@article {10.3844/jcssp.2020.1709.1717,
article_type = {journal},
title = {Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction},
author = {Fadhil, Julanar Ahmed and Wei, Koh Tieng and Na, Kew Si},
volume = {16},
number = {12},
year = {2020},
month = {Dec},
pages = {1709-1717},
doi = {10.3844/jcssp.2020.1709.1717},
url = {https://thescipub.com/abstract/jcssp.2020.1709.1717},
abstract = {The need for speed and quality in delivering all software engineering artifacts has inevitably remained the biggest challenge in today’s software development environment. While everyone caters to complex software engineering processes, new releases are expected by the market on almost a daily basis. Thus, several Artificial Intelligence (AI) techniques have been introduced that are intensively used in the modern software engineering industry to fulfill market needs. This paper presents the initial results of our review work on software bug detection and prediction studies using AI techniques. Our focus is to (i) identify factors affecting the effectiveness of current software bug detection and prediction techniques and (ii) identify the effectiveness of AI techniques in improving current software bug detection and prediction techniques. The evidence showed that the software engineering domain has utilized artificial intelligence approaches and techniques to facilitate the complex tasks of software bug detection and bug prediction. It mainly demonstrates the significance of merging artificial intelligence with the software engineering domain in terms of reduced overhead and efficient results to enhance the quality of software products.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}