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Department of ComputingRefereed conference pape

Replication data for: Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults

In the last decade, empirical studies on object-oriented design metrics have shown some of them to be useful for predicting the fault-proneness of classes in object-oriented software systems. This research did not, however, distinguish among faults according to the severity of impact. It would be valuable to know how object-oriented design metrics and class fault-proneness are related when fault severity is taken into account. In this paper, we use logistic regression and machine learning methods to empirically investigate the usefulness of object-oriented design metrics, specifically, a subset of the Chidamber and Kemerer suite, in predicting fault-proneness when taking fault severity into account. Our results, based on a public domain NASA data set, indicate that 1) most of these design metrics are statistically related to fault-proneness of classes across fault severity, and 2) the prediction capabilities of the investigated metrics greatly depend on the severity of faults. More specifically, these design metrics are able to predict low severity faults in fault-prone classes better than high severity faults in fault-prone classes

Replication data for: Examining the potentially confounding effect of class size on the associations between object-oriented metrics and change-proneness

Previous research shows that class size can influence the associations between object-oriented (OO) metrics and fault-proneness and therefore proposes that it should be controlled as a confounding variable when validating OO metrics on fault-proneness. Otherwise, their true associations may be distorted. However, it has not been determined whether this practice is equally applicable to other external quality attributes. In this paper, we use three size metrics, two of which are available during the high-level design phase, to examine the potentially confounding effect of class size on the associations between OO metrics and change-proneness. The OO metrics that are investigated include cohesion, coupling, and inheritance metrics. Our results, based on Eclipse, indicate that (1) the confounding effect of class size on the associations between OO metrics and change-proneness in general exists, regardless of whichever size metric is used, that (2) the confounding effect of class size generally leads to an overestimate of the associations between OO metrics and change-proneness; and that (3) for many OO metrics, the confounding effect of class size completely accounts for their associations with change-proneness or results in a change of the direction of the associations. These results strongly suggest that studies validating OO metrics on change-proneness should also consider class size as a confounding variable

combining concept lattice with call graph for impact analysis

Software change impact analysis (CIA) is a key technique to identify unexpected and potential effects caused by software changes. Given a changed entity, most of current CIA techniques compute the change effect composed of some potentially impacted entities. The generated results are often of no help to the maintainers in starting the analysis of impacted entities. In this article, we combine concept lattice with call graph together to obtain a ranked list of potentially impacted methods from the proposed changed methods and/or classes. These impacted methods are ranked based on the hierarchical feature of concept lattice, represented by an impact factor, which can then be used to prioritize these methods to be inspected. Case studies based on four real-world programs show that our approach can improve the precision of the impact result without severely decreasing its recall, when compared with results from either concept lattice or call graph used independently. In addition, the predicted impacted methods with higher impact factor values are also shown to have higher probability to be affected by the changes. Our study also shows that our approach is better than the JRipples CIA approach in removing the false-positives, but at the cost of losing more false-negatives and much more time overhead. © 2012 Elsevier Ltd. All rights reserved