#AI/Ml testing

[vijayashree4400]

How AI and ML Are Reshaping Software Testing ?  

Software testing stands as a pivotal phase in the software development lifecycle, guaranteeing that applications fulfill quality standards, functional requirements, and user expectations prior to deployment. Historically reliant on manual efforts, this process has been time-consuming and susceptible to human error. However, with the emergence of Artificial Intelligence (AI) and Machine Learning (ML), software testing practices have undergone a profound transformation, ushering in automation, predictive analytics, and advanced data analysis capabilities. 

The incorporation of AI and ML has transformed defect identification, test case prioritization, and overall testing efficiency. By providing data-driven insights, these technologies enable informed decision-making, optimize testing strategies, and automate repetitive tasks, ensuring accuracy and reliability. 

AI/ML Tools Transforming Software Testing: 

Test Automation Frameworks: These frameworks, such as Selenium with AI integration, Testim, and TestCraft, automate repetitive testing tasks, reducing manual effort and human error while ensuring consistency and accuracy. 

Defect Prediction Tools: Utilizing machine learning algorithms, tools like DeepCode and CodeGuru analyze code repositories to identify potential defects, enabling testing teams to proactively address issues before they impact software quality. 

Test Case Prioritization Tools: AI-driven tools such as Applitools and mabl prioritize test cases based on risk factors, historical data analysis, and code changes, optimizing resource allocation and enhancing testing efficiency. 

Predictive Analytics Platforms: Platforms like Parasoft and Tricentis leverage machine learning algorithms to analyze historical data and predict potential defects, enabling testing teams to focus efforts on critical areas and prevent issues before deployment. 

Intelligent Test Execution Systems: AI/ML-powered test execution systems such as Eggplant and Test.ai dynamically adjust test coverage based on code changes and prioritize tests likely to uncover defects, maximizing testing effectiveness and efficiency. 

These AI/ML tools empower testing teams to streamline their processes, enhance software quality, and adapt to the evolving landscape of software development methodologies. 

AI/ML in Software Testing Practices: 

Automated Test Case Prioritization: AI-powered tools prioritize test cases based on various factors such as risk, frequency of occurrence, and criticality, enabling testing teams to focus on high-priority areas first. 

Predictive Analytics for Defect Prevention: ML algorithms analyze historical data to predict potential defects, allowing testing teams to proactively address issues before they escalate, thus enhancing software quality and reliability. 

Intelligent Test Execution: AI-driven testing frameworks automate test case execution, dynamically adjusting test coverage based on code changes and prioritizing tests that are most likely to uncover defects. 

Importance of AI/ML in Software Testing: 

Agile Adaptability: AI and ML testing processes to adapt to changing requirements and environments, facilitating seamless integration with agile and DevOps methodologies and ensuring continuous delivery of high-quality software. 

Future-Proofing Software Quality: By leveraging AI and ML, testing practices remain resilient to technological advancements and market shifts, enabling software products to maintain their quality and relevance over time. 

Enhanced Customer Satisfaction: Improved software quality resulting from AI-driven testing practices leads to higher customer satisfaction, increased user retention, and positive brand reputation. 

The integration of QA practices with AI/ML is indeed a boon for streamlining the software development cycle. By implementing the AI/ML-driven testing practices, organizations can enhance their SDLC processes and deliver high-quality products to market.  

For further insights into this methodology, reaching out to a professional software testing company like Testrig is highly recommended. Their experienced testers can quickly address any queries and provide efficient solutions to optimize your testing processes.  

[raffaellopalandri]

Statistical Tools

Daily writing promptWhat was the last thing you searched for online? Why were you looking for it?View all responses Checking which has been my most recent search on Google, I found that I asked for papers, published in the last 5 years, that used a Montecarlo method to check the reliability of a mathematical method to calculate a team’s efficacy. Photo by Andrea Piacquadio on Pexels.com I was…

[covalense]

The Role of ML & AI in Testing

[habilelabs]

Testing AI and ML systems involve determining how well they operate and how accurate they are. Let's know about AI & ML Testing and Future of Software Testing.

[marrywillson]

m have a best AI, ML & Block chain development service company USA. Our Artificial Intelligence technologies usher in a new era of intelligent breakthroughs, unlock boundless discoveries, and improve human potential.

[techinsightweekly]

https://www.zymr.com/blog/smart-loans-how-ai-ml-can-help-reimagine-fintech-lending-platforms

The disruption in recent times underscores the dynamic nature of the financial landscape, especially the lending domain. Changing consumer behavior, new emerging technologies like blockchain, and shift in trends in funding and investment have had a severe impact on how fintech lending solutions are accommodated in the mainstream market. Despite rapid growth for a good period, funding for digital lending faces a significant decline of 53%, with just $11.5 billion raised during the year 2022, as per reports. Therefore, there is a need for fintech software development companies to build lending platforms that can regain ground in this ever-changing environment.

