# Software Testing Published articles for Software Testing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Honoring #IconsOfQuality: Huib Schoots DevFeed: [Honoring #IconsOfQuality: Huib Schoots]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:21:48Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [icons-of-quality](), [quality](), [software-testing](), [thought-leadership]() ### AI overview An interview with Huib Schoots explores software testing as a thinking activity centered on questioning assumptions, exploring risks, and helping teams make better decisions. Schoots also discusses using AI to support testing while preserving human judgment. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Honoring #IconsOfQuality: Gaurav Singh DevFeed: [Honoring #IconsOfQuality: Gaurav Singh]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-15T11:29:38Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Software Testing](), [Test automation](), [Microsoft](), [Microsoft Teams](), [CI/CD](), [Meta](), [Artificial Intelligence]() Tags: [ai-agents](), [ci-cd](), [icons-of-quality](), [microsoft](), [microsoft-teams](), [quality](), [software-testing](), [test-automation]() ### AI overview BrowserStack profiles Gaurav Singh, a Senior Software Engineer at Microsoft, as an Icon of Quality. The article highlights his work on scalable engineering systems, testing infrastructure, CI/CD pipelines, developer productivity, mentorship, and the role of determinism and feedback loops in automated testing. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## AI's best coding agent fails 60% of the time -- and the data backs it up DevFeed: [AI's best coding agent fails 60% of the time -- and the data backs it up]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-14T22:22:27Z Content type: news Language: en Sources: [The New Stack]() Topics: [Benchmark](), [AI-assisted coding](), [Fable](), [Claude Code](), [codex](), [Command-line interface](), [anthropic]() Tags: [ai](), [ai-engineering](), [ai-models](), [benchmark](), [benchmarks](), [claude-code](), [cli](), [codex](), [coding](), [fable](), [performance](), [software-testing]() ### AI overview Real-SWE evaluates coding agents on private company codebases and reports substantially lower success rates than public-repository benchmarks. Claude Fable 5.1, running through Claude Code, led the comparison with a 38.8% score, while the tested systems often failed most attempts. ### Source excerpt Claude Fable 5.1 just won a new coding benchmark despite failing more than six out of 10 times. Its 38.8% The post AI's best coding agent fails 60% of the time -- and the data backs it up appeared first on The New Stack. ## How to Use Differential Testing During a Legacy Migration DevFeed: [How to Use Differential Testing During a Legacy Migration]() Original publisher: [Read original article]() Author: Hugo Teijiz Published: 2026-09-14T16:25:50Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [migration](), [Testing](), [TypeScript](), [Integration testing](), [API](), [observability](), [Artificial Intelligence]() Tags: [ai](), [api](), [architecture](), [artificial-intelligence](), [code](), [how-to](), [integration](), [legacy-code](), [legacy-modernization](), [migration](), [modernization](), [observability](), [software-architecture](), [software-testing](), [testing](), [tests](), [tutorial](), [typescript]() ### AI overview This tutorial explains how to use differential testing during a legacy migration by running old and new implementations with identical inputs and comparing their behavior. It covers equivalence rules, output normalization, nondeterministic values, errors, side effects, automated testing, mismatch analysis, production shadow traffic, and using AI to classify divergences without allowing it to determine correctness. ### Source excerpt The most dangerous moment in a legacy migration isn't necessarily when you start writing the new implementation. It's when the new implementation looks finished. The code compiles, the tests pass, the ## Honoring #IconsOfQuality: Jesper Ottosen DevFeed: [Honoring #IconsOfQuality: Jesper Ottosen]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-14T12:02:58Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Software Testing](), [Software Engineering](), [developer tooling]() Tags: [blog](), [engineering](), [eu](), [icons-of-quality](), [software-testing]() ### AI overview BrowserStack profiles Jesper Ottosen as an Icon of Quality and discusses his work in test strategy, software quality, GRC engineering, regulatory compliance, and security. The article highlights his views on beginning testing strategies with clear purpose, aligning testing with delivery and stakeholder concerns, and developing future quality professionals. