Machine learning approach for software defect prediction using multi-core parallel computing

Parashar, Anshu; Goyal, Raman Kumar; Kaushal, Sakshi; Sahana, Sudip Kumar

doi:10.1007/s10515-022-00340-2

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…The following are some state-of-the-art studies related to this issue. In [35], the authors proposed a new, multi-core parallel-processing random forest approach for software defect prediction (SDP). They evaluated their approach on 11 software systems from NASA/PROMISE and other relevant repositories and compared it to various state-of-the-art machine learning models.…”

Section: Related Workmentioning

confidence: 99%

Predicting Software Defects in Hybrid MPI and OpenMP Parallel Programs Using Machine Learning

Althiban,

Alharbi,

Al Khuzayem

et al. 2023

Electronics

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High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems.

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Section: Related Workmentioning

confidence: 99%