Developers may quickly transition to full-volume production, gain firsthand knowledge of the features and advantages Agilex FPGAs can offer, and easily and affordably access Altera hardware with FPGA development kits.

Kits are available for a wide range of application cases and all geographical locations. To find out how to buy, go to Altera’s Partner Showcase website.

Read more on govindhtech.com

[elsa16744]

10 Ways Artificial Intelligence is Transforming the Market Research Industry

Artificial intelligence (AI) is revolutionizing various industries, and the market research solutions sector is no exception. By harnessing the power of AI, companies can gain deeper insights into consumer behavior, optimize their strategies, and make data-driven decisions with greater precision. In this article, we’ll explore 10 transformative ways AI is reshaping the market research industry and how Gen AI development solutions are at the forefront of this evolution.

Understanding What Market Research Necessitates

Market research is essential for businesses seeking to understand consumer needs, preferences, and market dynamics. It involves collecting and analyzing data from various sources to inform strategic decisions. Effective market research solutions help brands grow by uncovering valuable insights into consumer behavior, market trends, and competitive positioning.

10 Ways Artificial Intelligence is Transforming the Market Research Industry

1. Context-Aware Virtual Chatbots

The use of context-aware virtual chatbots has become increasingly popular in market research. These advanced AI-driven tools engage customers in meaningful conversations, gather qualitative feedback, and provide insights into consumer preferences. By automating interactions and analyzing responses, businesses can enhance their market research efforts and increase survey participation.

2. Real-Time Behavior Monitoring

AI-powered tools enable real-time behavior monitoring, offering valuable insights into consumer interactions and product usage patterns. This real-time data helps businesses identify trends, optimize user experiences, and improve customer satisfaction.

3. Unstructured Data Processing

Processing unstructured data from sources like social media, news articles, and multimedia content is a critical aspect of market research. AI-driven solutions excel at unstructured data processing, turning diverse information into actionable insights for market research strategies.

4. Emotion-Driven Profiling

AI technologies enable emotion-driven profiling by analyzing sentiment and emotional responses from customer feedback. This approach helps businesses understand emotional drivers behind consumer behavior and refine their marketing strategies.

5. Predictive Analytics

With predictive analytics, AI helps market researchers forecast future trends, customer behaviors, and market shifts. These insights empower businesses to make informed decisions and stay ahead of the competition.

6. Customer Segmentation

AI enhances customer segmentation by analyzing data to group consumers based on shared characteristics. This segmentation enables businesses to create targeted marketing campaigns and improve customer engagement.

7. Cost-Time Analysis and Optimization

AI solutions streamline market research processes, reducing both cost and time. Through automation and advanced analytics, businesses can conduct comprehensive market studies efficiently, making cost-time optimization accessible to companies of all sizes.

8. Extensive Automation

AI facilitates extensive automation in market research tasks, allowing for continuous data collection and analysis. This automation frees up resources and enables employees to focus on strategic decision-making.

9. Product Performance Simulation

AI-driven product performance simulations allow businesses to test and refine product concepts in virtual environments. These simulations provide valuable insights into product durability, user experience, and potential improvements.

10. Risk Monitoring and Resolution

AI-powered risk monitoring tools help businesses identify potential issues in marketing strategies and product developments. By analyzing data for risk monitoring, businesses can implement proactive measures and ensure resilience against market fluctuations.

Conclusion

AI is a game-changer for the market research industry, offering innovative solutions for data analysis, customer engagement, and strategic planning. By exploring advanced market research solutions and leveraging Gen AI development solutions, businesses can unlock new opportunities and stay competitive in a rapidly evolving market landscape. Embracing these technologies will be crucial for market research professionals looking to drive success in the future.

[ideaboxcreations]

7 Hidden Google Ads Features Every Marketer Must Know in 2025

Introduction

Google Ads continues to evolve with powerful yet underused features that can significantly boost campaign performance in 2025. Beyond the popular tools, these seven hidden gems offer strategic advantages—from AI-driven placements to smarter measurement and creative automation. Read on to sharpen your digital strategy and stay ahead of the competition.

1. Ads in AI Overviews & AI Mode

Google is now surfacing ads within AI Overviews and its new AI Mode tab. These placements appear in conversational summaries and chatbot-style replies.

Why it matters: Queries in AI Mode tend to be longer and more exploratory—ideal for brand discovery.

How to prepare: Ensure your Search, Performance Max, and Shopping campaigns (with broad match) are active, as Google automatically matches eligible ads to AI-driven placements.

Pro Tip: Focus on long-tail keywords to increase relevance for conversational queries.

2. Smart Bidding Exploration

Smart Bidding Exploration uses machine learning to identify untapped, high-value queries and adjust bids accordingly.

How it works: It expands ROAS targets to capture conversions from new and emerging search intent.

Benefits: Helps advertisers stay competitive on evolving search patterns.

Tip: Start conservatively, monitor performance closely, and scale strategically.

3. PMax Enhancements: Negative Keywords & Search-Term Visibility

Performance Max (PMax) now includes advanced controls for better optimization.

Campaign-level negative keywords: Exclude irrelevant queries at scale to enhance targeting.

Search-term visibility: View which queries triggered PMax ads to better understand user intent.

Action Steps:

Regularly review your search term reports.

Add negative keywords to block low-performing terms.

Refine asset groups for higher engagement.

4. Seasonality Adjustments & Data Exclusion

These advanced tools improve Smart Bidding and measurement accuracy.

Seasonality Adjustments: Fine-tune bids during known sales events or promotions to align with expected traffic spikes.

Data Exclusion: Exclude time periods with tracking issues or website errors to maintain algorithmic integrity.

Use Case Examples:

Schedule bid boosts for holiday sales.

Exclude days with broken site tracking to avoid misleading data.

5. Incrementality Testing Made Accessible

Incrementality testing is no longer limited to large advertisers.

Update: Minimum spend thresholds have dropped significantly, making lift studies more accessible.

Why It Matters: Provides a clearer picture of real campaign impact beyond last-click attribution.

Best Practices: Use split testing to measure lift and adjust strategy based on true conversion influence.

6. Creative Studio & Asset Tools (Image-to-Video, QR/Barcode Promos)

Google’s Creative Studio has introduced new features that enhance visual and interactive advertising.

Image-to-Video and AI Assets: Quickly turn static images into engaging video creatives with AI tools.

QR and Barcode Integration: Add scannable codes to promotions, connecting offline and online experiences.

Tips for Use:

Create multiple asset versions for different placements (vertical, square, landscape).

Run localized QR code promotions for tracking in-store engagement.

7. Demand Gen & Video Integration

Demand Gen campaigns have redefined YouTube and Discover advertising.

What’s New: Shoppable formats on Connected TV (CTV) and mobile let users engage directly with ads.

Creative Boosts: Enhanced support for vertical Shorts, AI video extensions, and seamless influencer collaborations.

Optimization Advice:

Upload vertical videos to reach Discover and Shorts users.

Partner with YouTube creators for credible, authentic reach.

Conclusion

These seven hidden Google Ads features—AI Overviews, Smart Bidding Exploration, enhanced PMax tools, smarter measurement, affordable incrementality tests, AI-powered creative tools, and Demand Gen campaigns—are reshaping how marketers drive performance in 2025. Now is the time to audit your campaigns, activate these tools, and test strategically to outperform competitors.

