AI in Software Testing: Revolutionizing Quality Assurance

Artificial intelligence (AI) is an area of tremendous potential, especially in the software testing domain, where it has changed the dynamics of the process, storms in efficiency, accuracy, and flexibility in a given SDLC. This paper presents findings from recent investigations of AI in the testing and quality assurance focusing on its transformational potential. Particular attention is paid to such issues as automation of testing processes through AI, testing process enhancement, and possible changes in software engineering due to AI implementation. In this paper, various research perspectives have been integrated to reveal the effectiveness of AI in enhancing the perceived quality assurance processes, improving product quality, and adopting principles of agile methodology in today's software development.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/telfor63250.2024.10819179
Citations
2
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.69
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article

AI in Software Testing: Revolutionizing Quality Assurance

Ivan Chorbev, Boro Jakimovski, Petre Lameski, Andrea Trifunova
2 citations
Anomaly Detection Techniques and Applications
0.69
article

AI in Software Testing: Revolutionizing Quality Assurance

Ivan Chorbev, Boro Jakimovski, Petre Lameski, Andrea Trifunova
article en
2 citations

Abstract

Artificial intelligence (AI) is an area of tremendous potential, especially in the software testing domain, where it has changed the dynamics of the process, storms in efficiency, accuracy, and flexibility in a given SDLC. This paper presents findings from recent investigations of AI in the testing and quality assurance focusing on its transformational potential. Particular attention is paid to such issues as automation of testing processes through AI, testing process enhancement, and possible changes in software engineering due to AI implementation. In this paper, various research perspectives have been integrated to reveal the effectiveness of AI in enhancing the perceived quality assurance processes, improving product quality, and adopting principles of agile methodology in today's software development.

Ss. Cyril and Methodius University in Skopje (MK)
Openalex Percentile: Top 22%
Anomaly Detection Techniques and Applications
0.69
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AI in Software Testing: Revolutionizing Quality Assurance — Ivan Chorbev, Boro Jakimovski, et al. · (2024) | TGRS Research Map | TGRS