State-Of-The-Art In Empirical Validation Of Software Metrics For Fault Proneness Prediction: Systematic Review

TL;DR

This paper systematically reviews empirical studies on software metrics, especially CK and size metrics, that predict fault proneness in object-oriented software, highlighting key metrics related to faults.

Contribution

It compiles and analyzes the current state-of-the-art empirical validation studies on fault prediction metrics for object-oriented software.

Findings

Complexity, coupling, and size metrics are strongly related to fault proneness.

29 empirical studies on fault prediction metrics were identified.

CK and size metrics are validated as effective predictors of faults.

Abstract

With the sharp rise in software dependability and failure cost, high quality has been in great demand. However, guaranteeing high quality in software systems which have grown in size and complexity coupled with the constraints imposed on their development has become increasingly difficult, time and resource consuming activity. Consequently, it becomes inevitable to deliver software that have no serious faults. In this case, object-oriented (OO) products being the de facto standard of software development with their unique features could have some faults that are hard to find or pinpoint the impacts of changes. The earlier faults are identified, found and fixed, the lesser the costs and the higher the quality. To assess product quality, software metrics are used. Many OO metrics have been proposed and developed. Furthermore, many empirical studies have validated metrics and class fault proneness (FP) relationship. The challenge is which metrics are related to class FP and what activities are performed. Therefore, this study bring together the state-of-the-art in fault prediction of FP that utilizes CK and size metrics. We conducted a systematic literature review over relevant published empirical validation articles. The results obtained are analysed and presented. It indicates that 29 relevant empirical studies exist and measures such as complexity, coupling and size were found to be strongly related to FP.