FAQs

1. What is Smart Bidding Exploration? A tool that identifies and bids on emerging queries using flexible ROAS strategies.

2. How are seasonality adjustments different from data exclusion? Seasonality tweaks future bidding; data exclusion removes flawed past data.

3. Can small brands use incrementality testing? Yes—lower budget thresholds now make it accessible to smaller advertisers.

4. How do I add QR codes to promotions? Use QR generators and upload them through Google Ads promotion assets.

5. Are AI Mode ads globally available? Currently rolling out in stages, with broader access expected during the year.

Looking for a trusted Google Ads agency? We’re here to drive real results for your business.

[simpatel]

Why UK Retailers Monitor Meesho Ratings for Trend Analysis

Why UK Retailers Monitor Meesho Ratings for Trend Analysis

Introduction

What Do UK Retailers Have to Do with Meesho?

At first glance, Meesho—a mobile-first eCommerce giant in India—might seem distant from the UK retail scene. But as global markets become hyperconnected, UK retailers are turning to Meesho’s product ratings and reviews to forecast emerging trends, test microproduct concepts, and anticipate consumer sentiment shifts in real time.

At Datazivot, we help UK brands tap into Meesho’s treasure trove of reviews and product ratings through automated scraping and AI-driven analysis. This gives retail and fashion analysts a first-mover advantage—especially in fast-moving categories like fashion, jewelry, home goods, and beauty.

Why Meesho Reviews Matter for UK Retailers

Meesho has over 140 million active users in India, primarily from Tier 2 and Tier 3 cities. This user base is experimental, price-conscious, trend-driven—and incredibly vocal in feedback.

Key reasons UK brands are paying attention:

Detect emerging fashion trends before they hit the West

Understand low-cost product performance under scale

See what types of SKUs resonate with Gen Z & women buyers

Analyze real feedback across thousands of products in minutes

What Datazivot Extracts from Meesho Ratings & Reviews

Sample Data Extracted from Meesho by Datazivot

Real-World Application

UK Retailer Uses Meesho Insights to Launch Collection :

Retailer: StyleLab London

Goal: Understand what budget-conscious fashion is trending in South Asia

Process:

100,000+ Web Scraping Meesho Reviews for ethnic wear, kurtis, and dupattas

Filtered products with 4.5 star+ and over 1,000 reviews

Identified trends in color (mustard, bottle green), fabric (rayon, cotton), and neck design

Result: StyleLab launched an Indo-fusion collection tailored for the UK’s South Asian diaspora. The line sold out within 6 weeks.

Top 5 Categories UK Retailers Monitor on Meesho

These categories show rapid rotation of trends and garner thousands of daily reviews.

AI-Based Trend Detection from Meesho Reviews

Using Datazivot’s AI model, UK retailers receive:

Trend Heatmaps: Which colors/styles are gaining momentum

Complaint Clusters: What recurring issues (fit, material) are hurting sales

Sentiment Trajectory: Are reviews improving or declining over time?

Material Mentions: Cotton, polyester, viscose frequency tracking

Color Trends: “Maroon” reviews up 18% MoM in ethnic wear (May 2025)

How Meesho’s Mass Market Feedback Helps UK Pricing & Sourcing

A/B Test Concepts: See which styles resonate with Indian women before investing in manufacturing

Price Elasticity Signals: Understand value-to-feedback ratio on SKUs priced under ₹500

Sourcing Leads: Identify consistent sellers with high ratings and low return complaints

Case Study: Beauty Brand Forecasts Product Success

Client: Glow & Go UK

Use Case: Track reviews on natural skincare products on Meesho to plan UK product drops

Finding:

Products with turmeric, sandalwood, and rose water received higher sentiment scores and positive skin-effect feedback

Negative reviews often mentioned strong chemical smell or fake ingredients

Outcome: Glow & Go tailored its 2025 “Ayurvedic Glow Kit” based on insights from 20,000+ Meesho reviews. They launched in UK salons and online with a 92% sell-through rate in Q1.

Benefits for UK Retailers Monitoring Meesho

How Datazivot Enables Meesho Review Scraping for UK Brands

What’s Next? Merging Meesho Reviews with TikTok & Instagram Trends

UK marketers are combining:

Meesho review analysis

TikTok hashtag trend reports

Instagram story mentions

This 360° view allows brands to validate products across geographies before launching.

Conclusion

Global Retail Begins with Local Listening :

The future of fashion and lifestyle retail is global—but the insights begin locally. Meesho, with its grassroots user base and real-time product reviews, has become a trend incubator for savvy UK retailers.

By partnering with Datazivot, you gain the ability to:

Forecast product success

Align SKUs with cross-border tastes

Launch faster with confidence

Win over niche communities with personalized offerings

Ready to Explore India’s Retail Pulse?

Get in touch with Datazivot to receive a free trend report on top-rated Meesho products across fashion, home, and beauty. Use real reviews to fuel global retail success.

Originally Published at https://www.datazivot.com/uk-retailers-monitor-meesho-ratings-trend-analysis.php

[robotico-digital]

Next-Gen Security Testing Services Using AI: A Deep Dive

In the ever-evolving landscape of software development, security breaches have grown more frequent and sophisticated. Traditional testing methods, though foundational, are no longer sufficient in identifying and addressing the fast-moving threats facing modern systems. This is where Next-Gen Security Testing Services come into play, blending AI innovation with robust testing protocols.

At Robotico Digital, we’ve redefined how security integrates into software engineering by embedding Artificial Intelligence (AI) into our advanced Security Testing Services. This deep dive explores how AI transforms Security Testing in Software Testing, enabling faster detection, smarter remediation, and continuous protection across development pipelines.

The Shift Toward AI in Security Testing

Historically, Security Testing Services were heavily reliant on manual reviews, rule-based scanners, and time-intensive penetration testing. While still valuable, these methods struggle to keep up with:

lRapid DevOps cycles

lEvolving attack vectors

lIncreasing application complexity

lHybrid cloud and microservices infrastructure

AI, specifically through machine learning (ML), Natural Language Processing (NLP), and behavioral analytics, has introduced a transformative layer of intelligence to these services. It allows security testers and developers to go beyond reactive defenses—identifying risks proactively and at scale.

How AI Enhances Security Testing in Software Testing

Incorporating AI into Security Testing in Software Testing provides multi-dimensional improvements across efficiency, accuracy, and adaptability. Let’s break down the core components.

1. Automated Vulnerability Detection

AI-powered scanners can crawl source code, binary files, API endpoints, and web interfaces to detect anomalies that indicate vulnerabilities. Unlike traditional scanners, AI engines learn from past vulnerabilities and global threat databases to continually improve detection precision.

Key Features:

lPattern recognition across massive codebases

lZero-day threat detection using anomaly detection models

lAuto-mapping of application attack surfaces

2. Adaptive Risk Prioritization

One major challenge in Security Testing Services is managing false positives and prioritizing true threats. AI models rank vulnerabilities based on:

lExploitability

lBusiness impact

lData sensitivity

lThreat intelligence feeds

This reduces alert fatigue and ensures engineering teams focus on high-priority issues first.

3. Dynamic Threat Modeling

AI systems can automatically generate and update threat models for evolving software architectures. By simulating attacker behavior, AI enables predictive testing—discovering how vulnerabilities might be chained or escalated.

