An empirical analysis of package-modularization metrics: Implications for software fault-proneness

ContextIn a large object-oriented software system, packages play the role of modules which group related classes together to provide well-identified services to the rest of the system. In this context, it is widely believed that modularization has a large influence on the quality of packages. Recently, Sarkar, Kak, and Rama proposed a set of new metrics to characterize the modularization quality of packages from important perspectives such as inter-module call traffic, state access violations, fragile base-class design, programming to interface, and plugin pollution. These package-modularization metrics are quite different from traditional package-level metrics, which measure software quality mainly from size, extensibility, responsibility, independence, abstractness, and instability perspectives. As such, it is expected that these package-modularization metrics should be useful predictors for fault-proneness. However, little is currently known on their actual usefulness for fault-proneness prediction, especially compared with traditional package-level metrics.ObjectiveIn this paper, we examine the role of these new package-modularization metrics for determining software fault-proneness in object-oriented systems.MethodWe first use principal component analysis to analyze whether these new package-modularization metrics capture additional information compared with traditional package-level metrics. Second, we employ univariate prediction models to investigate how these new package-modularization metrics are related to fault-proneness. Finally, we build multivariate prediction models to examine the ability of these new package-modularization metrics for predicting fault-prone packages.ResultsOur results, based on six open-source object-oriented software systems, show that: (1) these new package-modularization metrics provide new and complementary views of software complexity compared with traditional package-level metrics; (2) most of these new package-modularization metrics have a significant association with fault-proneness in an expected direction; and (3) these new package-modularization metrics can substantially improve the effectiveness of fault-proneness prediction when used with traditional package-level metrics together.ConclusionsThe package-modularization metrics proposed by Sarkar, Kak, and Rama are useful for practitioners to develop quality software systems.     

Applying machine learning to software fault-proneness prediction

The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module’s fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA’s Metrics Data Program data repository.