#Continuous Testing

[qualityassurance11]

How AI Is Shaping the Future of Test Automation 

In today’s hyper-competitive digital environment, software development cycles are shorter, user expectations are higher, and the margin for error is razor-thin. To keep up, organizations have adopted test automation as a critical part of their quality assurance (QA) strategies. However, traditional test automation methods often fall short in scalability, adaptability, and efficiency. Enter Artificial Intelligence (AI).

AI is changing the game in software testing by introducing a new level of intelligence and automation. From smarter test creation to predictive analytics, AI is helping QA teams move beyond repetitive scripting into a more strategic and proactive role. This blog explores how AI is reshaping the landscape of test automation and what the future holds, especially with GhostQA, your go-to AI-powered test automation solution.

1. Smarter Test Case Generation

One of the most labor-intensive aspects of testing is creating and maintaining test cases. Traditionally, QA engineers would manually write test scripts based on requirements and user stories. With AI, this process becomes significantly faster and more intelligent.

Machine learning models can analyze source code, historical test data, and user behavior to generate relevant and high-coverage test cases. These AI-generated tests focus on the most commonly used features and identify edge cases that might otherwise go untested.

Example: Tools like Testim, Functionize, and TestCraft use AI to suggest test scenarios based on how users interact with the application, ensuring more meaningful test coverage.

2. Self-Healing Test Scripts

Test maintenance is another major hurdle in automation. A small change in the UI — like renaming a button or changing a field’s position — can cause test scripts to fail. AI addresses this issue through what are called self-healing tests.

Self-healing automation frameworks use machine learning to detect when a UI element has changed and automatically update the test scripts accordingly. This eliminates the need for testers to constantly fix broken tests, thereby improving stability and reducing downtime.

Example: AI-based platforms like Mabl and Testim recognize page elements not just by their static identifiers but also by their behavior, attributes, and surrounding context.

3. Predictive Analytics for Risk-Based Testing

AI can analyze past testing data and identify patterns that indicate where failures are most likely to occur. This is particularly helpful in large-scale enterprise environments where running every test case may be impractical.

By using predictive analytics, QA teams can prioritize testing efforts on high-risk areas of the application, focusing resources where they are most needed. This leads to faster feedback cycles and more efficient test planning.

Example: AI algorithms can analyze commit histories, previous defects, and test results to flag potentially risky code changes that require deeper testing.

4. Visual Validation with Computer Vision

Traditional automation frameworks struggle with UI validation, often relying on pixel-based comparisons that are brittle and error-prone. AI, specifically computer vision, brings a new dimension to visual testing.

AI-driven visual validation tools can detect layout shifts, misalignments, and color inconsistencies with far greater accuracy. They understand the structure of the user interface and can even validate responsiveness across devices and screen sizes.

Example: Applitools uses Visual AI to compare baseline and current UI states, highlighting only meaningful visual differences and ignoring minor, non-critical changes.

5. Natural Language Processing for Test Creation

AI-powered Natural Language Processing (NLP) makes it easier for non-technical team members to contribute to test automation. Instead of writing complex scripts, users can write test cases in plain English.

The AI engine interprets the intent and context of these instructions and converts them into executable test scripts. This democratizes the testing process and enhances collaboration between QA, development, and business stakeholders.

Example: Tools like TestRigor and Katalon Studio allow test scripts to be created using natural language, dramatically lowering the barrier to entry for test automation.

6. Continuous Testing in CI/CD Pipelines

In DevOps environments, continuous testing is essential to ensure rapid releases without compromising quality. AI enhances CI/CD pipelines by automatically analyzing build logs, detecting flaky tests, and providing intelligent insights.

AI-driven test orchestration platforms can dynamically decide which tests to run based on code changes, impact analysis, and historical failure rates. This leads to faster, more reliable deployments.

Example: Intelligent test orchestration tools like Launchable and TestGrid use AI to reduce test cycle times and prioritize execution based on test impact.

7. The Human + AI Collaboration

It’s important to emphasize that AI is not replacing human testers but augmenting their capabilities. While AI can handle repetitive, time-consuming tasks, human testers bring critical thinking, creativity, and domain expertise to the table.

