Which Process Metrics Are Significantly Important to Change of Defects in Evolving Projects: An Empirical Study

Jiang, Li; Jiang, Shujuan; Gong, Lina; Dong, Yue; Yu, Qiao

doi:10.1109/access.2020.2994528

Cited by 6 publications

(6 citation statements)

References 46 publications

(80 reference statements)

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“…For file-level defect prediction, two types of features can be distinguished: process features, which describes the software development process, and code features, which reflect code properties. Process features can be divided into developer metrics, code change metrics, and development process metrics [27]. Code features include static code metrics, objectoriented metrics, network metrics, and source code features, which are extracted from a source code represented by an AST or a control flow graph (CFG).…”

Section: A Software Defect Predictionmentioning

confidence: 99%

Graph Neural Network for Source Code Defect Prediction

et al. 2022

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Predicting defective software modules before testing is a useful operation that ensures that the time and cost of software testing can be reduced. In recent years, several models have been proposed for this purpose, most of which are built using deep learning-based methods. However, most of these models do not take full advantage of a source code as they ignore its tree structure or they focus only on a small part of a code. To investigate whether and to what extent information from this structure can be beneficial in predicting defective source code, we developed an end-to-end model based on a convolutional graph neural network (GCNN) for defect prediction, whose architecture can be adapted to the analyzed software, so that projects of different sizes can be processed with the same level of detail. The model processes the information of the nodes and edges from the abstract syntax tree (AST) of the source code of a software module and classifies the module as defective or not defective based on this information. Experiments on open source projects written in Java have shown that the proposed model performs significantly better than traditional defect prediction models in terms of AUC and F-score. Based on the F-scores of the existing state-of-the-art models, the model has shown comparable predictive capabilities for the analyzed projects.INDEX TERMS Software defect prediction, deep learning, graph neural network.

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Section: A Software Defect Predictionmentioning

confidence: 99%

Graph Neural Network for Source Code Defect Prediction

et al. 2022

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“…Many studies have so far focused on process metrics rather than product or code metrics when developing models to predict cross-version defects. In this context, they have proposed different process metrics that can be divided into the following three types: developer metrics, code change metrics and development process metrics [77].…”

Section: A Types Of Process Metricsmentioning

confidence: 99%

“…Using the same data set, Jiang et al [77] investigated which process metrics are significantly important for altering defects. In their study, the following process metrics were considered: a number of revisions, a number of distinct committers, a number of modified lines, a degree of code modification, and an average number of modified lines.…”

Section: B Historical Sequences Of Software Metricsmentioning

confidence: 99%

“…Since recent research [24], [77] has shown that the quality of a software module can be estimated based on the commit history of a project, we decided to extract more process metrics and analyze their impact on the performance of software defect predictors. The proposed metrics of the same name are proposed by Madeyski and Jureczko [5], but they are not designed to identify defect-prone modules, but to detect defective commits, and therefore differ in their formulas.…”

Section: B Historical Sequences Of Software Metricsmentioning

confidence: 99%

“…The second setting uses a recursive formula to aggregate change metrics that are not represented by the size of some set, which is the case in the first setting. Some previous studies [6]- [8], [77] have defined some process metrics as an average, minimum or maximum of values for some metric over commits. However, they ignore the chronological order of the commits, which we believe should be considered.…”

Section: ) Aggregated Number Of Developers (mentioning

confidence: 99%

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Improving Software Defect Prediction by Aggregated Change Metrics

et al. 2021

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To ensure the delivery of high quality software, it is necessary to ensure that all of its artifacts function properly, which is usually done by performing appropriate tests with limited resources. It is therefore desirable to identify defective artifacts so that they can be corrected before the testing process. So far, researchers have proposed various predictive models for this purpose. Such models are typically trained on data representing previous project versions of a software and then used to predict which of the software artifacts in the new version are likely to be defective. However, the data representing a software project usually consists of measurable properties of the project or its modules, and leaves out information about the timeline of the software development process. To fill this gap, we propose a new set of metrics, namely aggregated change metrics, which are created by aggregating the data of all changes made to the software between two versions, taking into account the chronological order of the changes. In experiments conducted on open source projects written in Java, we show that the stability and performance of commonly used classification models are improved by extending a feature set to include both measurable properties of the analyzed software and the aggregated change metrics.INDEX TERMS Classification, feature engineering, process metrics, change metrics, software defect prediction.

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