Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect

TL;DR

This paper reviews the significant impact of AI on software testing, highlighting its potential to automate bug detection, address current challenges, and shape future developments in the field.

Contribution

It provides a comprehensive analysis of AI's role in software testing, identifying challenges and proposing future directions for AI integration.

Findings

AI enhances automation in software testing processes.

Challenges include integration with existing pipelines and accuracy issues.

Future prospects involve advanced AI techniques for improved testing efficiency.

Abstract

Artificial Intelligence (AI) is making a significant impact in multiple areas like medical, military, industrial, domestic, law, arts as AI is capable to perform several roles such as managing smart factories, driving autonomous vehicles, creating accurate weather forecasts, detecting cancer and personal assistants, etc. Software testing is the process of putting the software to test for some abnormal behaviour of the software. Software testing is a tedious, laborious and most time-consuming process. Automation tools have been developed that help to automate some activities of the testing process to enhance quality and timely delivery. Over time with the inclusion of continuous integration and continuous delivery (CI/CD) pipeline, automation tools are becoming less effective. The testing community is turning to AI to fill the gap as AI is able to check the code for bugs and errors without any human intervention and in a much faster way than humans. In this study, we aim to recognize the impact of AI technologies on various software testing activities or facets in the STLC. Further, the study aims to recognize and explain some of the biggest challenges software testers face while applying AI to testing. The paper also proposes some key contributions of AI in the future to the domain of software testing.