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

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,...

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Bibliographic Details
Published in:International journal of performability engineering 2022-11, Vol.18 (11), p.798
Main Authors: Rao, K Eswara, Rao, G Appa, Rao, P Sankara
Format: Article
Language:English
Subjects:
Computer programming
Defects
Machine learning
Performance measurement
Quality assurance
Reliability aspects
Software
Software development
Software reliability
Source code
Online Access:Get full text
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Summary: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.
ISSN:0973-1318
DOI:10.23940/ijpe.22.11.p5.798807