The optimization of software testing efficiency and effectiveness using AI techniques
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P104Keywords:
AI-powered testing, AI-driven quality assurance, AI-based functional testing, AI-enhanced UI/UX testing, AI in mobile and microservices testing, AI-Driven Test, AI-based automation, self-healing scripts, AI in robotic test automation, scriptless test automation, AI-enhanced cloud-native testing, AI for Defect Prediction & Test Execution: AI-powered bug prediction, ML-based failure analysis, AI-driven test execution, dynamic test optimization, autonomous test executionAbstract
By means of software testing, defining a critical point of view of the software development life ensures reliability, security, and performance. Still, traditional testing techniques bring various challenges including high costs, limited time, and the increasingly more complicated modern software systems. Usually not able to support rapid development cycles, manual and rule-based automated testing methods lead to inefficiencies and maybe quality problems. Responding to these problems, artificial intelligence-driven testing approaches have equal ability to increase efficiency and effectiveness both equally. Together deep learning, machine learning, and natural language processing will enable artificial intelligence to maximize test case development, defect identification, and vulnerability prediction. While generative artificial intelligence models provide suitable test data, techniques such as reinforcement learning provide adaptive test execution. Artificial intelligence driven anomaly detection also improves defect prediction, hence reducing the demand for thorough hand-off testing. Applied in software testing, artificial intelligence has many benefits. Reducing human errors helps to drastically lower test times, increase test coverage, and improve test correctness. Constant learning and improvement guaranteed by automated artificial intelligence-based testing assures dependability and resilience to meet changing software environments. Furthermore, proper evaluation of test cases made possible by artificial intelligence optimizes resource allocation and improves general program quality. This book summarizes the primary artificial intelligence techniques for effective and successful software testing. It covers realistic implementations, investigates challenges in artificial intelligence acceptance, and provides comments on additional artificial intelligence led testing developments. The results show that artificial intelligence not only accelerates testing processes but also increases fault detection rates, therefore enabling the development of more homogeneous and high-quality software solutions
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