By automating mundane aspects of testing, AI frees up QA teams to focus on exploratory testing, UX validation, and strategic decision-making. This synergy leads to better software and a more empowered QA workforce.

Conclusion

The integration of AI in test automation is no longer a futuristic concept — it’s happening right now. AI is making testing faster, smarter, and more efficient by automating routine tasks, improving accuracy, and enabling proactive quality management.

As tools continue to evolve, organizations that embrace AI-driven testing, like those provided by GhostQA, will have a significant competitive edge. The future of QA is not just automated; it’s intelligent.

At GhostQA, we’re committed to leveraging AI to streamline your testing process, helping you achieve faster, more reliable releases with cutting-edge technology.

By incorporating GhostQA into your test automation strategy, you can take advantage of the latest in AI-driven quality assurance tools to ensure your software is always at its best. Discover how we can optimize your QA efforts at GhostQA.

[coderower]

Automated Testing the Future of Software Testing 

Explore how automated testing is revolutionizing software quality assurance, enhancing efficiency and accuracy in today’s digital landscape.

In the hastily evolving landscape of software development, the need for green and reliable checking out methods has by no means been greater. As packages grow to be more complicated and necessary to our day by day lives, the demand for first-rate warranty has surged. Enter automated testing—a modern solution that is reworking the way software program is tested. This blog explores the evolution,…

[nous-infosystems]

Efficiency Meets Quality: The Power of Tricentis Implementation in Continuous Testing

Tricentis Implementation: Accelerate and Automate Continuous Testing

Current organizations strive to deliver high-quality applications at an accelerated pace. This requires efficient and effective software testing practices to ensure optimal performance and functionality. Tricentis implementation is revolutionizing the testing process by offering a comprehensive solution to accelerate and automate continuous testing, enabling organizations to achieve their software delivery goals seamlessly. This blog post will delve into Tricentis implementation and explore its benefits for agile software development.

Tricentis implementation introduces a smart and impactful analysis of software changes through intelligent test automation. With its advanced features like innovative impact analysis, regression testing, and model-based test automation, Tricentis empowers organizations to identify the impact of changes and efficiently plan their testing efforts. By automating the creation and execution of test cases, Tricentis streamlines the testing process, saving time and effort compared to traditional manual testing methods.

The Tricentis continuous testing platform is designed to integrate testing seamlessly into the agile and DevOps processes. It enables organizations to optimize and accelerate their software testing by providing real-time feedback on quality and performance. With Tricentis, teams can efficiently conduct end-to-end data integrity testing, ensuring that applications perform flawlessly across various platforms and environments. This reduces the risk of issues arising during production and enhances overall customer satisfaction.

Tricentis Tosca, a flagship product of Tricentis, is a versatile and powerful testing tool that caters to diverse software testing needs. It offers an intelligent test automation suite, enabling organizations to efficiently create and execute automated tests. With Tricentis Tosca's intuitive user interface, teams can design and manage UI tests effortlessly, ensuring maximum test coverage and accuracy. Its native testing capabilities for mobile apps further enhance the testing process, allowing organizations to deliver robust applications across multiple platforms.

One of the key advantages of Tricentis implementation is its ability to optimize and accelerate the testing process in agile and DevOps environments. By integrating seamlessly with CI/CD pipelines, Tricentis enables organizations to achieve faster software delivery cycles without compromising quality. It supports efficient test data management, allowing teams to generate and manage test data effectively, further enhancing the testing process's speed and reliability.

With Tricentis implementation, organizations can leverage the power of real-time analytics and reporting to gain actionable insights into their testing efforts. Tricentis provides comprehensive dashboards and reports with visibility into test coverage, execution results, and quality metrics. This enables teams to make data-driven decisions, identify bottlenecks, and continuously improve their testing processes for optimal results.

Conclusion

Tricentis implementation is revolutionizing the way organizations approach software testing. By accelerating and automating continuous testing, Tricentis empowers agile and DevOps teams to deliver high-quality applications at an accelerated pace. With its intelligent test automation capabilities, comprehensive testing tools like Tricentis Tosca, and seamless integration with CI/CD pipelines, Tricentis implementation is a game-changer for organizations aiming to achieve faster software delivery cycles without compromising quality.

