An empirical study on dependence clusters for effort-aware fault-proneness prediction

Abstract

A dependence cluster is a set of mutually inter-dependent program elements. Prior studies have found that large dependence clusters are prevalent in software systems. It has been suggested that dependence clusters have potentially harmful effects on software quality. However, little empirical evidence has been provided to support this claim. The study presented in this paper investigates the relationship between dependence clusters and software quality at the functionlevel with a focus on effort-aware fault-proneness prediction. The investigation first analyzes whether or not larger dependence clusters tend to be more fault-prone. Second, it investigates whether the proportion of faulty functions inside dependence clusters is significantly different from the proportion of faulty functions outside dependence clusters. Third, it examines whether or not functions inside dependence clusters playing a more important role than others are more fault-prone. Finally, based on two groups of functions (i.e., functions inside and outside dependence clusters), the investigation considers a segmented fault-proneness prediction model. Our experimental results, based on five wellknown open-source systems, show that (1) larger dependence clusters tend to be more fault-prone; (2) the proportion of faulty functions inside dependence clusters is significantly larger than the proportion of faulty functions outside dependence clusters; (3) functions inside dependence clusters that play more important roles are more fault-prone; (4) our segmented prediction model can significantly improve the effectiveness of effort-aware fault-proneness prediction in both ranking and classification scenarios. These findings help us better understand how dependence clusters

From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis

“…More recently we were able to demonstrate the potentially pernicious effects of dependence clusters [135], thereby motivating this initial interest. As a result of chasing an unexpected use-case for a deployed research prototype, we were thus rewarded with a rich seam of novel research questions and intellectually-stimulating scientific investigation.…”

Progress on approaches to software defect prediction

“…To study the relationships between dependence clusters and defect‐proneness in effort‐aware defect prediction, Yang et al [124] empirically evaluated the effect of dependence clusters with different statistical techniques. They found that large dependence clusters, functions inside dependence clusters tend to be more defect‐prone, which help us to better understand dependence clusters and its effect on software quality in the effort‐aware context.…”

Connecting software metrics across versions to predict defects

“…In this situation, a cost-effective model would rank the files in descending order of their bug density. The effort-aware ranking effectiveness of classification techniques in defect prediction is always evaluated by cost-effectiveness (CE) curve, which is widely used in prior works [29], [44], [63]. Figure 5 shows an example of the CE curve.…”

“…The preliminary step of bug fixing is to identify the potential locations of bugs in a software project. In the last decades, many software defect prediction models [16], [17], [22], [26], [54], [55], [63], [65] have been proposed to identify defect-prone modules, which could help software engineers test and debug software more effectively and efficiently. In order to achieve accurate defect prediction, it is essential to use quality defect predictors and modeling techniques to build the prediction models.…”