Qazi Mazhar ul Haq¹, Fahim Arif^2,3, Khursheed Aurangzeb⁴, Noor ul Ain³, Javed Ali Khan⁵, Saddaf Rubab⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172 - 26 March 2024

Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature… More >

C. Anjali^*, Julia Punitha Malar Dhas, J. Amar Pratap Singh

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1241-1256, 2023, DOI:10.32604/iasc.2023.029678 - 05 January 2023

Abstract The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is devoid of bugs. One of the most time-consuming and costly software procedures is finding and fixing bugs. Although it is impossible to eradicate all bugs, it is feasible to reduce the number of bugs and their negative effects. To broaden the scope of bug prediction techniques and increase software quality, numerous causes of software problems must be identified, and successful bug prediction models must be implemented. This study employs a hybrid… More >

M. Sangeetha^1,*, S. Malathi²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1587-1601, 2022, DOI:10.32604/iasc.2022.025192 - 25 May 2022

Abstract Software testing is an effective means of verifying software stability and trustworthiness. It is essential in the software development process and needs a huge quantity of resources such as labor, money, and time. Automated software testing can be used to save manual work, shorten testing times, and improve testing performance. Recently, Software Bug Prediction (SBP) models have been developed to improve the software quality assurance (SQA) process through the prediction of bug parts. Advanced deep learning (DL) models can be used to classify faults in software parts. Because hyperparameters have a significant impact on the… More >