4. Self-Learning Penetration Testing

AI agents mimic ethical hackers using reinforcement learning. These bots evolve through trial and error, discovering unconventional paths to exploitation and mimicking real-world attack tactics.

Robotico Digital’s AI-Powered Security Testing Stack

At Robotico Digital, we’ve built a proprietary AI-enhanced testing framework designed to deliver intelligent, continuous, and scalable security coverage. Here's what powers our next-gen Security Testing Services:

AI-Powered SAST & DAST Engines

SAST (Static Application Security Testing): Our AI models review code for insecure functions, misconfigurations, and data flow leaks at the source level.

DAST (Dynamic Application Security Testing): AI crawlers test running applications by simulating user behavior and injecting payloads to trigger security vulnerabilities.

Machine-Learning Vulnerability Correlation

We reduce redundant findings by merging results from multiple tools and identifying duplicate alerts. ML models group similar issues, track them across builds, and learn from developer remediation behavior.

AI-Based Compliance Validation

Robotico Digital uses AI to ensure compliance with:

lOWASP Top 10

lGDPR / HIPAA / PCI DSS

lNIST and ISO 27001 We map discovered vulnerabilities to these frameworks, highlighting gaps in your security and compliance posture.

Use Cases of AI in Security Testing Services

Web & Mobile Application Testing

AI identifies issues such as insecure authentication, broken access controls, and injection attacks. It tests logic errors and parameter tampering based on how real users interact with the app.

API Security Testing

APIs are high-value targets. Our AI models analyze OpenAPI/Swagger specs, apply fuzzing techniques, and test for broken object-level authorization (BOLA) and mass assignment vulnerabilities.

Cloud & Infrastructure Testing

For cloud-native applications, AI detects misconfigurations in IAM roles, storage permissions, and network security groups—especially in multi-cloud environments like AWS, Azure, and GCP.

DevSecOps Pipeline Integration

Robotico Digital integrates AI-based scanning tools directly into CI/CD platforms like GitLab, Jenkins, and Azure DevOps. This ensures shift-left security with automated gates at every build stage.

Implementation Challenges & Considerations

While the benefits are substantial, integrating AI into Security Testing Services is not without hurdles:

1. Data Quality & Training Bias

AI models require high-quality, labeled data to function accurately. Poor or biased training datasets can lead to both false positives and false negatives.

2. Explainability & Developer Trust

“Black-box” decisions from AI can frustrate developers. Robotico Digital addresses this by using explainable AI (XAI) models that provide root cause analysis and remediation context.

3. AI Model Drift

Security threats evolve. AI models must be updated regularly to avoid “drift” that could miss emerging threats. We maintain continuous model updates with feedback loops from threat intelligence systems.

Future of AI in Security Testing Services

AI in Security Testing Services is still in its growth phase. Here’s what’s on the horizon:

lGenerative AI for Test Case Creation: Using models like GPT-4 to simulate attacks and generate intelligent test scripts.

lAutonomous Remediation Agents: AI that not only finds issues but can propose or apply secure code fixes autonomously.

lFederated Threat Learning: Secure sharing of anonymized threat data across organizations to train more resilient models.

lAI-Powered Red Teaming: Simulated human-like attackers that learn and evolve to breach complex systems.

Conclusion

Security is not a checkbox; it’s a continuous journey. As applications grow more complex and the cyber threat landscape expands, relying on traditional methods alone is no longer enough. AI enables Security Testing Services to become proactive, intelligent, and deeply integrated into the development lifecycle.

At Robotico Digital, we’re proud to lead the next generation of Security Testing in Software Testing by harnessing the power of artificial intelligence. Our AI-enhanced services empower organizations to detect vulnerabilities faster, respond more intelligently, and scale their cybersecurity operations with confidence.

[vtusoft]

What is AI Automation Testing and How does it Redefine QA

Introduction: Why QA Needs AI Automation Now More Than Ever

As digital transformation accelerates, the need for faster and more reliable software delivery has become vital. Businesses across industries are pushing for agile practices, continuous integration, and faster release cycles. However, traditional QA processes often fail to keep pace, especially when applications evolve frequently. Manual testing is time-consuming and resource-heavy, while even conventional automation lacks adaptability. AI automation testing is emerging as the new standard in quality assurance, where intelligent systems streamline testing tasks with higher accuracy and lesser human intervention.

So, what is AI automation testing in real-world terms? It refers to the use of artificial intelligence, especially machine learning algorithms, to design, execute, and manage software tests. Instead of static, hard-coded scripts, AI automation tools learn from application changes, user interactions, and past test results. They dynamically adapt test cases, uncover hidden bugs, and even prioritize areas that need urgent attention. This intelligent layer ensures that quality becomes an integrated part of development rather than a bottleneck. With the rising complexity of systems and increased user expectations, AI-based testing is quickly becoming essential. Companies that embrace AI-driven QA processes report faster deployments, improved software performance, and significantly reduced regression defects.

How Generative AI Is Modernizing Test Script Development

Traditional automation depends heavily on scripted test cases, which must be updated every time the application changes. This quickly becomes unmanageable in agile environments. Generative AI in test automation offers a revolutionary solution to this challenge. By learning from test history, code repositories, and user flows, it automatically generates test scripts that are more resilient and contextual. This eliminates the need for frequent manual updates and makes automated testing far more reliable and scalable.

Generative AI in Test automation reduces the script maintenance burden by understanding the underlying logic of the application. It does not rely solely on DOM elements or static page objects but focuses on the intent behind the interaction. For example, if a login form changes its layout, traditional automation might fail, but a generative AI model can still test the form by understanding that user authentication is the core task. This capability is especially beneficial in CI/CD pipelines where rapid deployment is key.

Moreover, these AI-powered tools also provide visual and functional test coverage reports, flag potential risks, and simulate user behaviours across different platforms. Teams can focus on building better software while AI takes care of repetitive, error-prone testing tasks. This automation of test creation saves up to 50 percent of QA effort while increasing confidence in release quality.

Enhancing QA Productivity with Generative AI in Software Testing

The ever-growing demand for robust, high-quality software puts immense pressure on QA teams. Often, testers work under strict timelines, trying to identify defects across multiple platforms and configurations. This leads to missed bugs and delayed feedback loops. With the introduction of Generative AI in Software Testing, testing becomes more efficient, thorough, and intelligent. AI identifies weak spots in the application, predicts potential failures, and even recommends corrective actions before issues escalate into production-level defects.

AI-driven QA doesn’t just stop at automated scripts. It actively learns from previous test failures, production incidents, and user behaviour to improve the test strategy. For instance, if a certain feature has historically caused bugs after every update, AI tools can automatically create targeted regression tests around that feature. This reduces blind spots and ensures better code coverage. These tools also enable shift-left testing, empowering developers to catch issues early in the development cycle.

By enhancing exploratory testing through intelligent suggestions and prioritizations, QA teams can now uncover edge-case bugs that typically evade manual checks. Companies implementing generative AI in software testing report over 30 percent faster defect identification and 25 percent fewer escaped bugs. As a result, development and QA teams become more collaborative, reducing friction and increasing overall delivery speed.

