Entropy Churn Metrics for Fault Prediction in Software Systems
Arvinder Kaur1, Deepti Chopra1
1University School of Information and Communication Technology (U.S.I.C.T), Guru Gobind Singh Indraprastha University, New Delhi 110087, India.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces Entropy Churn Metrics (ECM) for improved fault prediction in software engineering. ECM, based on History Complexity Metrics (HCM), shows comparable performance to HCM across diverse software projects.
Area of Science:
- Software Engineering
- Computer Science
- Software Quality Assurance
Background:
- Fault prediction is crucial for efficient software development and maintenance.
- Existing methods aim to reduce fault resolution time and effort.
- Novel approaches are continuously sought to enhance fault prediction accuracy.
Purpose of the Study:
- To propose Entropy Churn Metrics (ECM) as a new approach for fault prediction.
- To evaluate the performance of ECM against History Complexity Metrics (HCM).
- To analyze factors influencing the preference between ECM and HCM.
Main Methods:
- Developed Entropy Churn Metrics (ECM) based on History Complexity Metrics (HCM) and Churn of Source Code Metrics (CHU).
- Compared the performance of ECM and HCM across 14 subsystems from 5 diverse software projects (Android, Eclipse, Apache Http Server, Eclipse CDT, Mozilla Firefox).
- Analyzed fault distribution, subsystem size, and programming language to understand metric preference.
Main Results:
- ECM demonstrates comparable performance to HCM in fault prediction.
- The analysis identified characteristics of software systems that favor one metric over the other.
- Performance variations were observed across different software projects and subsystems.
Conclusions:
- Entropy Churn Metrics (ECM) offer a viable alternative for fault prediction.
- The choice between ECM and HCM may depend on specific software project characteristics.
- Further research can refine these metrics for more accurate fault prediction.
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