Software Defect Prediction Using Clustering: A Comprehensive Literature Review
DOI:
https://doi.org/10.54489/ijcim.v3i2.283Keywords:
Software Defect Prediction, Clustering, Software EngineeringAbstract
Anticipating software defects prior to the testing phase proves advantageous for efficient resource allocation to develop the high-quality software, a necessity for any organization. Machine learning (ML) methodologies play a pivotal role in addressing these issues, leading to the creation of numerous predictive models designed to categorize software modules as either defective or non-defective. Several obstacles hinder the analysis of software data that is defected, encompassing issues like redundancy, correlation, irrelevant features, missing data points, and an unbalance distribution between faulty and non-faulty classes. Both supervised and unsupervised machine learning techniques have garnered global attention from practitioners and researchers as viable approaches to tackle these challenges, yielding noticeable enhancements in defect prediction accuracy. This review paper examines clustering unsupervised machine learning technique developed for software defect prediction spanning the years 2017 to 2023 and covered the 15 researches.
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