Model Analytics for Defect Prediction Based on Design-level Metrics and Sampling Techniques

A. Kaya, A.S. Keceli, B. Tekinerdogan, C. Catal

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Predicting software defects in the early stages of the software development life cycle, such as the design and requirement analysis phase, provides significant economic advantages for software companies. Model analytics for defect prediction lets quality assurance groups build prediction models earlier and predict the defect-prone components before the testing phase for in-depth testing. In this study, we demonstrate that Machine Learning-based defect prediction models using design-level metrics in conjunction with data sampling techniques are effective in finding software defects. We show that design-level attributes have a strong correlation with the probability of defects and the SMOTE data sampling approach improves the performance of prediction models. When design-level metrics are applied, the Adaboost ensemble method provides the best performance to detect the minority class samples.
Original languageEnglish
Title of host publicationModel Management and Analytics for Large Scale Systems
EditorsB. Tekinerdogan, Ö. Babur, L. Cleophas, M. van den Brand, M. Aksit
PublisherElsevier Academic Press
Chapter6
Pages125-139
ISBN (Electronic)9780128166505
ISBN (Print)9780128166499
DOIs
Publication statusPublished - 17 Jul 2019

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