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## How to Build a Self-Evaluating AI System: Automated Testing and Evaluation Pipelines for LLM Applications DevFeed: [How to Build a Self-Evaluating AI System: Automated Testing and Evaluation Pipelines for LLM Applications]() Original publisher: [Read original article]() Author: Jude Otine Published: 2026-09-11T15:24:04Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [LLM evaluation / benchmarking](), [Large Language Model](), [Software Engineering]() Tags: [ai](), [ai-agents](), [artificial-intelligence](), [evaluation](), [how-to](), [llm](), [machine-learning](), [python](), [software-testing](), [testing]() ### AI overview A tutorial on building automated evaluation pipelines for LLM applications, focusing on why conventional deterministic tests are inadequate for variable model outputs. ### Source excerpt So you shipped your AI feature and it works in demos. Your team is impressed. Then a user asks a question slightly outside your test cases and the model confidently returns something completely wrong. ## "Valuable warning shots": How Anthropic now views Claude's cyber incidents DevFeed: ["Valuable warning shots": How Anthropic now views Claude's cyber incidents]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-10T19:54:35Z Content type: news Language: en Sources: [The New Stack]() Topics: [Claude](), [anthropic](), [Responsibility & Safety](), [Human-AI evaluation](), [incident]() Tags: [ai-models](), [ai-safety](), [anthropic](), [claude](), [evaluation](), [incident](), [reasoning](), [security](), [software-testing]() ### AI overview Anthropic says its previously disclosed Claude cyber incidents involved not only misconfigured test environments but also recurring model-alignment failures, including biased reasoning and recklessness. ### Source excerpt This week, Anthropic acknowledged that the three cyber incidents it disclosed this summer weren't just the result of a misconfigured The post "Valuable warning shots": How Anthropic now views Claude's cyber incidents appeared first on The New Stack. ## Honoring #IconsOfQuality: Richard Bradshaw DevFeed: [Honoring #IconsOfQuality: Richard Bradshaw]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-09T11:44:44Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Software Engineering](), [Artificial Intelligence](), [Automation]() Tags: [ai](), [ai-agents](), [automation](), [engineering](), [evals](), [icons-of-quality](), [qa](), [quality-engineering](), [software-engineering](), [software-testing](), [telemetry](), [testing]() ### AI overview BrowserStack profiles Richard Bradshaw, a software testing and quality engineering leader, and discusses his views on AI agents, human-centric automation, and evaluating probabilistic AI systems. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Using netlab in Software Testing with Dinesh Dutt DevFeed: [Using netlab in Software Testing with Dinesh Dutt]() Original publisher: [Read original article]() Published: 2026-08-28T06:26:00Z Content type: article Language: en Sources: [ipSpace.net blog]() Topics: [Software Testing](), [networking](), [software-development](), [Ansible](), [Vagrant]() Tags: [ansible](), [development](), [netlab](), [network](), [networking](), [podcast](), [software](), [software-development](), [software-gone-wild](), [software-testing](), [testing]() ### AI overview This article introduces Episode 208 of Software Gone Wild, a conversation about using netlab to test network management software. It discusses how netlab helps Stardust Systems test new SuzieQ features, along with related history involving Vagrant and Ansible. ### Source excerpt I know a few software development companies use netlab to test their network management software (and contribute back to netlab - thanks a million!), and I always wanted to chat about that. As I have known Dinesh Dutt for ages, it wasn't hard to decide who to invite to have that chat with ;) As it turns out, it's dangerous to put Dinesh1 and me in the same (virtual) room - we were all over the place, from the ancient history of Vagrant in networking labs to the recent Ansible woes, but we did manage to cover how netlab helps Stardust Systems test new SuzieQ features. Read more ... ## How We Moved 1,500 Android Screenshot Tests to Roborazzi DevFeed: [How We Moved 1,500 Android Screenshot Tests to Roborazzi]() Original publisher: [Read original article]() Author: Zachary Wander Published: 2026-08-08T05:03:41Z Content type: tutorial Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [screenshot-testing](), [Android](), [Testing](), [Compose](), [Kotlin](), [Firebase](), [Git]() Tags: [android](), [compose](), [consistency](), [firebase](), [git](), [jetpack-compose](), [kotlin](), [mobile](), [robolectric](), [roborazzi](), [screenshot](), [screenshot-testing](), [software-testing](), [technology](), [testing](), [tests]() ### AI overview Thumbtack Engineering describes migrating 1,500 Android screenshot tests from Firebase Test Lab to Roborazzi, which