A crucial help in this context comes in the form of Artificial Intelligence. The integration of AI/ML will offer the potential for data-driven decision-making and precise lending models. Therefore, in addition to enhanced efficiency and reduced risks AI/ML services will also reignite interest and investor confidence in the digitalized financial services for lending.

In this blog, we will explore, in detail, how AI machine learning can play a pivotal role in building fintech lending platforms to meet the evolving market demands. We'll examine how AI/ML can help bridge the gap between declining funding and a thriving fintech lending ecosystem while maintaining essential features like CRM, LOS, LMS, and more that borrowers and investors rely on.

[woodjessica123-blog]

How is AI and ML in Test Automation Revolutionizing the Industry

AI and ML in automation have led to a 40% average increase in operational efficiency across various industries. From fitness trackers utilizing AI to enhance training, to smart home assistants simplifying daily tasks, and apps suggesting personalized recommendations for shoppers, their influence is pervasive. We’ve witnessed streaming platforms tailoring music and movie recommendations based on user data, and automation testing tools optimizing test procedures. These technologies are ubiquitous, transforming the way we interact with the world around us.

AIandMLinTestAutomation #AI #ML #TestAutomation

[qualibarinc]

AI & ML technologies in the digital age demand innovative methods of software testing given their paradigm-shifting nature. Utilizing these technologies requires organizations to have up-to-date security measures for business applications. At Qualibar, we assist companies with simplifying such complex processes by providing testing services.

Our AI and ML Technologies:

Data Visualisation

Feature Selection and Reduction

Data Verification

Natural Language Processing

Machine Learning

Artificial intelligence (AI) and machine learning (ML) skills are combined in a novel technique called AI & ML Intelligent Test Automation to automate the testing process. By streamlining their testing procedures, firms can boost productivity and raise the general caliber of their software products.

The automation of test cases is improved with the help of AI and ML, which increases their accuracy and dependability. The system may learn from previous test cases and automatically spot patterns and trends to optimize future testing by utilizing AI algorithms. Regression testing and other time-consuming, repetitive operations can be automated as a result, freeing up valuable resources and allowing teams to concentrate on more important phases of the software development lifecycle.

The automation of test cases is improved with the help of AI and ML, which increases their accuracy and dependability. The system may learn from previous test cases and automatically spot patterns and trends to optimize future testing by utilizing AI algorithms. Regression testing and other time-consuming, repetitive operations can be automated as a result, freeing up valuable resources and allowing teams to concentrate on more important phases of the software development lifecycle.  

The capacity of AI and ML test automation to adjust to and learn from shifting testing needs is one of its main advantages. The system can continuously track and evaluate test results, pinpoint areas that could use improvement, and automatically modify test cases as necessary. This makes it possible to guarantee that the software has undergone rigorous testing and is of the highest caliber. 

For more information visit our website: www.qualibar.com  

[catalyswitch]

Was talking with wife recently about AI and the ways it's incredibly stupid and I am reminded of the time a few years ago the Execs at the place I worked previously wanted to incorporate AI into our workflow in order to help materials development. They wanted to make sure that the company was "utilizing the latest technology to make us more productive" so they partnered with a company that uses AI/ML to predict chemical structures in order to enhance performance based on our desired properties. My boss and I kinda thought this was stupid when it was first announced, but we were still unprepared for how bad it was really going to be.

The problem of course here is that what a computer thinks is good and will perform well does not often make sense according to the laws of physics. So more often than not the computer would spit out extremely specific and nonsensical structures that it believed would increase performance. These structures could range from completely impractical to sometimes downright impossible to actually make, so for every set of predictions we got back we had to first filter all the nonsense and then select a set from the ones that could be made and tested in a reasonable amount of time. In addition, they emphasized that the more data that they have the better the predictions would be, so the pressure was on to synthesize and validate as many molecules as possible as quickly as possible. This was a huge drain on time and energy because again some of these structures were nontrivial to make. Not that the computer people would be able to tell the difference. But still the executives were excited about it so we gave it a try anyway. The idea was that we would start by making a bunch of different materials and test the results and then feed those results back into the machine to predict better structures based on the ever growing data pool.

The funny part of the story, of course, is that with every iteration, the performance got worse. This was not surprising to me. The mechanisms that dictate performance in this field are not fully understood even now, and there are still many papers coming out every year adding more knowledge to the field. Additionally, the predictions weren't being made using some fundamental understanding of the mechanisms at play, but by training an algorithm using a pool of existing literature. You're just not going to get good results by "midjourneying" chemistry. We did around 3-4 iteration cycles with them over that year contract and every time the performance of the structures that it had predicted were worse than the last set, sometimes dramatically so. And they would tell us "no no, the data set isn't really big enough to give good results yet" and "once the model has tested enough structures it'll get better" but it didn't in that period. And it's possible that on a long enough timescale it might be possible? But, the reality was that we had a whole year of time and resources essentially wasted because our CEO thought that some tech guys in SV could use AI to do chemistry and didn't believe us when we said it was stupid.