Strategic Advantages of Test Automation Using Generative AI

Organizations that adopt test automation using Generative AI gain a clear advantage in terms of time, cost, and reliability. Unlike traditional frameworks, where writing and debugging scripts can take weeks, generative AI models can develop and update test cases within minutes. These models are trained on real application behaviour, ensuring that they can simulate human-like interactions with a high degree of accuracy. Additionally, generative AI facilitates self-healing automation. If a UI element changes, the AI adapts rather than failing the test.

Another strategic benefit lies in test case prioritization. Generative AI models use change impact analysis to identify the most crucial test cases for each build, reducing the need to execute the entire suite. This results in faster feedback cycles and ensures critical issues are caught first. AI-powered analytics also provide insights into the quality of releases, highlighting trends, recurring bugs, and areas of concern.

Industries like banking, healthcare, and retail are already reaping the benefits of this shift. For example, healthcare companies have reported a 40 percent increase in test coverage and 35 percent reduction in compliance violations after adopting generative AI-based QA tools. Furthermore, these solutions often integrate seamlessly with DevOps pipelines, enhancing collaboration between developers, testers, and release managers. In summary, organizations that transition to generative AI for test automation not only improve efficiency but also gain a competitive edge in software delivery.

Implementing AI in Testing: Practical Guidance

Many QA leaders ask how to practically implement AI into their existing frameworks. The best starting point is to identify the repetitive, time-consuming areas of testing where AI can provide immediate relief. For instance, UI regression tests, form validations, and API contract checks are excellent candidates for automation through AI. Understanding how to use Gen AI in test automation involves selecting the right tools that integrate with your current tech stack while offering predictive analytics, self-healing tests, and intelligent defect tracking.

Before deployment, teams should establish goals and KPIs to measure AI’s effectiveness, such as reduced test cycle time, improved defect detection, and higher automation coverage. Next, historical data such as past defects, user behaviour logs, and release notes should be fed into the AI models. These datasets allow the algorithms to learn patterns and recommend optimized testing strategies.

Additionally, training testers to work alongside AI is crucial. AI should augment human judgment, not replace it. Testers can focus on complex exploratory testing, business logic validation, and usability assessments, while AI handles volume and repeatability. Teams should also run pilot projects to evaluate performance before full-scale rollout. Over time, the system self-learns and enhances accuracy, significantly improving the quality and predictability of every release. With the right strategy, generative AI can transform a reactive QA process into a proactive quality assurance function.

Company Comparison and Global Trends

As AI testing technologies continue to evolve, several leading companies are investing heavily in generative AI for quality assurance. Google has integrated AI into Android testing, allowing them to reduce user crashes by more than 25 percent through predictive failure detection. Microsoft, on the other hand, has introduced AI-powered tools within Azure DevOps to support smarter test prioritization and intelligent automation. Infosys has launched a suite of cognitive automation solutions that streamline end-to-end testing across enterprise applications.

V2Soft, a global IT services provider, stands out with its newly launched platform, SANCITI AI. This solution combines generative AI with advanced data analytics to offer predictive defect identification and autonomous testing. With over 92 percent accuracy in test outcome predictions and the ability to reduce testing time by 38 percent, V2Soft’s innovation is setting benchmarks in the QA industry. Its seamless integration with CI/CD pipelines ensures test coverage does not lag behind development velocity.

Global adoption is steadily increasing. According to a 2025 Forrester survey, 61 percent of enterprises are planning to invest in generative AI testing within the next 18 months. The demand for intelligent automation tools is growing across sectors like finance, telecom, and e-commerce. This shift is not only improving QA performance but also transforming how companies approach software engineering at large.

India vs. the US: Market Statistics and Forecast

India and the US are the two most active regions in the development and application of AI-based testing, but their approaches differ significantly. While the US leads in R&D investment and innovation, India is catching up quickly due to its vast engineering talent pool and lower operational costs. In 2024, Indian firms contributed to 34 percent of global AI QA projects, and that number is expected to rise to 43 percent by 2026.

Indian companies like Infosys, TCS, and V2Soft are providing AI-powered QA services not just domestically, but to clients around the world. These companies offer comprehensive test automation frameworks using generative AI that are cost-effective and adaptable. India's ability to scale testing operations quickly makes it an attractive hub for global enterprises. Furthermore, Indian QA professionals are gaining expertise in areas like data labelling, model training, and AI test orchestration.

In contrast, US-based firms focus on product innovation and tooling platforms. Their solutions are often integrated with proprietary DevOps ecosystems, offering deep functionality but at higher costs. India’s strength lies in its agility and affordability, which is crucial for mid-market and emerging startups. With government initiatives supporting AI research and startups, India’s footprint in the global QA landscape is set to expand significantly in the coming years.

Conclusion: The Future Is Self-Adaptive, AI-Driven QA

The shift to AI automation testing is not just a technological change; it is a strategic transformation that enhances software quality, accelerates delivery, and reduces operational overhead. Traditional automation, while still useful, cannot cope with the speed and complexity of modern software. Generative AI makes QA smarter by identifying risks early, self-healing broken scripts, and continuously improving test coverage based on live data.

Organizations that embrace this transformation now will be better prepared for future challenges. With solutions like SANCITI AI from V2Soft and growing support for AI testing across tools and platforms, the path toward intelligent QA is clearer than ever. As software becomes more complex, the tools to test it must become more intelligent, adaptive, and predictive. AI automation testing is the future, and the future is already here.

Have Questions? Ask Us Directly! Want to explore more and transform your business with generative AI in testing?

Send your queries to: vtusoftbglr@gmail.com / info@v2soft.com

[techit-rp]

AI in Wealth Management: How Robo-Advisors Are Changing the Investment Landscape

The world of wealth management is undergoing a radical shift. What once relied heavily on face-to-face consultations and manual portfolio balancing is now being transformed by artificial intelligence (AI). Enter the age of robo-advisors—AI-powered digital platforms that are redefining how individuals invest, save, and manage their finances.

In 2025, robo-advisory services are not only mainstream—they’re preferred by a growing number of investors. These AI-driven systems are democratizing financial advice, offering personalized strategies at a fraction of the cost charged by traditional wealth managers. As this transformation takes hold, there’s a parallel rise in demand for professionals skilled in finance, analytics, and technology. That’s why many aspiring finance professionals are enrolling in the best Financial Modelling Course in Delhi to prepare for the future of smart finance.

What Are Robo-Advisors?

Robo-advisors are automated platforms that use algorithms and machine learning to create and manage investment portfolios. They typically ask users a series of questions—about income, risk tolerance, investment goals—and then use that data to build a customized portfolio.

Key features of robo-advisors include:

Low fees and minimal investment thresholds.

Automatic rebalancing and tax-loss harvesting.

Real-time adjustments based on market data and user preferences.

24/7 access and transparency via mobile or web platforms.

Popular platforms in India include Zerodha’s Nudge, Groww’s Smart Portfolios, and Scripbox, while international names like Betterment, Wealthfront, and SoFi continue to lead in the U.S. and Europe.

Why Robo-Advisors Are Gaining Popularity

Several factors are driving the rapid adoption of robo-advisors:

1. Affordability

Traditional wealth management is expensive and often inaccessible to the average investor. Robo-advisors lower the barrier to entry with minimal fees and starting investments.

