Artificial Intelligence in Software Testing: A Systematic Review

Abstract

Software testing is a crucial component of software development. With the increasing complexity of software systems, traditional manual testing methods are becoming less feasible. Artificial Intelligence (AI) has emerged as a promising approach to software testing in recent years. This systematic review study aims to provide the recent trend and the current state of software testing using AI. This study examines different types of approaches, techniques, and tools used in this area and assesses their effectiveness. The selected articles for this study have been extracted from different research databases using a search string. Initially, 90 articles were extracted from different research libraries. After gradual filtering in three different phases, 20 articles were selected for final review. Around 50 articles were studied to explore the use of AI in software testing and get an in-depth overview of it. The findings of this study suggest that various testing tasks can be automated successfully using AI, including Machine Learning (ML) and Deep Learning (DL), such as Test Case Generation, Defect Prediction, Test Case Prioritization, Metamorphic Testing, Android Testing, Test Case Validation, and White Box Testing. This study concludes that the integration of AI in software testing is simplifying software testing activities while improving overall performance. This study offers a comprehensive analysis of the utilization of AI techniques in different software testing activities.