A Weighted Ada-Boosting Approach for Software Defect Prediction using Characterized Code Features Associated with Software Quality

doi:10.23940/ijpe.22.11.p5.798807

Abstract

Abstract: Software defect prediction is a major concern for estimating many factors of software products such as reliability maintenance, estimating the cost, and quality assurance. Under different circumstances/phases, the defects can be expected before scheduling each stage of software development. However, most of the software products are being developed by individuals, which leads to unwanted types of defects in different scenarios. Software structural quality refers to analyzing the source code, its inner structure, and compliance with the functional requirements. In this work, an Adaptive Boosting Meta-estimator has been proposed for software defect prediction using characterize code features associated with software quality. The proposed method has been tested with various performance metrics and compared with existing machine learning-based methods to prove its superiority.

Key words: software defect prediction, software engineering, ensemble learning, adaptive boosting

K. Eswara Rao, G. Appa Rao, and P. Sankara Rao. A Weighted Ada-Boosting Approach for Software Defect Prediction using Characterized Code Features Associated with Software Quality [J]. Int J Performability Eng, 2022, 18(11): 798-807.

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