2. Convenience

With intuitive mobile apps and real-time access to investment insights, users can manage their portfolios anytime, anywhere.

3. Data-Driven Decisions

AI analyzes market trends, economic indicators, and individual behavior to offer smart, emotion-free investment strategies.

4. Personalization at Scale

Robo-advisors can customize portfolios for thousands of clients simultaneously, offering tailored strategies without manual intervention.

5. Younger Investors Trust Tech

Millennials and Gen Z investors are digital natives. They’re more comfortable relying on algorithms than visiting financial advisors.

The Impact on Traditional Wealth Management

The rise of robo-advisors doesn’t mean human advisors are obsolete—but their role is shifting. Many firms are now adopting hybrid models, combining AI automation with human oversight for more complex financial planning.

This change creates a demand for professionals who understand both the financial fundamentals and the technical side of AI-driven tools. That’s where financial modelling plays a crucial role.

The Role of Financial Modelling in the Age of AI

AI systems need clean, structured data and accurate financial models to make smart recommendations. Behind every robo-advisor is a series of financial models forecasting returns, evaluating risk, optimizing portfolios, and simulating various market scenarios.

Professionals trained in financial modelling are critical for:

Designing and testing investment algorithms.

Building simulation models to predict returns and volatility.

Analyzing client data to generate actionable insights.

Working with product teams to launch AI-driven financial tools.

If you want to become part of this new era in finance, enrolling in the best Financial Modelling Course in Delhi can give you a head start. These programs teach you how to build real-world models using Excel, Python, and financial theory—skills that are in high demand across fintech and investment firms.

Career Opportunities in AI-Driven Wealth Management

As the industry evolves, so do career roles. Emerging job titles include:

Quantitative Analyst (Fintech)

Robo-Advisory Product Manager

Investment Technology Analyst

AI Financial Model Developer

Client Experience Strategist (Digital Wealth Platforms)

Companies hiring for these roles look for a blend of finance, coding, and data literacy—skills that can be acquired and sharpened through the best Financial Modelling Course in Delhi.

Challenges and Ethical Considerations

While robo-advisors offer efficiency and scalability, they also raise important questions:

Can AI understand complex life goals and emotions behind financial decisions?

Are robo-advisors transparent enough in how they allocate and manage funds?

What happens when markets behave irrationally or unpredictably?

The future of wealth management will likely include a strong ethical framework, combining smart automation with human judgement and accountability.

Final Thoughts

AI-powered robo-advisors are more than a trend—they’re reshaping the foundation of wealth management. As more investors embrace automation for its convenience and personalization, the financial world is being redefined.

For aspiring professionals, this is a golden opportunity to prepare for the future. By mastering core financial concepts and learning to build models that power intelligent systems, you can play an active role in the evolution of fintech.

[ioweb3tech]

Why Your Business Needs a Web3 Development Company in 2025

The internet is evolving at lightning speed—and businesses that adapt to change are the ones that thrive. Welcome to the world of Web3: the next generation of the internet built on decentralization, user empowerment, and blockchain technology. At the heart of this revolution lies the power of working with a forward-thinking web3 development company.

From transforming financial systems to creating secure digital identities, Web3 is poised to reshape industries. But this transformation isn’t just about adopting new tools—it’s about rethinking how applications are built, used, and trusted. That’s where expert development partners come in.

What Is Web3 and Why Does It Matter?

Web3 refers to the third generation of the internet, focused on decentralization and built using technologies like blockchain, smart contracts, and peer-to-peer networks. Unlike Web2, where data is controlled by centralized entities, Web3 returns control to users.

Key principles of Web3 include:

Decentralization

Trustless and permissionless architecture

Token-based economics

User ownership of data and digital assets

Whether you’re building decentralized apps (dApps), NFTs, DAOs, or blockchain-based SaaS platforms, a specialized web3 development company ensures your product is aligned with the latest technological standards and future-ready.

The Role of a Web3 Development Company

Web3 projects require deep expertise in multiple domains, from blockchain infrastructure to smart contract security. Here's how a top-tier development company contributes to your success:

1. Smart Contract Development

Smart contracts are self-executing programs that run on the blockchain. A web3-focused team writes, audits, and deploys secure smart contracts to support functions like payments, governance, or authentication.

2. dApp Design and Development

Building a dApp involves more than coding. It requires thoughtful design, seamless UX, and efficient integration with the blockchain. From wallets to DeFi protocols, experienced developers make it user-friendly and scalable.

3. Tokenomics and Blockchain Strategy

Launching a token-based product? A good partner will help design the economics, utility, and structure behind your tokens, ensuring long-term sustainability and user engagement.

4. Security and Audits

Web3 apps are frequently targeted by cyber threats. Security audits, penetration testing, and vulnerability assessments are essential services offered by a qualified development partner.

5. Cross-Chain and Interoperability Solutions

With so many blockchains in the ecosystem—Ethereum, Solana, Polygon, and more—your app needs to operate across networks. Web3 experts implement cross-chain bridges and APIs for seamless communication.

SaaS Meets Web3: A Powerful Combo

One of the most exciting trends today is the fusion of SaaS with Web3. Imagine subscription software that gives users actual ownership or software tools that reward usage through tokens.

This is where saas experts and blockchain engineers join forces to:

Build decentralized SaaS platforms

Integrate smart contract-based subscriptions

Enable tokenized access and incentives

Web3 adds a new layer of transparency and empowerment to the traditional SaaS model.

AI and Web3: Intelligent, Decentralized Innovation

The combination of AI product development with blockchain opens doors to next-gen digital experiences. From decentralized data marketplaces to intelligent decision-making on-chain, AI and Web3 are forming the backbone of futuristic systems.

Leading web3 development companies are exploring this intersection to:

Automate decisions through on-chain AI models

Protect data integrity using blockchain

Enable decentralized autonomous systems (DAOs) that learn and evolve

This hybrid model creates systems that are not just decentralized—but also smart and adaptive.

Hiring the Right Web3 Talent

Web3 is still a specialized field. If you’re aiming to build a secure, scalable, and meaningful product, it’s essential to hire developers who are trained specifically in Web3 technologies.

Qualities to look for:

Proficiency in Solidity, Rust, or Go

Understanding of blockchain architecture and consensus mechanisms

Hands-on experience with layer 1 and layer 2 chains

Experience in DeFi, NFT, or DAO platforms

Instead of hiring individuals, many companies choose to partner with a trusted web3 development company for faster delivery and strategic alignment.

Benefits of Choosing a Full-Service Web3 Partner

Collaborating with a full-service development company allows you to:

Get expert guidance from ideation to launch

Access ready-built modules and APIs

Ensure compliance, security, and scalability

Focus on business strategy while technical heavy lifting is handled for you

It’s more efficient, cost-effective, and secure.

Final Thoughts: Building the Future Starts Now

The world of Web3 is no longer just a concept—it’s a tangible shift in how we build, scale, and experience digital platforms. Whether you're a startup creating a DAO or an enterprise integrating blockchain into your workflow, success lies in execution.

And execution starts with partnering with the right people.

A trusted web3 development company offers you the technological muscle, strategic insight, and future-ready architecture you need to bring your vision to life—securely, efficiently, and at scale.

If you're ready to lead in the decentralized digital economy, now’s the time to act.

