Abstract Empirical studies have shown complexity metrics to be good predictors of testing effort and maintainability in traditional, imperative programming languages. Empirical validation studies have also shown that complexity is a good predictor of initial quality and reliability in object‐oriented (OO) software. To date, one of the most empirically validated OO complexity metrics is the Chidamber and Kemerer Weighted Methods in a Class (WMC). However, there are many more OO complexity metrics whose predictive power has not been as extensively explored. In this study, we explore the predictive ability of several complexity‐related metrics for OO software that have not been heavily validated. We do this by exploring their ability to measure quality in an evolutionary software process, by correlating these metrics to defect data for six versions of Rhino, an open‐source implementation of JavaScript written in Java. Using statistical techniques such as Spearman's correlation, principal component analysis, binary logistic regression models and their respective validations, we show that some lesser known complexity metrics including Michuraet al.'s standard deviation method complexity and Etzkornet al.'s average method complexity are more consistent predictors of OO quality than any variant of the Chidamber and Kemerer WMC metric. We also show that these metrics are useful in identifying fault‐prone classes in software developed using highly iterative or agile software development processes. Copyright © 2008 John Wiley & Sons, Ltd.

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.

Abstract Assessing progress and stability in rapid, highly iterative and agile object‐oriented software development processes is difficult because program evolution appears somewhat disorderly. With agile software development methods, the expected time to reach software stability is obscured by the dynamic nature of the agile process itself. The System Design Instability (SDI) metric was developed to help analyze object‐oriented software evolution under these conditions. In this paper we examine a new version of SDI, called SDIe, based on the maximum entropy in a system. We believe our new measure is easier to calculate than the original version of SDI since it is based on data that are more commonly available than those used by SDI; also, SDIe gives a more accurate indication of software stability and maturity since it suffers less from data spikes. To validate the new metric, we use maintenance data from a commercial software project, developed using an agile process; these are the same data that were collected and analyzed in the original SDI study. In addition, we apply the refined SDI metric to the evolution of a well‐established, open‐source, iteratively‐developed software product. The results of our case study indicate our new SDIe metric is a viable measure of system design stability. Copyright © 2006 John Wiley & Sons, Ltd.