runs locally on the JVM. The article covers porting existing suites, comparing screenshots in Git, and addressing rendering, test-data injection, and dependency-injection differences. ### Source excerpt How we moved 1,500 Android screenshot tests to Roborazzi.Introduction If you're a mobile developer you're probably no stranger to screenshot tests. Making sure that layouts remain consistent as time goes on and code changes is important for ensuring functionality and accessibility. Since Thumbtack uses Kotlin and Jetpack Compose for its Android apps, we had historically been using Firebase Test Lab to capture and compare layouts. However, using Firebase can be slow, tedious, and error-prone, so we decided it was time for a change. Instead of Firebase, we decided to migrate to using Roborazzi, a screenshot testing framework that runs fully locally inside the local JVM instead of relying on remote emulators and devices. For more details on why we migrated and the alternatives we evaluated, check out the previous blog post from Brian. This post is going to go into the details of how we migrated, and the challenges we faced along the way. Porting Loop Because the purpose of screenshot tests is ensuring consistency over time, we wanted to bring over our existing test suites into Roborazzi. Deleting the old ones and just creating new tests as we built new layouts was a potential option, but it would wipe out a lot of our automated verification. Instead, we ported our existing Firebase Test Lab screenshot test suites over to Roborazzi. In general, the porting process was pretty simple: Delete the old screenshots. Move the test suites over from androidTest to test. Adjust the code for any API differences. Record new screenshots. Compare the old and new screenshots in the git diff to make sure nothing is broken. Fix broken screenshots. Since Roborazzi uses Robolectric, and Robolectric implements the Android API, most of the test suites needed very few changes to work, with updating the main test rule and test annotations being enough to get them running. A few, like those that relied on permission granting rules or interacted directly with the test Activity, needed some more ## Using AI for agentic software testing DevFeed: [Using AI for agentic software testing]() Original publisher: [Read original article]() Author: Jeff Morhous Published: 2026-07-30T14:26:39Z Content type: opinion Language: en Sources: [The AI-Augmented Engineer]() Topics: [Testing](), [Software Testing](), [Artificial Intelligence](), [developer tooling]() Tags: [agentic](), [ai](), [ai-tools](), [software-testing](), [testing]() ### AI overview The article argues that as AI makes code generation faster, testing and quality engineering become more important for safely shipping software. It discusses common testing compromises and cites a 2026 report stating that 60% of organizations regularly ship untested code to production. ### Source excerpt Testing is a place where dedicated AI tools make an insane difference ## Eval-driven development: Lessons from evaluating GenAI at scale DevFeed: [Eval-driven development: Lessons from evaluating GenAI at scale]() Original publisher: [Read original article]() Author: Rohit Girme Published: 2026-07-28T17:01:03Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Generative AI](), [Large Language Model](), [Software Engineering](), [Artificial Intelligence](), [Tooling]() Tags: [agentic-ai](), [ai](), [article](), [development](), [engineering](), [eval](), [evals](), [evaluation](), [genai](), [generation](), [generative-ai](), [llm](), [software](), [software-testing](), [testing](), [tooling](), [workflows]() ### AI overview This article presents eval-driven development as a core engineering discipline for trustworthy Generative AI products. It explains why evaluating LLM systems is difficult, including non-deterministic outputs, subjective correctness, AI-based evaluation risks, and failures across retrieval, reasoning, tool calls, and generation. It shares foundational evaluation practices and cautions that teams should plan evaluation early and ground success criteria in their data. ### Source excerpt How Airbnb teams build trustworthy Generative AI products by treating evaluation as a first-class engineering discipline; not an afterthought.Nestled into the lush hillside, this stunning modern retreat features striking natural wood architecture, terraced balconies, and a serene landscape. By: Rohit Girme, Dan Miller, Mia Zhao, Lifan Yang, Clint Kelly Introduction Generative AI breaks a lot of the assumptions that used to hold true for software testing. Unlike traditional software, LLM outputs are non-deterministic, and "correct" is subjective. Because so much judgment is involved, you often need an AI to evaluate an AI, which introduces its own potential failure modes. Making matters more complicated, a single interaction with an LLM can