# qa engineer Published articles for qa engineer. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From Bug Report to Production: How I Fix Bugs with AI DevFeed: [From Bug Report to Production: How I Fix Bugs with AI]() Original publisher: [Read original article]() Author: inDrive.Tech Published: 2026-11-01T21:40:15Z Content type: article Language: en Sources: [HackerNoon]() Topics: [bugfixes](), [Monitoring](), [Static code analysis](), [engineering-culture](), [Risk]() Tags: [agent](), [ai](), [ai-agent](), [ai-coding-agents](), [api-testing](), [bugs](), [claude](), [clusters](), [code](), [code-review](), [good-company](), [hackernoon-top-story](), [indrive](), [logs](), [production](), [production-monitoring](), [qa](), [qa-engineer](), [qa-engineering](), [quality-assurance](), [ship](), [software-testing]() ### AI overview The article describes a three-level workflow in which a QA engineer uses Claude to investigate code, verify hypotheses with production logs and metrics, then prepare and ship small fixes through normal review and release checks. Bug reports include a proposed fix, regression criteria, risks, user impact, and code references, with confirmed findings distinguished from hypotheses. Production analysis can reveal misleading error counts: one week of data across 13 clusters showed about 1.86 million 500 errors, roughly 96.6% tied to client cancellations; the proposed handling change was an estimate, not a deployed result. A shipped idempotency-key fix validated requests before reserving keys and released them after failures; duplicate-key errors fell about 30%, with no noticeable response-time or other-error change and an assessed cache load of about 0.01%. The engineer retains responsibility for evidence, scope, tests, review, release, and post-release checks. ### Source excerpt How a QA engineer at inDrive uses AI to investigate bugs, analyze production logs, write fixes, and ship changes to production. ## Android Journey Tests with Gemini: CI Setup & 11-Week Review DevFeed: [Android Journey Tests with Gemini: CI Setup & 11-Week Review]() Original publisher: [Read original article]() Author: Swapnil Gupta Published: 2026-06-19T03:27:13Z Content type: opinion Language: en Sources: [REA Group]() Topics: [Android](), [Artificial Intelligence](), [ui-testing](), [CI/CD](), [Android Studio](), [adb]() Tags: [adb](), [android](), [android-studio](), [ci](), [firebase](), [gemini](), [qa-engineer](), [quality](), [tech](), [tests]() ### AI overview An 11-week experiment evaluated Android Journeys powered by Gemini for AI-driven UI testing in REA Group's production Android app and CI infrastructure. The tests generally remained resilient through UI changes, but CI setup took eight weeks, Firebase Test Lab was unsupported, and manual QA hours did not decrease. The team rated the experience 8.75 out of 10 and voted to continue. ### Source excerpt Writing AI-powered UI tests took minutes. Getting them on CI took eight weeks. Here's what we learned after 11 weeks with Android Journeys and Gemini. ## A new era for software testing DevFeed: [A new era for software testing]() Original publisher: [Read original article]() Author: Antirez Published: 2026-06-07T09:46:06Z Content type: article Language: en Sources: [Antirez]() Topics: [Test automation](), [Software Testing](), [Integration testing]() Tags: [agent](), [ai](), [ai-agent](), [automate](), [complexity](), [covering](), [domains](), [engine](), [inference](), [integration](), [integration-testing](), [llms](), [markdown](), [qa](), [qa-engineer](), [quality](), [redis](), [software](), [software-testing](), [testing]() ### AI overview The author proposes using an AI agent for manual-style QA passes alongside test suites, especially where integration conditions, timing, visual checks, or setup demands leave gaps. A Markdown checklist can have the agent inspect commits since the last release, identify affected areas, then run targeted checks--for example, distributed inference across two Macs with available GGUF files and speed regression checks. The author says speed can be judged against the previous release without a fixed expected benchmark, since performance changes with optimizations. Another example asks the agent to build a Redis Arrays application, configure replication and persistence, and simulate prolonged multi-user use. The workflow can also flag features that seem surprising, poorly documented, or rough from a user's perspective. The author presents this as an addition to existing testing methods that could raise release quality, while acknowledging that fast AI-generated code may trade structural quality for development speed. ### 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 ## Senior QA Engineer DevFeed: [Senior QA Engineer]() Original publisher: [Read original article]() Author: upday dev (dev@upday.com) Published: 2017-09-06T04:00:55Z Content type: article Language: en Sources: [Upday]() Topics: [Software Testing](), [Mobile](), [incident management](), [Service-Level Agreements](), [App]() Tags: [app](), [bug](), [incident-management](), [mobile](), [qa](), [qa-engineer](), [service-level-agreements](), [testing]() ### AI overview A Senior QA Engineer role focused on improving quality processes for mobile apps. Responsibilities include maintaining QA, bug and incident-management processes, overseeing