[ethanparker9692]

Boost Your Career with Generative AI for Software Development Professional Certificate

In the age of automation, Generative AI for Software Development is transforming how applications are created and maintained. The GSDC Certified Generative AI in Software Development program offers an industry-leading generative ai for software development skill certificate, preparing professionals to integrate AI into their workflows.

📘 What is the Generative AI for Software Development Professional Certificate? This globally recognized certification trains professionals in AI-powered software creation. The course covers advanced topics like AI-assisted coding, AI-driven testing, automated debugging, and architecture optimization. Whether you're an AI enthusiast or an experienced developer, this generative ai for software development course will elevate your skill set.

🎯 Who Should Enroll?

Software Developers & Engineers

AI & Data Science Professionals

Project Managers & Team Leads

IT Experts seeking to innovate with AI

💡 Key Learning Areas:

Gen AI software development tools and frameworks

Real-world use cases of generative ai for software development

AI-powered CI/CD pipelines

Leveraging AI for scalability and performance

✅ Why Get Certified?

Validate your expertise with a generative ai for software development skill certificate

Gain a competitive edge in AI-driven markets

Join a global community of generative ai for software development professional certificate holders

Accelerate your career in tech innovation

Ready to future-proof your skills? Explore the generative ai for software development course at GSDC and become a leader in next-gen development.

🔗 Visit: https://www.gsdcouncil.org/certified-generative-ai-in-software-development

#GenerativeAI #GenAI #SoftwareDevelopment #AICoding #GenerativeAISoftwareDevelopment #GenerativeAIForSoftwareDevelopment #ProfessionalCertificate #SkillCertificate #GSDCCertification #AIEngineer #SoftwareEngineer

[ithealthbyimbibetech]

In a digital era where speed, accuracy, and automation are crucial, laboratories across India are shifting away from manual systems and outdated software. As a result, choosing the best path lab software has become vital for lab owners aiming to scale operations, improve report turnaround time, and ensure regulatory compliance. Among the many available options, ItHealth by Imbibe Tech has emerged as the most trusted and best pathology lab software in India, winning the confidence of over 100+ labs nationwide.

This article explores why lab owners prefer ItHealth and how it is redefining the standards of pathology lab management software in India.

The Problem with Traditional Path Lab Systems

Most small and medium labs in India still rely on Excel sheets, handwritten reports, or basic standalone software. These systems come with multiple challenges:

Time-consuming report generation

Error-prone manual entries

Lack of automated billing

Inability to integrate with diagnostic equipment

Poor data backup and security

No remote access or multi-location support

These issues slow down operations, impact the patient experience, and increase the risk of non-compliance with healthcare standards.

The ItHealth Solution: Redefining Lab Efficiency

ItHealth by Imbibe Tech is a next-gen laboratory information management system designed with the needs of Indian pathology labs in mind. From Tier-1 cities to Tier-3 towns, ItHealth is enabling labs to digitize and automate workflows at an affordable cost.

Here’s why 100+ labs across India trust ItHealth:

1. Complete Lab Automation

From sample collection to report delivery, ItHealth automates every stage. Lab technicians can generate reports in just a few clicks, reducing human errors and speeding up the process.

2. Customizable Reporting Templates

ItHealth offers editable Word-format reports for tests like FNAC, Biopsy, and Fluids. These customizable templates maintain consistency while allowing pathologists to tailor details.

3. Real-Time Data Backup and Security

Data loss is a nightmare for any lab. ItHealth ensures regular, automated backups and cloud-based access, preventing data loss while maintaining security and HIPAA compliance.

4. Multi-Channel Report Delivery

ItHealth supports sending reports via WhatsApp, Email, and SMS directly from the software. This improves patient communication and reduces the load on front-desk staff.

5. AI-Powered Assistance

ItHealth provides 24/7 automated AI support for labs, helping users resolve issues instantly. This reduces downtime and dependency on human technical teams.

6. Flexible Printing Options

Labs can print reports on pre-printed stationery or plain paper with or without headers, offering flexibility based on lab branding and patient requirements.

7. Affordable for Every Lab Size

Unlike many global systems, ItHealth is priced starting at just ₹490/month. It’s cost-effective even for small labs in rural or semi-urban areas, making it one of the best laboratory information management systems in India.

8. Scalable & Multi-Location Ready

Whether you operate a single lab or a chain of diagnostic centers, ItHealth scales with your business. It supports multi-location access, making it ideal for expanding labs.

Why ItHealth is the Best Path Lab Software for Indian Labs

With intense competition in the diagnostic industry, labs must focus on faster service, data security, and operational efficiency. Here’s how ItHealth delivers on those fronts:

Faster Report Turnaround: Reports in minutes, not hours.

Digital Payment Integration: Easily manage billing and payments.

Regulatory Compliance: Stay audit-ready with WHO-aligned documentation.

Device Integration: Seamlessly connects with lab instruments and analyzers.

User-Friendly Interface: Easy to use for lab staff, with minimal training required.

ItHealth is not just a tool-it’s a business enabler that ensures labs run smoothly, scale faster, and deliver top-notch services to patients.

How ItHealth Compares to Other LIMS in India

Many international laboratory information management systems (LIMS) are expensive, complex, and not built for the Indian diagnostic landscape. Here’s how ItHealth stands apart:

FAQs About Path Lab Software in India

1. What is the best path lab software in India?

ItHealth by Imbibe Tech is widely considered one of the best due to its affordability, automation, and ease of use tailored for Indian labs.

2. How does a laboratory information management system help labs?

It streamlines lab operations, improves report accuracy, enables faster turnaround, and ensures better patient service.

3. Is ItHealth suitable for small or rural labs?

Yes. With pricing as low as ₹490/month, ItHealth is perfect for small and rural labs that want to go digital without heavy investment.

4. Can I use ItHealth on mobile or tablet?

Absolutely. ItHealth is compatible across devices including smartphones, tablets, laptops, and desktops.

Final Thoughts

In 2025, staying competitive means embracing digital transformation. Lab owners looking to streamline operations, improve accuracy, and enhance patient satisfaction must invest in a reliable system.

ItHealth by Imbibe Tech is the best path lab software in India, not just for its advanced features but for its commitment to making pathology lab management simple, scalable, and affordable.

If 100+ labs trust ItHealth already, maybe it’s time your lab did too.

Originally published at https://imbibe.in on June 16, 2025.

[anilpal]

AI in Software Testing: Actionable Advice for 2025

As we move deeper into the age of intelligent automation, software testing is undergoing a radical transformation. Artificial Intelligence (AI) is not just a futuristic concept — it’s a powerful enabler in the software development lifecycle today. From test case generation to execution, from bug prediction to risk-based testing, AI has already started redefining how QA teams operate.

In 2025, the fusion of human expertise with AI-powered tools is setting a new standard for speed, quality, and scalability in testing. In this article, we explore how AI is shaping the software testing landscape and provide actionable advice on leveraging its full potential — with a focus on Genqe.ai, a leading AI testing platform.

The State of Software Testing Today

In 2025, software testing remains one of the most vital aspects of product development. The challenge, however, is the increasing complexity of software systems. Applications today are built using microservices, accessed across platforms, and deployed rapidly through CI/CD pipelines. Testers face mounting pressure to deliver faster, more accurate results in shorter cycles.

