AI growing relevance in software testing has garnered substantial attention in today's software industry. While incorporating AI and ML approaches into software testing is not required, it is worthwhile to explore and study how these methods may deliver valuable benefits in specific elements of the testing process. With the incorporation of artificial intelligence (AI) techniques software testing will undergo a transformational transition.
Lately, AI and Machine Learning have been the most significant breakthroughs in the software testing space, and they will remain a hot topic for the foreseeable future.
Generative AI is bringing a new era of “convenience” with ChatGPT, from OpenAI, taking center stage in our daily lives. From aiding in solving complex problems to generating content, this large language model has become a helpful companion for various testing-related tasks. As generative AI is becoming increasingly present in our daily lives, we should understand how to use it and account for its limitations.
Exploratory testing is a dynamic, flexible methodology emphasizing simultaneous learning, testing strategy, and execution. Unlike traditional scripted testing, exploratory testing enables testers to actively explore software applications using their intuition, creativity, and experience. By assuming the end-user role, testers interact with the software in real-time, identifying potential issues and uncovering usability problems that scripted tests might overlook.
In part 1of our software testing compliance blog we discussed the overall significance of compliance and how it can be facilitated at the strategic level for several popular universal standards. Now, let’s take a look at how day-to-day operations could be optimized with industry-specific examples of Xray’s applicability.
Every industry has evolved to require high-quality products that are safe for usage according to the applicable rules. Those often appear as international standards that provide a framework for applying the global best practices from operational, ethical, and legal standpoints. Therefore, the primary benefits of aligning with those guidelines are reputational and financial gains.
Artificial Intelligence (AI) is rapidly evolving, and one of the prominent breakthroughs is OpenAI's ChatGPT, which has gained considerable attention. As AI and Large Language Models (LLMs) like GPT-3/4 gain prevalence, various industries are exploring their potential, including software testing. Integrating AI into test management tools seems enticing.
In the fast-paced world of software development, quality assurance, and testing have evolved into critical components of the development process. Navigating this complex landscape has become a challenge for many, especially when deciding which tests to perform, what to automate, and how to ensure the highest quality in software releases. Our recent episode on the QA Therapy Podcast delved deep into these pressing questions, and today, we're excited to bring you the highlights.
Many questions arise whenever adopting exploratory testing (ET), primarily due to misunderstandings about what it means. The fact that exploratory testing is mainly unstructured, especially compared to the traditional scripted testing approach that is highly detailed and restrictive, makes it harder to connect it to the word “plan.” Sometimes, users call it ad hoc. Does all this mean that we can’t prepare ourselves for exploratory testing and establish some level of plan for it?
Success. We all seek it. However, what defines success? It all depends on the goals we have established. We often hear that testing is about ensuring there are no bugs. That's wrong in many ways. First, testing doesn't assure; testing gives information about quality so the team can decide what to do next, hopefully using it to improve its current status.
