AI-Assisted Software Testing and Bug Prediction

Abstract

The technology of AI has significantly transformed the manner in which software engineering is performed particularly in the field of software testing as well as bug prediction. The conventional software testing focuses on manual testing and autotesting programs based on rules, which is not only time-consuming, but also prone to errors, and could not test large-sized software systems with constant changes. It introduces AI-assisted software testing that is based on machine learning, deep learning, and data-driven algorithms to generate test cases automatically, prioritize the testing process, and predict defects before executing the code in the software. Bug prediction models rely on the historical software measures, code complexity measures, and the execution data to anticipate defect-prone parts at the early phases of the software development lifecycle. AI-assisted software testing techniques, bug prediction techniques, the results of the research, challenges, and implications are critically examined in this research article. The paper places the stress of how AI can be used to enhance the quality of their software, reduce the cost of software development, and provide a continuous integration and delivery platform.