CoreBug: Improving Effort-Aware Bug Prediction in Software Systems Using Generalized k-Core Decomposition in Class Dependency Networks
Xin Du, Tian Wang, Liuhai Wang, Weifeng Pan, Chunlai Chai, Xinxin Xu, Bo Jiang, Jiale Wang
Abstract
Complex network theory has been successfully introduced into the field of software engineering. Many works in the literature have built complex networks in software, usually called software networks, to represent software structure. Such software networks and their related graph algorithms have been proved effective in predicting software bugs. However, the software networks used were unweighted and undirected, neglecting the strength and direction of the couplings. Worse still, they ignored many important types of couplings between classes, such as local variable, instantiates, and access. All of these greatly affect the accuracy of the software network in representing the topological detail of software projects and ultimately affect the metrics derived from it. In this work, an improved effort-aware bug prediction approach named CoreBug is proposed. First, CoreBug uses a weighted directed class dependency network (WDCDN) to precisely describe classes and their couplings, including nine coupling types and their different coupling strengths and directions. Second, a generalized k-core decomposition is introduced to compute the coreness of each class in the WDCDN. Third, CoreBug combines the coreness of each class with its relative risk, as returned by the logistic regression, to quantify the risk of a given class being buggy. Empirical results on eighteen Java projects show that CoreBug is superior to the state-of-the-art approaches according to the average ranking of the Friedman test.