Systems | Development | Analytics | API | Testing

Let’s start with some absolutes. Maintaining a steady CI/CD pipeline is crucial to modern software development. Testing and quality engineering principles that drive solid deliverables should be flexible and agile. QA teams need hundreds of device and O/S combinations. With those non-negotiables in mind, how would you implement a flexible device architecture to support ongoing and evolving needs to assess mobile and web applications?

Welcome to the world of test automation tools, where efficiency, accuracy, and enhanced test coverage are just a click away. No, really. Automation testing tools offer numerous advantages, including time and cost savings, increased test coverage, improved accuracy, scalability, and the ability to handle complex testing scenarios. By utilizing automation testing tools effectively, organizations can enhance the quality and reliability of their software applications while optimizing their testing efforts.

One of the most critical pieces to delivering on users’ expectations is understanding their experience and extensively testing the entire lifecycle of their journey, from initial awareness, through the purchase process and ongoing use, to the end of their relationship with your company. Each interaction is an opportunity to test UX quality and brand consistency.

In the past year, generative AI has gone well beyond basics. Experiments with GenAI, such as ChatGPT4, can solve technical problems using mental models and complex systems consisting of specific rules, context, and toolsets. This is not your mother’s chatbot; tech companies that immediately integrated generative AI tools into their workflow report fast and almost universal performance gains.

If regular scripted software testing follows clear directions on a map through your project, then exploratory testing starts with a compass and a blank map. There are software bugs that can’t be discovered by following a map: skilled explorers must create their unique map. Every project can benefit from having exploratory testing seeking out the hidden bugs lying off the beaten path.

Layoffs, budget cuts, market shifts, outsourcing halts. Times are tight in engineering departments across the globe. While crowdsourcing can mitigate many of those issues, there’s a glaring QA challenge staring down the last two quarters of 2023: a talent shortage. Let me be clear: lack of qualified talent. There are enough bodies, enough devices, and O/S combinations to hobble together a crowdsourced testing team that can check the boxes.

Generative AI in software testing is an advanced approach that augments human testers to make the testing process faster and more efficient while improving the quality of the software test results. At Testlio, we use artificial intelligence in software testing to assist expert QA testing managers in new test creation, faster test case refactoring, and creating issue reports with significantly fewer errors. In essence, generative AI helps enhance human performance.

Regression testing is a critical aspect of software testing that ensures the reliability and stability of software systems after changes have been made. As software systems are updated, and new bugs are created, previously functioning features may stop working as intended. If you ask, “What is regression testing” or “How do I use it in my QA strategy,” let’s start here.

May 25, 2023, Austin, TX — Testlio, a leader in technology-powered software testing services, announced today that for 30 days across April and May 2023, it ran two experiments using generative AI technologies. These experiments signaled that quality assurance can be better and faster using a combination of expert quality professionals and AI-powered machines. With these findings, Testlio has embraced commercial-grade generative AI capabilities via its software testing platform and services.