[vijayashree4400]

Importance of Continuous Testing in DevOps 

DevOps – An Overview: 

DevOps, a combination of development and operations, is a set of practices that aims to shorten the system development life cycle and deliver high-quality software continuously. It focuses on collaboration, automation, and continuous feedback among developers and IT professionals. In a DevOps environment, Continuous Integration (CI) and Continuous Deployment (CD) are crucial components, and they rely heavily on another essential aspect - Continuous Testing Services. 

Importance of Continuous Testing: 

Continuous Testing plays a pivotal role in the success of DevOps practices. It involves automated testing throughout the development pipeline, ensuring that every code change is thoroughly tested before deployment. Here are some key initiatives from different functions of software development that highlight the significance of Continuous Testing: 

Accelerated Development Cycles: Continuous Testing enables rapid development cycles by automating the testing process. With each code change, automated tests are executed, providing rapid feedback to developers. This quick feedback loop allows for early detection of bugs, reducing the time and effort required for debugging. 

Enhanced Code Quality: Continuous Testing ensures that code changes do not introduce new defects or issues. Automated testing guarantees that the software meets predefined quality standards, leading to higher code quality. This, in turn, reduces the likelihood of critical issues surfacing in production. 

Reduced Time to Market: The seamless integration of testing into the development pipeline minimizes delays caused by manual testing processes. Continuous Testing accelerates the delivery of software, enabling organizations to bring new features and updates to the market faster. 

Increased Collaboration: DevOps emphasizes collaboration between development, testing, and operations teams. Continuous Testing fosters communication and collaboration by providing a common framework for testing. This shared approach encourages a collective responsibility for the quality of the software. 

Improved Risk Management: Continuous Testing helps identify and address potential risks early in the development process. By automating the testing of critical functionalities and edge cases, teams can mitigate the risk of system failures and unexpected issues in production. 

Conclusion: 

In the dynamic landscape of software development, where agility and reliability are paramount, Continuous Testing stands as a linchpin in the DevOps framework. It aligns with the principles of collaboration, automation, and feedback, ensuring that software development and deployment are seamless, efficient, and error-free. As organizations increasingly adopt DevOps practices, recognizing the importance of Continuous Testing becomes imperative for achieving faster time-to-market, enhanced code quality, and overall business success. 

Discover invaluable insights and best practices for implementing The Best DevOps Strategy for Continuous Testing in 2024!  in our comprehensive blog. 

[sensitiveheartless]

[market-insider]

Automation Testing Insights: Transforming Testing

The global automation testing market size is expected to reach USD 92.45 billion by 2030. Prominent technological advancement in artificial intelligence (AI) and machine learning (ML) is propelling the demand for the market. Mobile application usage is growing across various end-user industries, and smartphone penetration is rising, opening an attractive potential for market expansion. Furthermore, there is a rising demand for automation testing as web-based applications have developed significantly and new software technologies have emerged. The market is growing rapidly due to the increasing usage of ML and AI for advanced analytics and continuous testing across DevOps and DevSecOps areas.

Automation Testing Market Report Highlights

The services segment dominated the market and accounted for over 56% of the global revenue owing to rapid advancements in implementation services, which make it easier to include automation into a functioning infrastructure for software testing

With the aid of this service, automation is integrated into an existing software automation testing setup

The large enterprises segment held the largest revenue share in 2022 as it helps improve efficiency, reduce manual effort, increase test coverage, and ensure the quality of software applications

The BFSI segment is estimated to have significant growth over the forecast period; adopting digitalization in the BFSI sector creates a significant demand for application software automation testing

Gain deeper insights on the market and receive your free copy with TOC now @: Automation Testing Market Report

The emerging use of RPA to automate time-consuming, error-prone manual processes are just a few instances of the usage of AI & ML in automation testing. Moreover, a bot uses the page’s numerous links and web forms to systematically explore through an online application when web crawling or spidering. This is a new use for AI and ML in automation testing. This approach is typically used for indexing online browsing. It may be improved further to perform reverse engineering on an application being tested and automatically find Test Cases. Emerging automation testing tools are significantly fueling market growth.

