Using Software Dependency to Bug Prediction

He, Peng; Li, Bing; Ma, Yutao; He, Lulu

doi:10.1155/2013/869356

Cited by 8 publications

(9 citation statements)

References 25 publications

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“…Note that, except for mE-Weight, BC measure performs better than the proposed measures. The results further validated the advantage of BC measure as mentioned in [6,30]. Meanwhile, it is clear that mE-Weight measure performs best, which indicated that computing the centrality on the weighted edge of nodes has an advantage compared to that based on the node degree and even prior to other frequently used centrality measures.…”

Section: 4supporting

confidence: 60%

An Improved Approach to Identifying Key Classes in Weighted Software Network

Ding

2016

Mathematical Problems in Engineering

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To help the newcomers understand a software system better during its development, the key classes are in general given priority to be focused on as soon as possible. There are numerous measures that have been proposed to identify key nodes in a network. As a metric successfully applied to evaluate the productivity of a scholar, little is known about whether ℎ-index is suitable to identify the key classes in weighted software network. In this paper, we introduced four ℎ-index variants to identify key classes on three open-source software projects (i.e., Tomcat, Ant, and JUNG) and validated the feasibility of proposed measures by comparing them with existing centrality measures. The results show that the measures proposed not only are able to identify the key classes but also perform better than some commonly used centrality measures (the improvement is at least 0.215). In addition, the finding suggests that mE-Weight defined by the weight of a node's top edges performs best as a whole.

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Section: 4supporting

confidence: 60%

An Improved Approach to Identifying Key Classes in Weighted Software Network

Ding

2016

Mathematical Problems in Engineering

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“…In defect prediction literature, a considerable number of software metrics, such as static code metrics, code change history, process metrics and network metrics [10], have been used to construct different predictors for defect prediction [35]. Almost all existing prediction models are built on the complex combinations of software metrics, with which a prediction model usually can achieve a satisfactory accuracy.…”

Section: Introductionmentioning

confidence: 99%

An empirical study on software defect prediction with a simplified metric set

Liu

et al. 2015

Information and Software Technology

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252

134

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Context: Software defect prediction plays a crucial role in estimating the most defect-prone components of software, and a large number of studies have pursued improving prediction accuracy within a project or across projects. However, the rules for making an appropriate decision between within-and cross-project defect prediction when available historical data are insufficient remain unclear.Objective: The objective of this work is to validate the feasibility of the predictor built with a simplified metric set for software defect prediction in different scenarios, and to investigate practical guidelines for the choice of training data, classifier and metric subset of a given project. Method: First, based on six typical classifiers, three types of predictors using the size of software metric set were constructed in three scenarios. Then, we validated the acceptable performance of the predictor based on Top-k metrics in terms of statistical methods. Finally, we attempted to minimize the Top-k metric subset by removing redundant metrics, and we tested the stability of such a minimum metric subset with one-way ANOVA tests. Results: The study has been conducted on 34 releases of 10 open-source projects available at the PROMISE repository. The findings indicate that the predictors built with either Top-k metrics or the minimum metric subset can provide an acceptable result compared with benchmark predictors. The guideline for choosing a suitable simplified metric set in different scenarios is presented in Table 12. Conclusion: The experimental results indicate that (1) the choice of training data for defect prediction should depend on the specific requirement of accuracy; (2) the predictor built with a simplified metric set works well and is very useful in case limited resources are supplied; (3) simple classifiers (e.g., Naïve Bayes) also tend to perform well when using a simplified metric set for defect prediction; (4) in several cases, the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.

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“…However, these studies still ignore the structural features of programs, such as the dependencies between program files. Prior studies [12,[33][34][35] have demonstrated the effectiveness of network structure information in improving the performance of the defect prediction model. Nowadays, the node of a network can be represented as a low-dimensional vector by means of network embedding.…”

Section: 2mentioning

confidence: 99%

“…It can find the possible defective code blocks according to the features of historical data, thus allowing workers to focus their limited resources on the defect-prone code. Figure 2 presents a basic framework of software defect prediction and has been widely used in existing studies [1,8,12,18,19,24,25].…”

Section: Preliminariesmentioning

confidence: 99%

“…However, the abovementioned studies still overlook the globally structural information among program files which can lead to more accurate defect prediction, although they consider the fine-grained semantic information in the program files. In order to better represent the global structure of software, previous studies [10][11][12] have successfully abstracted a software as a directed dependency network using complex network theory, usually termed as software network (SN), where software components such as files, classes, or packages are nodes and the dependency relationships between them are edges. Furthermore, using network analysis technologies, they have demonstrated the effectiveness of network structure information in improving the performance of defect prediction.…”

Section: Introductionmentioning

confidence: 99%

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An Approach to Semantic and Structural Features Learning for Software Defect Prediction

Meilong

Xiao

et al. 2020

Mathematical Problems in Engineering

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Research on software defect prediction has achieved great success at modeling predictors. To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features. Few models, however, consider the semantic and structural features of programs. Understanding the context information of source code files could explain a lot about the cause of defects in software. In this paper, we leverage representation learning for semantic and structural features generation. Specifically, we first extract token vectors of code files based on the Abstract Syntax Trees (ASTs) and then feed the token vectors into Convolutional Neural Network (CNN) to automatically learn semantic features. Meanwhile, we also construct a complex network model based on the dependencies between code files, namely, software network (SN). After that, to learn the structural features, we apply the network embedding method to the resulting SN. Finally, we build a novel software defect prediction model based on the learned semantic and structural features (SDP-S2S). We evaluated our method on 6 projects collected from public PROMISE repositories. The results suggest that the contribution of structural features extracted from software network is prominent, and when combined with semantic features, the results seem to be better. In addition, compared with the traditional hand-crafted features, the F-measure values of SDP-S2S are generally increased, with a maximum growth rate of 99.5%. We also explore the parameter sensitivity in the learning process of semantic and structural features and provide guidance for the optimization of predictors.

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Using Software Dependency to Bug Prediction

Cited by 8 publications

References 25 publications

An Improved Approach to Identifying Key Classes in Weighted Software Network

An Improved Approach to Identifying Key Classes in Weighted Software Network

An empirical study on software defect prediction with a simplified metric set

An Approach to Semantic and Structural Features Learning for Software Defect Prediction

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