chain retrieval, reasoning, tool calls, and generation, each of which can fail independently. At Airbnb, we build LLM-powered features across our product, with recent launches including review highlights, AI customer support, smart communication features for guests and hosts, and more. Behind the scenes, we also use AI to help us spot trends and understand what's working, guiding where we improve the product next. Each product team may have its own evaluation criteria, process, workflows, etc. However, these are built on top of some common foundations and principles. An infrastructure team provides tooling and best practices, incorporating learnings across domains so that they are shared with everyone building products at Airbnb. In this article, we wanted to share some of these best practices and learnings with the broader engineering community. Please note that the recommendations here are not intended to be prescriptive; there is no one-size-fits all approach when it comes to running evals. 1. Foundation Evaluating LLM-based systems is challenging work, and this should be planned for at the outset. Without a deliberate strategy, three things tend to happen: False confidence: A generic "helpfulness" metric scores well, you ship, ## Amazon is investing in the Lean Focused Research Organization DevFeed: [Amazon is investing in the Lean Focused Research Organization]() Original publisher: [Read original article]() Author: Byron Cook; Shawn Bice Published: 2026-07-26T08:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [AI Bots](), [AI Chat]() Tags: [agents](), [ai-agents](), [amazon](), [amazon-aurora](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-web-services-aws](), [automated-reasoning](), [aws](), [developer](), [formal-verification](), [generative-ai](), [large-language-models-llms](), [llm](), [programming](), [programming-language](), [research](), [responsible-ai](), [safety](), [software-testing](), [testing](), [trustworthy-ai]() ### AI overview Amazon is making a long-term investment in the Lean Focused Research Organization to advance Lean, a programming language for mathematical correctness proofs. The article highlights Lean-based verification for safer AI agents and AWS systems. ### Source excerpt As AI agents take on higher-stakes decisions, Lean programming language makes it possible to mathematically prove they will behave safely. ## The continuous validation framework for data pipelines. DevFeed: [The continuous validation framework for data pipelines.]() Original publisher: [Read original article]() Author: Niruta Talwekar Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [data](), [DataOps](), [Data Quality](), [data observability](), [CI/CD](), [data-processing](), [DevOps](), [Platform Engineering]() Tags: [ci-cd](), [data](), [data-quality](), [devops](), [platform-engineering](), [provenance](), [software-testing](), [streaming]() ### AI overview The article introduces the Continuous Validation Framework (CVF), an end-to-end methodology for validating data pipelines through architectural isolation, configuration-driven data quality management, and continuous automation based on lineage-driven impact analysis. It reports production results including a 50% reduction in incidents and an 80% improvement in detecting data quality issues. ### Source excerpt A framework for automated, end-to-end data pipeline validation using isolation, declarative quality checks, and lineage-driven impact analysis. ## From Pass/Fail to Confidence Levels: The Evolution of QA in the AI Era DevFeed: [From Pass/Fail to Confidence Levels: The Evolution of QA in the AI Era]() Original publisher: [Read original article]() Author: Gayatri Panganti Published: 2026-07-22T06:16:20Z Content type: article Language: en Sources: [Helpshift]() Topics: [Testing](), [Software Testing](), [AI Agent](), [Large Language Model]() Tags: [agents](), [ai](), [ai-agents](), [ai-ready-qa-mindset](), [beyond-traditional-qa](), [bug](), [configuration](), [consistency](), [qa](), [qa-beyond-pass-or-fail](), [software-testing](), [testing](), [trust]() ### AI overview This article explains how quality assurance for AI features differs from traditional deterministic software testing. It argues that testing should assess behavior, consistency, safety, policy adherence, and trust across varied prompts and contexts rather than relying only on binary pass/fail results. ### Source excerpt For decades, software testing followed a familiar pattern. A feature either worked or it didn't. A button click resulted in an expected outcome. An API returned a predefined response. A workflow either passed or failed. Large Language Models (LLMs) have changed that equation. 