Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction

Li, Yihao; Wong, Wan Hui; Lee, Shou-Yu; Wotawa, Franz

doi:10.1109/access.2019.2916615

Cited by 26 publications

(9 citation statements)

References 73 publications

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“…In most cases, the software defect data contain much fewer defective modules than the defect-free modules, and this ratio is lower in extreme cases. In general, the minority defective class is usually called positive class, while the majority defect-free class is called negative class correspondingly [24][25].…”

Section: Class Imbalance Learningmentioning

confidence: 99%

Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction

李勇¹,

Li²,

Wen³

et al. 2022

Journal of Internet Technology

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Cross-project defect prediction (CPDP) is a field of study that allows predicting defects in software projects for which the availability of data is limited and produces generalizable prediction models. Due to the heterogeneity of cross projects, CPDP is particularly challenging and several methods have been employed to address this problem. Nevertheless, the class-imbalanced characteristic of the cross-project defect data also increases the learning difficulty of such a task but has not been investigated in depth. This paper proposed a novel, cost-cognitive ensemble method for CPDP, which includes four phases: bagging balanced resampling phase, base classifiers learning phase, cost value cognitive phase, and base classifiers ensemble phase. These phases create a composition of classifiers that are used for predicting defects. Results of an empirical evaluation on 10 datasets from the PROMISE repository indicated that our method achieves the best overall performance with respect to conventional methods. Moreover, our method could cognize the cost value automatically during the model training, it is shown to be more effective and practical.

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Section: Class Imbalance Learningmentioning

confidence: 99%

Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction

李勇¹,

Li²,

Wen³

et al. 2022

Journal of Internet Technology

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“…is the covariance of and represents the Frobenius norm of the matrix. According to the solution process in [14], we can get the optimal solution of A:…”

Section: Wherementioning

confidence: 99%

“…In response to this situation, researchers have proposed the Cross-project Defect Prediction method [14][15][16][17][18][19][20][21]. The Crossproject Defect Prediction (CPDP) method is used to train the model based on the labeled data of other similar software projects (i.e.…”

Section: Introductionmentioning

confidence: 99%

Cross-Project Defect Prediction Method based on Feature Distribution Alignment and Neighborhood Instance Selection

Zhu¹,

Zhu²,

Zhao³

et al. 2022

Journal of Internet Technology

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In the practice of software project development, the developed project is a brand-new project. Defect prediction for this type of software project requires the use of other similar projects (i.e. source projects) to collect relevant data to build a defect prediction model, and make defect prediction for the project under development (i.e. target project). However, the prediction model built with the relevant data of the source project cannot achieve the ideal prediction performance when predicting the target project. The main reason is that there is a large data distribution difference between the source project and the target project. The data distribution difference is mainly in the distribution of features between projects and differences between instances. In response to the above problems, starting from both features and instances, a cross-project defect prediction method is proposed. This method first aligns the feature distribution based on the data of the existing target project and the source project data. Then, it selects the labeled instance that is similar to the unlabeled instance in the target project, and finally builds a defect prediction model based on the selected source project instances. Cross-project defect prediction experiments were carried out on the Relink datasets and the Promise datasets. Compared with the classic instance-based cross-project defect prediction method, significant improvements have been made in F-measure and AUC; compared with the prediction of within project defect prediction, it has achieved comparable performance.

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“…It can quickly predict defects and provide guidance for allocating test resources and manpower in the early stage of software development [1,2]. At present, most SDP methods use machine learning technology to build defect prediction models [3][4][5]. For example, Lessmann et al [6] and Shepperd et al [7] used traditional machine learning algorithms, such as decision tree, naive Bayes (NB), neural network, and support vector machine (SVM) to SDP, and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Tsbagging: A Novel Cross-Project Software Defect Prediction Algorithm Based on Semisupervised Clustering

Tang

Huang

Liu

et al. 2022

Scientific Programming

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Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods.

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Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction

Cited by 26 publications

References 73 publications

Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction

Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction

Cross-Project Defect Prediction Method based on Feature Distribution Alignment and Neighborhood Instance Selection

Tsbagging: A Novel Cross-Project Software Defect Prediction Algorithm Based on Semisupervised Clustering

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