Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes

TLDR

Empirical validation of software metrics suites to predict fault proneness in object‑oriented components is essential, as some CK metrics have proven effective while the other two suites lack extensive validation. The study empirically validates the CK, MOOD, and QMOOD metrics suites for predicting fault‑proneness in object‑oriented classes. The authors evaluate the suites by applying multivariate binary logistic regression to defect data from six versions of the Rhino JavaScript engine. The results show that CK and QMOOD metrics effectively predict fault‑prone classes, whereas MOOD metrics do not, and the logistic regression models appear useful for assessing quality in classes produced by modern highly iterative or agile development processes.

Abstract

Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.