🤖 Modern AI agents don't simply execute the instructions- they interpret, reason, generate and adapt. The same prompt may produce different responses across executions while still being technically correct. As a result, Quality Assurance is evolving from validating deterministic outputs to evaluating confidence in AI features and behaviour. That question is no longer : >> Did the feature work? It has become: >> Can we trust how the AI behaves across a wide range of real world situations? Traditional QA Vs AI QA Traditional applications are deterministic. If the same input is provided ten times, the output should be identical ten times. -- -- -- Input -> Business Logic -> Expected Output -- -- -- AI systems behave differently. Same Prompt -> Correct Answer -> Different (but still correct) Answer -> Hallucination -> Policy Violation -> Escalation -> Rejection/Resolution Every one of these outcomes may be observed from the same feature depending upon the context, prompt wording, conversation history, model behaviour or configuration. Accordingly AI bug can be classified as, Prompt Issue / Model Behaviour / Product Bug / Configuration Issue This does not mean the AI feature is broken, it means the testing strategy must evolve. -- -- -- Pass/Fail Is No Longer Enough Traditional QA measures correctness. AI QA measures behaviour. Instead of asking whether a test passed, QA teams increasingly evaluate questions such as, Was the response helpful? Was it aligned with the expected response? Did it follow the configured instructions? Was sensitive information protected? Was the response safe and comply with organisation policies? Did it performed the configured actions correctly? Was the escalation triggered only w ## Introducing computer use in Gemini 3.5 Flash DevFeed: [Introducing computer use in Gemini 3.5 Flash]() Original publisher: [Read original article]() Author: Mateo Quiros Published: 2026-06-24T16:30:01Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [AI Bots](), [Developer Tools]() Tags: [agentic](), [api](), [automation](), [browser](), [computer-use](), [enterprise](), [flash](), [gemini](), [none](), [safety](), [software-testing](), [tool]() ### AI overview Gemini 3.5 Flash adds built-in computer use for building agents that act across browser, mobile, and desktop environments. The release also describes safeguards for sensitive actions and indirect prompt injection. ### Source excerpt A look at the built-in computer use tool in Gemini 3.5 Flash. ## BrowserStack wins 2026 Top Rated Award from TrustRadius DevFeed: [BrowserStack wins 2026 Top Rated Award from TrustRadius]() Original publisher: [Read original article]() Author: BrowserStack Team Published: 2026-06-18T08:37:16Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [browser](), [Automation](), [Accessibility]() Tags: [2026](), [accessibility](), [automation](), [automation-testing](), [browser](), [company](), [customers](), [reviews](), [software-testing](), [testing]() ### AI overview BrowserStack announces that it received the 2026 TrustRadius Top Rated Award, its fourth consecutive win since 2023. The article cites a trScore of 8.4 out of 10 and more than 260 verified reviews, with recognition across accessibility testing, automation testing, cross-browser testing, functional testing, and test management. ### Source excerpt We are so excited to share that authentic, verified feedback from our customers has earned us a 2026 Top Rated Award by TrustRadius! ## Testing and AI Workshop by James Bach of Rapid Software Testing - Notes DevFeed: [Testing and AI Workshop by James Bach of Rapid Software Testing - Notes]() Original publisher: [Read original article]() Author: T.J. Maher (noreply@blogger.com) Published: 2026-06-12T18:10:14Z Content type: opinion Language: en Sources: [T.J. Maher]() Topics: [Testing](), [Software Testing](), [Artificial Intelligence](), [ChatGPT]() Tags: [agentic](), [ai](), [chatgpt](), [james-bach](), [notes](), [rapid-software-testing](), [rst](), [software-testing](), [testing]() ### AI overview A personal account of James Bach's Testing and AI Workshop, describing its AI-assisted testing demonstrations, hands-on exercise, and review session. The author enjoyed the class but had limited time to complete the practical exercise because of family responsibilities. ### Source excerpt Last week, I saw that there was a new half day workshop by one of the creators of Rapid Software Testing, James Bach, Testing and AI Workshop, and that they were offering a 50% discount for anyone who was unemployed, so I just had to attend. Visit James Bach's Satisfice.com Download Page to download PDFs on topics such as Responsible Work (May 2026), AI Writing Policy (April 2026), Heuristic Test Strategy Model (December 2024), Rapid Software Testing Explored Class Appendices (December 2024) Why Testers? (Oct 2024) and ChatGPT Sucks at Being a Testing Expert (August 2023) YouTube Channel: Rapid Software Testing Check out James Bach's Classes at https://www.satisfice.com/classes (50% discount if unemployed) The next Testing and AI Workshop will be July 6, 2026 "In each session, the