Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction

Huang, Qiao; Xia, Xin; Lo, David

doi:10.1007/s10664-018-9661-2

Cited by 99 publications

(97 citation statements)

References 59 publications

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“…However, their study supports the general goal of Yang et al [8]. Huang et al [29] also performed a replication study based on the study of Yang et al [8], and extended their study in the literature [30]. ey found three weaknesses of the LT model proposed by Yang et al [8]: more context switching, more false alarms, and worse F1 than supervised methods.…”

Section: Effort-aware Software Defect Predictionsupporting

confidence: 54%

Local versus Global Models for Just-In-Time Software Defect Prediction

Yang

Fan

et al. 2019

Scientific Programming

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Just-in-time software defect prediction (JIT-SDP) is an active topic in software defect prediction, which aims to identify defect-inducing changes. Recently, some studies have found that the variability of defect data sets can affect the performance of defect predictors. By using local models, it can help improve the performance of prediction models. However, previous studies have focused on module-level defect prediction. Whether local models are still valid in the context of JIT-SDP is an important issue. To this end, we compare the performance of local and global models through a large-scale empirical study based on six open-source projects with 227417 changes. The experiment considers three evaluation scenarios of cross-validation, cross-project-validation, and timewise-cross-validation. To build local models, the experiment uses the k-medoids to divide the training set into several homogeneous regions. In addition, logistic regression and effort-aware linear regression (EALR) are used to build classification models and effort-aware prediction models, respectively. The empirical results show that local models perform worse than global models in the classification performance. However, local models have significantly better effort-aware prediction performance than global models in the cross-validation and cross-project-validation scenarios. Particularly, when the number of clusters k is set to 2, local models can obtain optimal effort-aware prediction performance. Therefore, local models are promising for effort-aware JIT-SDP.

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Section: Effort-aware Software Defect Predictionsupporting

confidence: 54%

Local versus Global Models for Just-In-Time Software Defect Prediction

Yang

Fan

et al. 2019

Scientific Programming

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“…Yang [1] greatly validates the importance of having the unsupervised model, as well as their methodology of computing the reciprocal of the raw metric and excluding the LA and LD and then removing the highly correlated data, as this helped in ranking the values obtained in descending order [31]. However, whilst observing the correlation with the target value and availability of sufficient data, it became obvious to opt for the supervised model.…”

Section: Discussionmentioning

confidence: 84%

A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

Albahli

2019

Future Internet

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Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions.

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“…Recently, researchers have put more emphasis on process metrics. A considerable process metrics are proposed, mainly including: i) metrics based on code change history, such as number of modified lines [13], [19], [20], [23], [28]- [33], and code relative change metrics [13], [20], [27], [30], ii) metrics based on developer information, such as number of distinct committers [15], [19], [20], [23]- [26], [30], [32], [34], experience of developers [16], [34], commit activities of developers [34], project team organizational structure [17], code ownership [34], and organizational dispersion degree [18], iii) development process related metrics, such as number of revisions [13]- [15], [19], [20], [22], [23], [25], [30], number of defects repaired [30], number of refactorings [20], [30], code change complexity [21], [32], and number of historical defects [19], [23]. The most widely used, classical, and defect-related process metrics are Number of Revisions (NR), Number of Distinct Committers (NDC), Number of Modified Lines (NML), and code relative change metrics.…”

Section: A Process Metricsmentioning

confidence: 99%

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

Jiang

Gong

et al. 2020

IEEE Access

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Process metrics can reflect the software development process and the code changes which are the main causes of defects. So, recently, the researches have put more emphasis on process metrics in the field of software defect prediction. For evolving projects, it is more meaningful to study whether the software module introduces or eliminates defects or not, not whether the software module is defective or defect-free. However, no such work is available in the literature focusing on the change of defect state. Discovering the factors that influence the change of defect state in the process of software development can help us to understand the causes of software defects and improve the quality of subsequent software versions. Therefore, this paper presents an extensive empirical study on which process metrics are significantly important to change of defects in evolving projects. Five process metrics of 37 versions in 12 software projects are collected. We not only analyze the class correlation values and the classification performance values among five process metrics, but also perform statistical analysis to verify whether the experimental results are of practical value. The experimental results indicate that Number of Distinct Committers plays a significantly important role in the change of defect state, especially for elimination of defects, and Number of Revisions is the second, whereas Degree of Code Modification is the last. In addition, Average Number of Modified Lines is superior to Number of Modified Lines. Based on the experimental results, some suggestions for software development and software defect prediction are also discussed.

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Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction

Cited by 99 publications

References 59 publications

Local versus Global Models for Just-In-Time Software Defect Prediction

Local versus Global Models for Just-In-Time Software Defect Prediction

A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

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

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