For instance, Testcraft, a codeless Selenium test automation platform for regression and continuous testing, as well as monitoring of web applications, is gaining traction among users. Their revolutionary AI tech removes maintenance time and cost, as it certainly affects changes in the app. Similarly, Applitools Eyes, Testim, and Test.ai are more automation testing tools propelling the market growth. Furthermore, mergers and acquisitions by other key players are propelling market growth. For instance, in 2022, to improve the user experience on 5G smartphones, Key sight introduced AI-driven and automated testing. Automation and AI enable mobile service providers and app developers to more swiftly evaluate how smartphone users engage with native apps in the real world.

[blondeaxolotl]

The insane part is that he's probably the most stylized and possibly my favourite design I've done so far, 💜moth💜epel💜

[bathylychnops]

quick headturnnn theyre funn theyre lookingat eachother imight finish stephens lines later and sequence these idk ilike hes big big eyes ghibli tears

oh my goddd i hate animating in csp SO MUCH ya i got to usemy lineart brush but. clutches myhead itsnot worthiut use a proper animation softwatre pleaasee tvpaint takeme back tvpaint pleause..

[kizzer55555]

Core Gems

So when a ghost becomes injured, they have a last ditch defense where they retreat into their core. And I mean, injured badly where their body is rip apart to the point they can’t hold a solid form anymore. And they basically go into a hibernation state until they are strong enough to form again.

Ellie, Danny, and Dan are all injured in a final battle against the GIW. The organization was destroyed and the ghosts were safe but the halfas ended up being so injured that they reverted to core form and then went to sleep for a bit. When they woke up, they were still weak but at least recovered enough to gain consciousness. And realize…they are in some kind of auction…in the middle of a heist. It appeared that two furries (one in a bat costume and one in a cat costume) were ducking it out. And they…they were a necklace. All three of them had been turned into a necklace with their cores as gems accompanied by sapphires, pearls, and opals. And frankly gorgeous craftsmanship as the metal was crafted around their cores as if to cradle them and the other gems.

Unfortunately, they were too weak to take a form properly, they could still feel the strain on their bodies. But at least they could still communicate through their auras. Then the cat lady punched a hole in the glass container surrounding them and grabbed their necklace.

However, the bat grabbed the other end and it resulted in a sort of tug-a-war. Meanwhile, Danny, Ellie, and Dan were having a back and form commentary on the situation and what they should do. Completely unheard by the other party.

In the corner of their eye, the three halfas finally noticed a third contender. Some kind of clown who was…hold on…holding a gun?! And it was pointed straight at the two fighting furies who had yet to notice him. The ghosts’ protective instincts went into overdrive and they frantically tried to shout, yell, move. Just do something to warn the two but their cries fell on deaf ears. All they succeeded in doing was faintly glow which immediatly caught the attention of the fighting duo. The two turned to look at the strange necklace but right at that moment, the clown fired and a gunshot rang throughout the auction room. Having no other options, Danny and the others poured every ounce of ectoplasm they had to try and phaseshift, making the two furries intangible as the bullets passed right through them, but in their shock, the two jumped away in opposite directions and accidentally ripped the necklace apart. Gems and pearls went flying and the three cores bounced along the ground.

Luckily, the two finally noticed the clown and went to deal with him and his minions who had appeared. Seemingly putting their fight on hold and forming a temporary truce. The three halfas could only watch as the battle finally wound down, ending with the cops barging into the place and arresting the clown and his grunts, the cat managing to escape with half the scattered gems and pearls from the broken necklace along with a few other jewelry pieces (none of their cores though) and the bat leaving through a skylight.

The auction continued and in the end, despite being broken, their necklace seemed to have caught someone’s interest. A man named Bruce Wayne bought up every piece of the shattered jewelry wear. The auctioneers appeared relived that the item managed to sell in the end and gratefully gave it to him.

Bruce had no idea what happened at the auction, but he could have sworn that some of the gems faintly glowed right before he and Selina were shot. If the necklace was some sort of magical item, then he needed to understand exactly what has been brought to Gotham. It was unfortunate that Selena had taken some parts of the necklace but he utilized his vast wealth to make sure all the other parts ended in his possession. Now he would take them back to the mansion for examination.