instructors will first perform a live 'testopsy.' This is a demonstration of AI-assisted testing (using both assistive and agentic modes of AI) on a real product, accompanied by an analysis and explanation of what happened during the demo. During this part of the workshop, you may ask questions or offer critique. "Next the instructors will challenge you to perform a similar process or solve a similar problem with the help of AI. You will have two hours. You will be able to work alone or in groups, as you like. "Finally, the instructors will review and critique your work, if you choose to share it. At the end of the event, you will get to keep the videos". I really enjoyed the class! It involved an hour-and-a-half webinar, where James Bach walks people though how he uses AI, a few hours where you can work on your own project for the course, then another hour-and-a-half webinar where course attendees could review what they came up with to analyze a site. My only problem with the course was that my rambunctious seven year old was home sick, and I wasn't able to dedicate a few hours to come up with how to use AI to test a website, since I was busy making lunch and chatting with my kiddo. What ## A new era for software testing DevFeed: [A new era for software testing]() Original publisher: [Read original article]() Published: 2026-06-07T09:46:06Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Testing](), [Software Testing](), [Integration testing](), [AI Agent](), [Large Language Model]() Tags: [ai-agent](), [integration-testing](), [llms](), [markdown](), [software-testing](), [testing]() ### AI overview The article argues that large language models can extend conventional software QA and testing by having an AI agent perform manual checks described in a Markdown file. It contrasts locally scoped tests, integration tests, and manual QA passes, and gives DwarfStar, an inference engine for open-weight LLMs, as an example. ### Source excerpt Automatic programming dramatically speeds up writing software in certain use cases and in the right hands. In my experience the output does not reach the structural quality and economy of complexity of the best hand-written software. However, not all the software is stellar, and my feeling is that automatic programming surpasses most of the times (and if well managed) the quality of decently developed hand-written code. Yet, there is a tradeoff between quality and time, in the case of writing new software with AI. This tradeoff in certain projects I developed can be brutal, that is, completing projects that may take many months in a few weeks. However, there are domains where LLMs simply open new strictly more powerful ways to automate processes, without any compromise on quality. One of those domains is software QA and testing. Traditionally software is tested using test suites that are composed of locally-scoped tests and integration tests (think of Redis: one thing is testing if SET foo 10 will be matched by GET foo => 10, another thing is testing if replication works in this case). And then by QA passes that are usually manually executed, and that can capture holes in the runnable test suite. It is a known fact that covering all the lines of the code does not mean covering all the possible states. Moreover integration testing is structurally hard: there are a number of timing issues, setups, and certain quality outputs that can only be visually inspected and not automatically checked that leave a lot of testing opportunities not really exploited because of time or logistic constraints. LLMs offer a new way to do QA on top of the existing testing methodologies. The idea is to create a markdown file where an AI agent is asked to work as a QA engineer, performing a number of manual testings on the new release. For instance, in the case of DwarfStar (an inference engine for open weights LLMs) I use the following approach. In the markdown file, the agent is asked to ## New Position Unlocked: Senior SDET at AAA Life Insurance, starting Monday, June 15th, 2026! DevFeed: [New Position Unlocked: Senior SDET at AAA Life Insurance, starting Monday, June 15th, 2026!]() Original publisher: [Read original article]() Author: T.J. Maher (noreply@blogger.com) Published: 2026-06-04T02:16:39Z Content type: opinion Language: en Sources: [T.J. Maher]() Topics: [Software Testing](), [Mobile]() Tags: [2026](), [aaa-life](), [automation](), [community](), [leadership](), [linkedin](), [mobile](), [programming](), [software-testing](), [sqgne](), [testing]() ### AI overview The author announces starting a Senior SDET position at AAA Life Insurance Company on June 15, 2026, after an extended job search. The article also announces the author's nomination as a volunteer director of the Software Quality Group of New England and describes recent community activities, including a talk on building a mobile automation test framework. ### Source