And you know what? We figured out something that worked really well less than six months after dumping them and getting to do it our way again.

[theomeganerd]

The Witcher 4 Tech Demo Debuts

CD PROJEKT RED and Epic Games Present The Witcher 4 Unreal Engine 5 Tech Demo at The State of Unreal 2025!

At Unreal Fest Orlando, the State of Unreal keynote opened with a live on-stage presentation that offered an early glimpse into the latest Unreal Engine 5 features bringing the open world of The Witcher 4 to life.

Spotlight:

Tech demo showcased how the CD PROJEKT RED and Epic Games are working together to power the world of The Witcher 4 on PC, PlayStation, and Xbox, and bring large open-world support to Unreal Engine. The tech demo takes place in the never-before-seen region of Kovir.

As Unreal Fest 2025 kicked off, CD PROJEKT RED joined Epic Games on stage to present a tech demo of The Witcher 4 in Unreal Engine 5 (UE5). Presented in typical CDPR style, the tech demo follows the main protagonist Ciri in the midst of a monster contract and shows off some of the innovative UE5 technology and features that will power the game’s open world.

The tech demo takes place in the region of Kovir — which will make its very first appearance in the video game series in The Witcher 4. The presentation followed main protagonist Ciri — along with her horse Kelpie — as she made her way through the rugged mountains and dense forests of Kovir to the bustling port town of Valdrest. Along the way, CD PROJEKT RED and Epic Games dove deep into how each feature is helping drive performance, visual fidelity, and shape The Witcher 4’s immersive open world.

 Watch the full presentation from Unreal Fest 2025 now at LINK.

Since the strategic partnership was announced in 2022, CDPR has been working with Epic Games to develop new tools and enhance existing features in Unreal Engine 5 to expand the engine’s open-world development capabilities and establish robust tools geared toward CD PROJEKT RED’s open-world design philosophies. The demo, which runs on a PlayStation 5 at 60 frames per second, shows off in-engine capabilities set in the world of The Witcher 4, including the new Unreal Animation Framework, Nanite Foliage rendering, MetaHuman technology with Mass AI crowd scaling, and more. The tools showcased are being developed, tested, and eventually released to all UE developers, starting with today’s Unreal Engine 5.6 release. This will help other studios create believable and immersive open-world environments that deliver performance at 60 FPS without compromising on quality — even at vast scales. While the presentation was running on a PlayStation console, the features and technology will be supported across all platforms the game will launch on.

The Unreal Animation Framework powers realistic character movements in busy scenes. FastGeo Streaming, developed in collaboration with Epic Games, allows environments to load quickly and smoothly. Nanite Foliage fills forests and fields with dense detail without sacrificing performance. The Mass system handles large, dynamic crowds with ease, while ML Deformer adds subtle, realistic touches to character animation — right down to muscle movement.

Speaking on The Witcher 4 Unreal Engine 5 tech demo, Joint-CEO of CD PROJEKT RED, 

Michał Nowakowski stated:

“We started our partnership with Epic Games to push open-world game technology forward. To show this early look at the work we’ve been doing using Unreal Engine running at 60 FPS on PlayStation 5, is a significant milestone — and a testament of the great cooperation between our teams. But we're far from finished. I look forward to seeing more advancements and inspiring technology from this partnership as development of The Witcher 4 on Unreal Engine 5 continues.”

Tim Sweeney, Founder and CEO of Epic Games said: 

“CD PROJEKT RED is one of the industry’s best open-world game studios, and we’re grateful that they’re working with us to push Unreal Engine forward with The Witcher 4. They are the perfect partner to help us develop new world-building features that we can share with all Unreal Engine developers.”

For more information on The Witcher 4, please visit the official website. More information about The Witcher series can be found on the official official website, X, Bluesky, and Facebook.

[covalense]

The Synergy of AI and ML in Testing

Conclusion

AI and ML-based testing represent a paradigm shift in software quality assurance, empowering organizations to achieve higher levels of efficiency, effectiveness, and agility in their testing practices. By harnessing the power of intelligent automation, predictive analytics, and adaptive testing, teams can deliver high-quality software at scale while accelerating innovation and reducing time-to-market.

[coldalbion]

"Grok has real-time access to info via the 𝕏 platform, which is a massive advantage over other models"

You know how a while back folks were saying that the change to X/Twitter's TOS to allow scraping of user data for ML/AI was just a standard thing and no one should care?

Yeah.