[qualityassurance11]

QA vs. Software Testing: What's the Difference and Why It Matters

In the world of software development, terms like Quality Assurance (QA) and Software Testing are often used interchangeably. However, while both contribute to software quality, they serve distinct purposes.

Think of QA as the blueprint that ensures a house is built correctly, while software testing is the process of inspecting the finished house to ensure there are no cracks, leaks, or faulty wiring. QA is proactive—preventing defects before they occur, whereas software testing is reactive—detecting and fixing bugs before deployment.

Understanding the difference between QA and software testing is crucial for organizations to build reliable, high-performing, and customer-friendly software. This blog explores their differences, roles, and why both are essential in modern software development.

What is Quality Assurance (QA)?

Quality Assurance (QA) is a systematic approach to ensuring that software meets defined quality standards throughout the development lifecycle. It focuses on process improvement, defect prevention, and maintaining industry standards to deliver a high-quality product.

Instead of identifying defects after they appear, QA ensures that the development process is optimized to reduce the likelihood of defects from the beginning.

Key Characteristics of QA:

Process-Oriented: QA defines and improves the software development processes to minimize errors.

Preventive Approach: It prevents defects before they arise rather than finding and fixing them later.

Covers the Entire Software Development Lifecycle (SDLC): QA is involved from requirement gathering to software maintenance.

Compliance with Industry Standards: QA ensures the software adheres to ISO, CMMI, Six Sigma, and other quality benchmarks.

Key QA Activities:

Defining Standards & Guidelines – Establishing coding best practices, documentation protocols, and process frameworks.

Process Audits & Reviews – Conducting regular audits to ensure software teams follow industry standards.

Automation & Optimization – Implementing CI/CD (Continuous Integration/Continuous Deployment) to streamline development.

Risk Management – Identifying potential risks and mitigating them before they become major issues.

Example of QA in Action:

A company implementing peer code reviews and automated CI/CD pipelines to ensure all new code follows quality guidelines is an example of QA. This process prevents poor-quality code from reaching the testing phase.

What is Software Testing?

Software Testing is a subset of QA that focuses on evaluating the actual software product to identify defects, errors, and performance issues. It ensures that the software behaves as expected and meets business and user requirements.

Testing is performed after the development phase to verify the correctness, functionality, security, and performance of the application.

Key Characteristics of Software Testing:

Product-Oriented: Testing ensures the final product works as expected and meets user requirements.

Defect Detection & Fixing: The main goal is to identify and fix bugs before software release.

Different Testing Methods: Includes manual and automated testing, covering functionality, usability, performance, security, and compatibility.

Part of the Software Testing Life Cycle (STLC): Testing occurs after development and follows a structured cycle of planning, execution, and bug tracking.

Types of Software Testing:

Functional Testing: Verifies that the software functions as per the requirements.

Unit Testing: Checks individual components or modules.

Integration Testing: Ensures different modules work together correctly.

System Testing: Tests the complete application to validate its behavior.

Performance Testing: Measures speed, scalability, and responsiveness.

Security Testing: Identifies vulnerabilities to prevent security breaches.

Example of Software Testing in Action:

Running automated UI tests to check if a login form accepts correct credentials and rejects incorrect ones is an example of software testing. This ensures that the application meets user expectations.

Key Differences Between QA and Software Testing

Focus: Quality Assurance (QA) is a process-oriented approach that ensures the entire software development process follows best practices and quality standards to prevent defects. In contrast, software testing is product-oriented and focuses on detecting and fixing bugs in the developed software.

Goal: The primary goal of QA is to prevent defects from occurring in the first place by refining development and testing methodologies. On the other hand, software testing aims to identify and fix defects before the software is released to users.

Scope: QA encompasses the entire Software Development Life Cycle (SDLC), ensuring that each phase—from requirement analysis to deployment—adheres to quality standards. In contrast, software testing is a subset of QA and is mainly concerned with validating the functionality, performance, security, and reliability of the software.