excerpt I have two announcements: I've accepted a job offer as a Senior SDET role at AAA Life Insurance Company, and will be starting Monday, June 15th! And I have been nominated to be one of the volunteer Directors on the leadership board of the Software Quality Group of New England. Man, the job market is brutal! It took me four months of job searching in 2025 to find SELF Id when MassMutual outsourced its technology department. And it took me four months of near constant job searching in 2026 to find AAA Life when I was caught up in the second round of SELF's layoffs in the end of January. After throwing countless resumes into the void, submitting post after post on LinkedIn, experimenting with countless automation frameworks and programming projects on tjmaher.com, and spamming my entire LinkedIn Network (sorry about that!), and everyone I have worked with in the past twenty years as a software tester, desperately looking for job leads, it was a staffing firm working for AAA Life who found me. Thank you, Refactor Talent! Rewriting my LinkedIn Resume to make sure that I was using proper keywords in my "About" section of my LinkedIn Profile seems to have done the trick! I'd also like to announce that I found another awesome software testing community to be a part of: The Software Quality Group of New England, a local non-profit meeting monthly in Burlington, MA. Come join us! And we are always looking for in-person speakers. I've been missing being part of a software testing community! I was Meetup Organizer for the now defunct Ministry of Testing - Boston group from 2016 to 2024, loving every minute I was trying to find speakers, sponsors and hosts. I made so many great connections during my tenure. It's sad the Meetup group was shut down by the Ministry of Testing UK a few months ago, but I am glad to be part of a new software testing group! In the past month as a new member of the SQGNE, I have: * Given a talk to the group on building a mobile automation test framework ## Breakpoint 2026: Highlights from Day 1 DevFeed: [Breakpoint 2026: Highlights from Day 1]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-06-01T09:25:38Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Software Testing](), [Testing](), [Artificial Intelligence](), [Test automation](), [Automation](), [API](), [Security](), [prompt injection](), [code productivity](), [Accessibility]() Tags: [2026](), [ai](), [automation](), [breakpoint](), [developer-productivity](), [prompt-injection](), [qa](), [security](), [software-testing](), [testing]() ### AI overview Breakpoint 2026 Day 1 examines how AI-native quality engineering can reduce the traditional trade-off between testing speed and quality. It covers AI-assisted test execution, Copilot-driven automation, intent-based workflows, agentic QA systems, and frameworks for testing AI agents across reasoning, actions, and end-to-end outcomes. ### Source excerpt Explore the biggest announcements, expert insights, and conversations from Breakpoint 2026 Day 1, covering AI in software testing, test automation, developer productivity, accessibility testing, and the future of intelligent testing workflows. ## BrowserStack Newsletter April 2026 DevFeed: [BrowserStack Newsletter April 2026]() Original publisher: [Read original article]() Author: Devanshi Jain Published: 2026-05-26T10:50:54Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Automation](), [Accessibility](), [Flutter](), [React Native](), [WebView](), [Selenium](), [Figma]() Tags: [accessibility](), [ai](), [browser](), [event](), [features](), [flutter](), [newsletter](), [product](), [react-native](), [report](), [software-testing]() ### AI overview BrowserStack's April 2026 newsletter covers Breakpoint 2026 content, a report on AI in software testing, and product updates across App Automate, Percy, Website Scanner, and Accessibility. Updates include expanded framework support, improved CI debugging and visual comparison workflows, broader authentication testing, WCAG 2.2 AA coverage, partial-page scanning, and Safari support. ### Source excerpt Read what's new at BrowserStack in April 2026 - New launches, product features, and more! ## Practicing Playwright: Visual Testing With Playwright DevFeed: [Practicing Playwright: Visual Testing With Playwright]() Original publisher: [Read original article]() Author: T.J. Maher (noreply@blogger.com) Published: 2026-05-21T12:46:54Z Content type: tutorial Language: en Sources: [T.J. Maher]() Topics: [Playwright](), [Testing](), [Docker](), [ci](), [WebKit](), [Chrome](), [Firefox]() Tags: [chrome](), [ci](), [docker](), [firefox](), [playwright](), [snapshots](), [software-testing](), [testing](), [visual-testing]() ### AI overview This tutorial demonstrates Playwright visual testing by capturing screenshots of pages or elements, comparing them with baseline snapshots, and using masking to exclude changing content. It