Manual testing, while still important for exploratory and UX validation, can no longer keep up with speed and coverage demands alone. Traditional automation tools, although powerful, require constant maintenance and scripting knowledge — leading to high overhead and rigidity. The shift towards AI in testing stems from the need to bridge these gaps and make QA more predictive, intelligent, and autonomous.

Enter Genqe.ai, a next-gen testing platform that addresses these needs with generative AI. Genqe empowers QA teams to author test cases in plain English, automate them across environments, and self-heal tests when applications evolve — all without writing a line of code.

The Emergence of AI & Its Impact on Software Testing

AI in testing is no longer optional — it’s a competitive advantage. The emergence of AI has significantly impacted all stages of the testing lifecycle. Machine learning algorithms can now analyze vast amounts of historical data to predict areas of risk, generate efficient test coverage maps, and identify test gaps.

Key impacts of AI on testing include:

Test case generation: AI analyzes requirements, code changes, and user flows to generate meaningful test cases automatically.

Smart test selection: Instead of running the entire suite, AI selects high-impact test cases based on recent code commits or production incidents.

Defect prediction: By analyzing past defect trends, AI helps testers focus on modules most likely to fail.

Self-healing: When UI elements change, AI dynamically updates test scripts to prevent failures.

Genqe.ai incorporates these features to help teams stay ahead. Its AI models continuously learn from test outcomes, evolving test coverage intelligently. This minimizes rework, accelerates test execution, and reduces manual intervention.

AI in Test Management

Test management in 2025 has evolved beyond spreadsheets and siloed tools. Today’s QA leaders need centralized, data-driven systems to plan, track, and report testing activities. AI takes test management to a whole new level by providing actionable insights and automating decision-making.

With AI-powered platforms like Genqe.ai, test managers can:

Automatically prioritize test cases based on risk and user behavior.

Analyze test coverage and suggest areas needing attention.

Receive real-time alerts for flaky or redundant test cases.

Identify blockers in the test process and suggest resolutions.

Genqe transforms test management into a proactive process. It helps stakeholders track project health through intelligent dashboards, identify regression hotspots, and optimize release cycles. Instead of reactive bug fixing, QA becomes a forward-looking discipline driven by data and insight.

AI in Software Test Automation

Test automation is where AI truly shines. Traditional automation requires time-consuming scripting and maintenance. In contrast, AI-driven automation enables dynamic test creation, self-healing, and adaptive execution.

Here’s how Genqe.ai enhances test automation:

Natural Language Testing: Testers describe what needs to be tested in plain English, and Genqe converts it into executable tests.

Self-healing scripts: When UI elements change, Genqe uses AI to identify similar components and fix broken tests automatically.

Visual testing: Genqe employs computer vision to verify UI consistency across devices and platforms.

Parallel execution: AI optimizes test runs by grouping compatible cases and executing them in parallel on device clouds or local environments.

This allows QA teams to scale automation without needing heavy coding skills. AI adapts to application changes and ensures continuous test relevance — making testing not only faster but also smarter.

AI-Human Collaboration: How Does it Look?

The future of testing is not AI vs. human — it’s AI with human. AI handles repetitive, predictable tasks while testers focus on complex reasoning, exploratory testing, and creative problem-solving.

In this collaborative model:

Testers become AI trainers, feeding models with correct test logic and expected outcomes.

QA analysts verify AI-generated cases for business relevance.

Developers and testers work together to teach Genqe.ai how their systems behave.

Human judgment remains essential in UX assessments, ethics reviews, and strategic risk evaluation.

Genqe embodies this collaboration by providing an intuitive interface where testers and developers can guide and validate AI behavior. The result is a symbiotic workflow where humans focus on what they do best — thinking critically — while AI handles execution at speed and scale.

Responsible AI: A Mechanism for Ensuring Software Quality

As AI takes on more responsibility in the testing process, ensuring its ethical and accurate use is critical. Responsible AI in testing means:

Transparency: Knowing how test decisions are made by AI (test selection, coverage analysis).

Bias reduction: Avoiding test models trained only on limited or skewed data.

Explainability: Being able to trace why a test failed or was skipped.

Data privacy: Ensuring user data used for training models is anonymized and secure.

Genqe.ai is designed with responsible AI at its core. It provides audit trails, customizable rules, and user oversight for every AI-driven action. Its models are trained on diverse datasets to avoid systemic bias, and it complies with industry standards for data protection.

By embracing responsible AI practices, teams not only improve their testing processes but also build user trust and product credibility.

Conclusion

AI is not just a tool — it’s a partner in modern software testing. In 2025, organizations that embrace AI-driven QA will be better positioned to innovate, reduce risk, and release faster. From intelligent test management and automated execution to collaborative workflows and responsible practices, AI transforms testing into a predictive, efficient, and strategic function.

Platforms like Genqe.ai make this transformation accessible and practical. By combining natural language processing, machine learning, and visual intelligence, Genqe allows teams to scale automation, improve coverage, and maintain software quality with minimal effort.

Actionable Advice for 2025:

Start with hybrid testing — combine AI with manual validation.

Invest in AI tools like Genqe.ai that support plain-English testing and self-healing.

Foster a collaborative culture where testers and AI learn from each other.

Regularly audit your AI-driven tests for transparency and fairness

In a world where user expectations are rising and development cycles are shrinking, AI is the QA ally every software team needs. And with Genqe.ai, the future of software testing is not just automated — it’s intelligent.

[growwithcertification]

Top Use Cases of Generative AI in Software Development You Need to Know

The software development landscape is evolving faster than ever before, and Generative AI is at the forefront of this transformation. Whether you're a budding developer or an experienced professional, embracing Generative AI for software development skill certificate is no longer optional it’s essential. Let's dive into the most impactful use cases that showcase the true power of Generative AI in software development.

💡 Want to become a certified generative AI in software development expert? Explore the Generative AI Certification by GSDC or reach out at 📞 +41 444 851 189.

✨ Code Generation with Precision

Generative AI tools like OpenAI's Codex are now capable of writing clean, functional code from simple natural language prompts. Developers can speed up project timelines and reduce errors by using Gen AI software development models trained on large codebases.

🔁 Automatic Code Refactoring

Cleaning up legacy code or optimizing algorithms can be time-consuming. Creative AI models can now refactor code, improving its performance and readability while preserving logic making maintenance far more efficient.

🛠️ Intelligent Debugging and Testing

Generative AI in software development excels at identifying bugs, suggesting fixes, and even writing unit tests. This reduces testing time and increases the reliability of software releases.

📋 Documentation Made Easy

Writing documentation is often overlooked. Generative AI software development solutions can generate detailed docstrings, explain code snippets, and create API documentation in real-time saving hours of manual effort.

🎯 Personalized Learning and Skill Enhancement

With a generative AI certification, developers gain not just a credential but exposure to how AI can create personalized learning paths, tutorials, and adaptive coding challenges tailored to individual learning styles.

🔄 Code Translation Across Languages

Whether it’s converting Python to Java or C++ to Rust, Generative AI for software development can seamlessly translate code, making it easier to adapt projects for different ecosystems and platforms.

🌐 Chatbot and Virtual Assistant Development

Using Open AI models, developers can now build conversational interfaces and AI-powered support bots with minimal manual scripting, enhancing user engagement with natural, fluid interactions.