Approach: QA follows a proactive approach by setting up quality checkpoints, code reviews, and documentation processes to reduce the chances of defects. Software testing, however, takes a reactive approach, meaning it focuses on identifying existing issues in the software after the development phase.

Activities Involved: QA activities include process audits, documentation reviews, defining coding standards, implementing CI/CD pipelines, and process optimization. In contrast, software testing involves executing test cases, performing unit testing, integration testing, functional testing, performance testing, and security testing to ensure the software meets the required specifications.

Example of Implementation: A company implementing peer code reviews, automated build testing, and compliance audits as part of its development process is engaging in QA. On the other hand, running test cases on a login page to check if valid credentials allow access while invalid ones do not is an example of software testing.

By understanding these differences, organizations can ensure they integrate both QA and testing effectively, leading to higher software quality, fewer defects, and a better user experience.

Why Both QA and Software Testing Matter

Some organizations mistakenly focus only on testing, believing that identifying and fixing bugs is enough. However, without strong QA practices, defects will continue to arise, increasing development costs and delaying software delivery. Here’s why both QA and testing are crucial:

1. Ensures High-Quality Software

QA minimizes errors from the start, while testing ensures no critical issues reach the end-user.

Organizations following robust QA practices tend to have fewer post-release defects, leading to better product stability.

2. Reduces Cost and Time

Finding a bug during requirement analysis is 10x cheaper than fixing it after deployment.

QA ensures that software defects are avoided, reducing the need for excessive testing and bug-fixing later.

3. Enhances User Experience

A well-tested software application performs smoothly without crashes or failures.

Poor QA and testing can result in negative user feedback, harming a company’s reputation.

4. Supports Agile and DevOps Practices

In Agile development, continuous QA ensures each sprint delivers a high-quality product.

DevOps integrates QA automation and continuous testing to speed up deployments.

5. Helps Meet Industry Standards & Compliance

Industries like finance, healthcare, and cybersecurity have strict quality standards.

QA ensures compliance with GDPR, HIPAA, ISO, and PCI DSS regulations.

How to Balance QA and Testing in Your Software Development Process

Implement a Shift-Left Approach: Start QA activities early in the development cycle to identify defects sooner.

Adopt CI/CD Pipelines: Continuous integration and automated testing help streamline both QA and testing efforts.

Use Test Automation Wisely: Automate repetitive test cases but retain manual testing for exploratory and usability testing.

Invest in Quality Culture: Encourage developers to take ownership of quality and follow best practices.

Leverage AI & Machine Learning in Testing: AI-driven test automation tools can improve defect detection and speed up testing.

Conclusion

While QA and software testing are closely related, they are not the same. QA is a preventive, process-oriented approach that ensures quality is built into the development lifecycle. In contrast, software testing is a reactive, product-focused activity that finds and fixes defects before deployment.

Organizations that balance both QA and testing effectively will consistently build high-quality, defect-free software that meets user expectations. By implementing strong QA processes alongside thorough testing, companies can save costs, speed up development, and enhance customer satisfaction.

[qoldenskies]

no more birthdays - sophie may

[canary continuity]

[m-1-8]

When sparring out of your weight class, you gotta fight dirty [part 2]

[mistercakerz]

LMAO IM TOO SHY TO POST IT ANYWHERE BUT COMIC PAGE TEST THINGY LOL FJAEIAWFEIOJAOFEIWJAWFEIOJAWEFIOJAWFEIOJAWEIFJIJA

[the--firevenus]

The best trope for superbat for me is Bruce falls first and Clark falls hardest, and add slow burn to the mix and it's DELICIOUS

Bruce who's did in fact falls first but didn't say anything and overtime get used to the feeling of butterflies in his stomach so now he just accepts it and didn't act on it at all. Vs Clark who one day slowly putting pieces of puzzle together in his head and fall head first to the crush zone and he falls HARD, HOW CAN HE BE NORMAL NOW??

[sensitiveheartless]

They’re having a sleepover :0 (Neither of them are very familiar with sleepovers in practice, but mutually decided there should be plenty of pillows for ammunition purposes) (their battle shall be legendary once one of them inevitably decides to instigate)