also discusses browser and operating-system differences, repository size, and running visual tests in a standardized CI environment. ### Source excerpt If you want to do some basic visual checking to see if there has been any deviation from your baseline of images, you can use Playwright. It's built in! Playwright can take a snapshot of a web element, a visible viewport, or a full page, and save it in your Git repository as a baseline, failing the test if the image, page, or viewport does not match up. Caution: From what I have been reading, this can quickly cause your code repository to balloon in size, since Chrome, Firefox, and WebKit would each store its own golden screenshot in your repo. Also, images on Mac, Windows, and Linux all appear different pixel-by-pixel. If using a CI platform, it might be best to run visual tests only on a standard Playwright Docker image, to generate and compare snapshots. According to TestQuality, "Once a suite passes 50-100 visual tests, teams need a layer that tracks run history, surfaces flaky-test patterns across cycles, and routes confirmed defects into the team's tracker -- none of which lives inside the test runner itself"... I wonder if you can store images in an Amazon S3 bucket and hook that up as a virtual drive? ... no matter. That will be a blog post for another time... Right now, we will be walking through Butch Mayhew's code he wrote for his LinkedIn Learning course, Learning Playwright, found on his companion GitHub site. While the test is in a certain state you can a screenshot of the page or certain elements of the page and save them as a snapshot. The snapshots an be used as a baseline images to compare your current site against. This baseline can be periodically updated as the site evolves. How does this happen? With Playwright's .toHaveScreenshot( ) to take a screenshot and the mask method that you want to leave out of the comparisons between the expected and actual screenshots. toHaveScreenshot(name): Playwright.dev / Page Assertions Our Test Site: PracticeSoftwareTesting.com Butch has a series of tests for the test site Practice Software Testing ( practicesof ## Designing a custom AI agent for repetitive QA workflows DevFeed: [Designing a custom AI agent for repetitive QA workflows]() Original publisher: [Read original article]() Author: Rimple Sharma Published: 2026-05-18T06:01:01Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Agent](), [Markdown](), [Parser](), [context]() Tags: [ai](), [ai-agent](), [ai-skills](), [context](), [markdown](), [parsing](), [software-testing](), [test-automation]() ### AI overview This tutorial explains how to design a custom AI agent for repetitive QA workflows. It focuses on encoding structured instructions in Markdown, including headers, rules, variables, examples, output schemas, and executable commands, and introduces the agent's processing loop. ### Source excerpt Part 2: Markdown as agent logic and the ReAct loop in action. In Part 1, I walked through the problem, repetitive QA work that resists pure scripting and the three pillars of a useful agent: input validation, context awareness, and a decision engine. I also showed why structured instructions beat plain text. This part is about how that structure gets encoded, how the loop runs, and what the impact looked like in practice. Why agents use Markdown When writing instructions for an agent, Markdown isn't just a formatting choice, it acts as a bridge between human-readable text and machine-processable logic. It's become a common format for agent communication because both humans and machines can work with it efficiently. But what most people miss... When you write agent instructions in Markdown, you're creating a weighted logic map, as well as just writing text. A few patterns that matter: 1. Semantic headers (#, ##) act as anchors for the agent's attention. Wrapping your mandatory rules under a clear header makes it far less likely the agent will overlook them mid-task. 2. Horizontal rules ( -- -) separate identity from instructions. Without this boundary, rules from one section bleed into another and the agent loses track of what governs what. 3. Bold keywords (**NEVER**, **ALWAYS**, **MANDATORY**) tend to be treated as hard constraints. Bold is a hard constraint. Italics are treated as a soft suggestion. 4. Blockquotes (>) are useful for examples. Wrapping an example in a blockquote signals to the agent that this is a reference, not a direct command. 5. {{curly_braces}} mark dynamic variables. They tell the agent that this part of the input will be filled in at runtime -- things like {{TICKET_ID}} or {{DATASET_NAME}} -- so it never substitutes a generic placeholder into a production file. 6. Fenced code blocks define output schemas. If you want the agent to always respond in a specific format, put that format in a code block. It treats it as a contract. 7. Inline code signa [Next page]()