💬 Real-time Code Suggestions

While coding, Gen AI software development tools offer real-time suggestions that not only speed up the process but also improve code quality and adherence to best practices.

🧠 Enhanced Decision-Making for Architects

From architecture design to tech stack selection, certified generative AI in software development professionals use AI-generated insights and simulations to make smarter and faster decisions during the planning phase.

📈 Boosting DevOps with AI Automation

Generative AI can automate DevOps pipelines—from CI/CD workflows to monitoring scripts—enhancing efficiency and reducing operational burden for software teams.

🎓 Ready to lead the change? Get Generative AI in Software Development Certification from a trusted source like GSDC and join the future of intelligent programming. 🔗 Get Certified Now 📞 For inquiries, contact us at +41 444 851 189

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[cal-on]

https://calonev.com/why-cal-on-evs-manufacturing-standards-set-a-new-benchmark-in-e-mobility/

Driving the Future of Green Mobility

India’s electric vehicle (EV) market is undergoing a revolutionary transformation, and at the heart of this shift stands CAL-ON EV, a company committed to reimagining mobility through precision, innovation, and sustainability. In an industry increasingly defined by performance, safety, and eco-consciousness, CAL-ON EV Manufacturing Standards have set an extraordinary benchmark in e-mobility manufacturing excellence.

This blog explores how CAL-ON EV’s quality assurance practices, cutting-edge production facilities, and relentless pursuit of sustainable electric vehicle manufacturing are elevating the brand as a symbol of reliable electric vehicles in India and global competitiveness.

1. The Foundation of Excellence: High-Quality EV Manufacturing in India

When it comes to high-quality EV manufacturing, CAL-ON EV Manufacturing Standards lead the charge by combining advanced automation, smart technologies, and precision engineering in EVs. The company leverages a state-of-the-art EV plant in India, equipped with AI-driven quality checks, robotic assembly lines, and lean production techniques that prioritize zero defects.

This focus on best practices in EV production has enabled CAL-ON to meet and exceed the most stringent EV safety standards in India, ensuring not only world-class performance but also driver and passenger safety.

2. Quality Assurance Beyond Industry Norms

CAL-ON EV quality assurance begins at the design table and continues through every stage of production. From component sourcing to final testing, CAL-ON integrates next-gen EV manufacturing systems that facilitate real-time feedback and automated quality control checkpoints.

Such robust quality control in EV production has helped CAL-ON build trust among its customers and industry partners. The rigorous inspection process—covering electrical safety, battery endurance, structural integrity, and software calibration—ensures that every vehicle meets benchmark EV production in India.

3. Innovation at the Core of CAL-ON EV Manufacturing Standards

Innovation isn’t just a buzzword at CAL-ON—it’s a philosophy. The brand’s R&D teams are constantly testing and implementing cutting-edge EV technology in India, including regenerative braking systems, advanced battery management, and IoT-enabled smart dashboards.

This spirit of CAL-ON electric vehicle innovation keeps the company ahead of the curve in delivering future-ready electric vehicles. It also fuels its mission of green technology in EVs, using lightweight composite materials, recyclable components, and energy-efficient assembly lines.

4. Smart Manufacturing in E-Mobility: An Industry Benchmark

Smart manufacturing in e-mobility is not just about digitizing processes—it’s about creating a connected ecosystem where every component, sensor, and module communicates seamlessly. CAL-ON EV Manufacturing Standards utilize predictive analytics, digital twins, and cloud-connected monitoring systems to anticipate issues before they arise and optimize production in real time.

This integration of technology and talent supports superior EV design and testing, enabling quicker prototyping, reduced production time, and improved reliability of end products.

5. Sustainability Built Into Every EV

The journey toward sustainable electric vehicle manufacturing involves minimizing carbon footprints and maximizing energy efficiency. CAL-ON’s facilities are designed for solar integration, rainwater harvesting, and responsible waste management. Its supply chain is curated to include vendors who align with green technology in EVs and follow ethical sourcing.

From eco-friendly paints to recyclable battery packs, sustainability is an integral part of CAL-ON EV Manufacturing Standards, underscoring their role as leaders in the Indian electric vehicle manufacturing benchmarks space.

6. Setting the Gold Standard for EV Safety in India

In the arena of EV safety standards in India, CAL-ON is a name synonymous with trust. Vehicles undergo over 200 individual safety tests, including frontal and side impact simulations, battery explosion resistance, and water-wading performance.

The company’s approach to best practices in EV production ensures all vehicles meet or exceed Bharat NCAP and AIS 038 safety requirements. Safety features like advanced driver-assistance systems (ADAS), traction control, and fail-safe battery cut-offs highlight their commitment to building reliable electric vehicles in India.

7. Precision Engineering in Every Bolt and Circuit

One of the defining traits of CAL-ON EV Manufacturing Standards is the company’s dedication to precision engineering in EVs. Whether it’s a bolt, wire, or sensor, every component is meticulously tested and positioned to achieve optimum performance and durability.

Such a focus on detail allows CAL-ON to perfect the balance between performance and economy, shaping vehicles that are both exhilarating to drive and cost-efficient to own.

8. The Indian EV Industry Growth and CAL-ON’s Role

The rapid Indian EV industry growth has been fueled by supportive policies, urbanization, and environmental awareness. But it’s brands like CAL-ON that are setting tangible benchmarks in EV manufacturing standards in India.

By aligning their processes with international certifications and constantly upgrading to newer technologies, CAL-ON is not just keeping pace—it is leading. The brand’s strategy is a case study in e-mobility manufacturing excellence, showcasing what’s possible when vision, technology, and talent converge.

9. Future-Ready and Built to Last

Future-ready electric vehicles are more than just EVs with modern tech-they are designed to evolve. With over-the-air (OTA) updates, modular battery architecture, and smart connectivity features, CAL-ON’s vehicles are built with tomorrow in mind.

Such a forward-thinking approach is embedded in CAL-ON EV Manufacturing Standards, ensuring longevity, scalability, and upgradability—traits that will be crucial as India transitions toward full-scale e-mobility.

10. The Benchmark for the EV Ecosystem in India

In a highly competitive market, CAL-ON EV Manufacturing Standards are helping redefine what it means to build an electric vehicle in India. Their model inspires confidence across stakeholders-customers, investors, government bodies, and supply chain partners.

This has led to the emergence of a new standard for benchmark EV production in India, paving the way for an ecosystem that’s not only profitable but also sustainable and socially responsible.

Conclusion: CAL-ON is the Future of EV Manufacturing in India

In conclusion, CAL-ON EV Manufacturing Standards represent more than just technical superiority-they embody a vision for smart manufacturing in e-mobility, rooted in sustainability, safety, and innovation. By consistently adhering to high-quality EV manufacturing, maintaining rigorous EV safety standards in India, and driving CAL-ON EV quality assurance, the company is spearheading a future where green mobility solutions in India are the norm, not the exception.

Whether you are an EV enthusiast, a policymaker, or a business looking to invest in the Indian electric vehicle manufacturing benchmarks, CAL-ON’s journey offers a roadmap to success. Their relentless commitment to next-gen EV manufacturing in India makes them the brand to watch in the electrified era ahead.

Also Read: Made in India, Built for the Future: The Manufacturing Journey of CAL-ON EV

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