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SOFTWARE QA TESTING FOR AI-POWERED APPLICATIONS

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  AI-powered platforms are reshaping how we interact with software, but they also introduce new challenges for quality assurance (QA). Unlike rule-based or deterministic systems, AI features behave probabilistically i.e. they can produce varied, sometimes unexpected outputs even for the same input. This blog explores how to approach testing AI systems and  what makes them different. Traditional QA vs. AI QA While foundational QA practices like validating data flows, checking UI behavior, and reporting bugs still apply, AI systems add an extra layer of complexity. The testing approach depends on the type of AI system being used: Generative AI (such as chatbots, language models): These systems generate or create new content (text, voice, images) based on probabilistic models. They don’t follow fixed rules and may return varied outputs for the same input. QA focus : hallucination, tone, misinformation, safety, escalation, bias, prompt injection. Non-generative AI (such as recomm...
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Test Case Design Techniques

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  What is are Test Case Design Techniques? Test case design techniques help create effective tests to uncover bugs in software based on defined processes and logic. They are the key to planning, designing, and implementing tests for software applications. 1. Specification-based (Black-Box) Techniques Equivalence Partitioning (EP) Divide inputs into classes where behavior is expected to be the same(valid and invalid). Test one representative value from each class to reduce redundancy.  Example: For an age field accepting 18–60: Valid class: 18–60 (e.g., test with 35) Invalid classes: below 18 (e.g., 17), above 60 (e.g., 61), non-numeric (e.g., "abc") Boundary Value Analysis (BVA) Focus on values at or around the edges of each valid range .  Example: For ages 18–60, test: 17, 18, 19, 59, 60, 61. Decision Table Testing Use tables to map combinations of inputs to expected outcomes i.e.. involves designing test cases based on decision tables formulated ...
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ARTIFICIAL INTELLIGENCE IN SOFTWARE TESTING

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In todays age, Artificial Intelligence (AI) and Machine Learning (ML) are the brains behind everything from your fitness tracker recommending workout session to Netflix suggesting the next show to binge watch. Beside these common cases, AI and ML are also working behind the scenes to completely reshape how we test software. Traditional automation testing was time-consuming and less adaptive to dynamic applications as it heavily relies on predefined scripts and manual intervention. Nonetheless, its reported that with AI-powered test automation, it could improve the reliability of tests (33%) and reduce the number of defects (29%) as stated in the World Quality report. THE QUESTION IS HOW??? ⚙️ From Repetitive Scripts to Intelligent Automation Gone are the days of writing rigid, rule-based test scripts that break when someone changes a button color. With AI-powered testing tools, testing is now: Faster : AI auto-generates and maintains test scripts. Smarter : ML helps prioritize which ...
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