automated tests, coordinating with developers and stakeholders, and analyzing production problems. ### Source excerpt Your Profile Process-minded problem solvers with a track record of creating environments that help people work effectively Skilled at identifying quality weaknesses in mobile apps, suggesting solutions and driving change Constantly looking for ways to improve quality and testing approach Care deeply about quality and has great attention to detail An excellent communicator used to juggle multiple teams with varying agendas, and to coordinate and execute under pressure Prioritize regularly and work iteratively to maximize positive organizational impact You have demonstrable experience of working on critical, highly visible projects and thriving even when given a short timeline Your Job Set up QA processes to pay close attention to flag issues in an efficient manner Be an overall quality leader and constantly seek opportunities to improve quality and testing approach across the organisations Create and maintain internal service level agreements with stakeholders to ensure new initiatives are flagged with the QA team on time Define stakeholder relationships and cultivate strong networks across Content, Tech, Product and Design to make sure everyone is on board Work closely with developers within various tech team to cover all angles Ensure bug and incident management processes are functional and continuously maintained Report bugs and doggedly pursue through the full cycle to effectively reach the best possible solution. Checkout and build the app and have an overview of the automated test suite Collect learning's and analyse problems in production to improve our proactive process, thus decreasing the amount of unexpected issues Increase transparency by capturing all known sub-optimal issues in a clear & simple format. When possible, follow up with each team and track improvements to ensure the problem has been amended in time for next applicable content release What We Offer A position that offers a great deal of responsibility and impact The chance to collaborate with ## IO.People Classification DevFeed: [IO.People Classification]() Original publisher: [Read original article]() Published: 2016-11-08T15:41:57Z Content type: article Language: en Sources: [Michael Dubakov]() Topics: [Learning]() Tags: [all](), [bench](), [books](), [classification](), [communication](), [conferences](), [courses](), [doing](), [examples](), [fail](), [filter](), [information](), [io](), [lazy](), [learn](), [learning](), [people](), [qa-engineer](), [videos](), [youtube]() ### AI overview The post groups people by how they turn information into work: some avoid new learning or absorb knowledge without applying it, while others learn through examples, need prompting to produce, focus narrowly on job-related material, or share broad learning with limited hands-on work. It describes a balanced type as combining theory and practice, filtering distractions, and contributing in several ways, while warning that enthusiasm can lead to burnout. Each category is linked to a purported workplace fit, such as middle manager, QA engineer, bench, developer, mentor, or lead. These are the author's informal characterizations, not evidence-based assessments; the supplied excerpt does not include the complete classification. ### Source excerpt This post is an attempt to create a classification of how people consume information and how it affects their work. Void: No input > No output This person is a master of ignorance. He doesn't read books that contain new knowledge, he doesn't visit conferences, doesn't watch videos, doesn't do online courses. He stops learning right after graduation with a strong belief that he learnt everything he needs (and probably he's right). If he reads something -- it's a fiction. It he watches something -- it's a TV-show or a blockbuster. If he visits YouTube channel -- it's Fail Army. All incoming information validated for a single purpose -- entertain. Validation filter becomes very advanced with time and around forties there is close to zero chance to let new information in. This type of person doesn't do much. His work is usually unchallenging and somewhat repetitive. He likes huge corporations where political games and dark corners of ancient divisions allow to survive and even move up the ladder sometimes. Hot spot: Middle manager. Gut: Some input > Action output Learn almost nothing, but somehow do things quite OK. Has supernatural ability to absorb information from the world via osmotic communication. He learns by doing and hates theoretical discussions. However, he do like examples and can somewhat generalize based on them. Hot spot: Middle QA engineer. Abyss: Input > No output This is a weird personality. He reads about everything and knows quite a lot. Unfortunately, all the knowledge carefully kept inside. He is quite lazy and doesn't like challenging tasks. The information inside puts pressure on his mind. He always unhappy about everything and usually grumpy. Life sucks. Existential crisis is real. Hot spot: Bench. Sponge: Input > .Squeeze > Output Absorbs information like a sponge. In some special mood can be quite productive, but he should be squeezed to shine. Direct orders, gentle pushes, flattery and all other manipulations are acceptable to help Sponge overcom