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Entropy 2018, 20(12), 963; https://doi.org/10.3390/e20120963

Entropy Churn Metrics for Fault Prediction in Software Systems

University School of Information and Communication Technology (U.S.I.C.T), Guru Gobind Singh Indraprastha University, New Delhi 110087, India
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Received: 5 November 2018 / Revised: 11 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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Abstract

Fault prediction is an important research area that aids software development and the maintenance process. It is a field that has been continuously improving its approaches in order to reduce the fault resolution time and effort. With an aim to contribute towards building new approaches for fault prediction, this paper proposes Entropy Churn Metrics (ECM) based on History Complexity Metrics (HCM) and Churn of Source Code Metrics (CHU). The study also compares performance of ECM with that of HCM. The performance of both these metrics is compared for 14 subsystems of 5different software projects: Android, Eclipse, Apache Http Server, Eclipse C/C++ Development Tooling (CDT), and Mozilla Firefox. The study also analyses the software subsystems on three parameters: (i) distribution of faults, (ii) subsystem size, and (iii) programming language, to determine which characteristics of software systems make HCM or ECM more preferred over others. View Full-Text
Keywords: fault prediction; entropy; mining software repositories; software metrics fault prediction; entropy; mining software repositories; software metrics
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Kaur, A.; Chopra, D. Entropy Churn Metrics for Fault Prediction in Software Systems. Entropy 2018, 20, 963.

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