MULTI: Multi-objective effort-aware just-in-time software defect prediction

Chen, Xiang; Zhao, Yingquan; Wang, Qiuping; Yuan, Zhidan

doi:10.1016/j.infsof.2017.08.004

Cited by 124 publications

(78 citation statements)

References 23 publications

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“…Tong et al worked on SDP using dual-stage ensembles and encoders [16]. Multi objective effort aware SDP model called as Just in Time(JIT) software defect predictor has been proposed by Chen et al They have used logistic regression to build this JIT software defect predictor [15]. Czibula et al, [4] proposed a novel classification model based on relational association rules mining which is an extension of ordinal association rules that describe numerical orderings between attributes that commonly occur over a dataset.…”

Section: Literature Surveymentioning

confidence: 99%

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Kumar*,

Prasad

2020

IJITEE

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Software enhances the working capability of any business. Developing such a software entrusts the developing organization to build defect free software. In this context we have used PC1 dataset(NASA dataset) which has sufficient parameters for analysis. Intelligent techniques using different methodologies have been applied exhaustively on the PC1 data to find out the best intelligent technique for software defect. As the PC1 data is highly imbalanced data, there was biasness in the prediction of the intelligent techniques. Hence, to overcome this issue, in this paper we tried to propose best balancing method along with the intelligent technique to predict the software defect accurately.

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Section: Literature Surveymentioning

confidence: 99%

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Kumar*,

Prasad

2020

IJITEE

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“…Therefore, we further consider AUC to evaluate the performance of different CPDP methods. Except for these traditional performance measures, we want to consider some effort‐aware performance measures, which consider the limitation of available testing resources.…”

Section: Threats To Validitymentioning

confidence: 99%

Do different cross‐project defect prediction methods identify the same defective modules?

Chen

Qu³

et al. 2019

J Software Evolu Process

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Cross‐project defect prediction (CPDP) is needed when the target projects are new projects or the projects have less training data, since these projects do not have sufficient historical data to build high‐quality prediction models. The researchers have proposed many CPDP methods, and previous studies have conducted extensive comparisons on the performance of different CPDP methods. However, to the best of our knowledge, it remains unclear whether different CPDP methods can identify the same defective modules, and this issue has not been thoroughly explored. In this article, we select 12 state‐of‐the‐art CPDP methods, including eight supervised methods and four unsupervised methods. We first compare the performance of these methods in the same experiment settings on five widely used datasets (ie, NASA, SOFTLAB, PROMISE, AEEEM, and ReLink) and rank these methods via the Scott‐Knott test. Final results confirm the competitiveness of unsupervised methods. Then we perform diversity analysis on defective modules for these methods by using the McNemar test. Empirical results verify that different CPDP methods may lead to difference in the modules predicted as defective, especially when the comparison is performed between the supervised methods and unsupervised methods. Finally, we also find there exist a certain number of defective modules, which cannot be correctly identified by any of the CPDP methods or can be correctly identified by only one CPDP method. These findings can be utilized to design more effective methods to further improve the performance of CPDP.

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“…In addition, since large files have been modified by multiple developers, it is not easy to find the right developer to check the files [6]. To this end, just-in-time software defect prediction (JIT-SDP) technology is proposed [7][8][9][10]. JIT-SDP is made at change level, which has a finer granularity than module level.…”

Section: Introductionmentioning

confidence: 99%

“…In order to build local models, the experiment uses the k-medoids clustering algorithm to divide the training samples into homogeneous regions. For each region, the experiment builds classification models and effort-aware prediction models based on logistic regression and effortaware linear regression (EALR), respectively, which are always used for baseline models in prior studies [7][8][9]. e main contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 99%

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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MULTI: Multi-objective effort-aware just-in-time software defect prediction

Cited by 124 publications

References 23 publications

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Do different cross‐project defect prediction methods identify the same defective modules?

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

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