Software Metrics Evaluation Based on Entropy

TL;DR

This paper evaluates object-oriented design metrics, especially entropy-based complexity measures, for predicting software fault-proneness and quality, providing empirical validation and proposing a new entropy-based complexity metric.

Contribution

It introduces an entropy-based measure of design complexity and empirically validates existing OO metrics for fault prediction in software classes.

Findings

Chidamber and Kemerer's metrics are effective in fault prediction.

Entropy-based complexity correlates with fault-proneness.

Proposed WMC-CK entropy metric enhances fault prediction accuracy.

Abstract

Software engineering activities in the Industry has come a long way with various improve- ments brought in various stages of the software development life cycle. The complexity of modern software, the commercial constraints and the expectation for high quality products demand the accurate fault prediction based on OO design metrics in the class level in the early stages of software development. The object oriented class metrics are used as quality predictors in the entire OO software development life cycle even when a highly iterative, incremental model or agile software process is employed. Recent research has shown some of the OO design metrics are useful for predicting fault-proneness of classes. In this paper the empirical validation of a set of metrics proposed by Chidamber and Kemerer is performed to assess their ability in predicting the software quality in terms of fault proneness and degradation. We have also proposed the design complexity of object-oriented software with Weighted Methods per Class metric (WMC-CK metric) expressed in terms of Shannon entropy, and error proneness.