An Adversarial Discriminative Convolutional Neural Network for Cross-Project Defect Prediction

Lei, Sheng; Lu, Lu; Lin, Junhao

doi:10.1109/access.2020.2981869

Cited by 23 publications

(16 citation statements)

References 39 publications

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“…We can see that BugPre achieves an average F1-score of 67.2% in five datasets, outperforming the other three approaches (in terms of F1-score). In addition, we conducted a comparison with a CPDP method named ADCNN [57]. The average F1-score of BugPre is 27% higher than that of ADCNN across the same dataset.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

Wang

Tong

et al. 2022

Complex Intell. Syst.

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Since defects in software may cause product fault and financial loss, it is essential to conduct software defect prediction (SDP) to identify the potentially defective modules, especially in the early stage of the software development lifecycle. Recently, cross-version defect prediction (CVDP) began to draw increasing research interests, employing the labeled defect data of the prior version within the same project to predict defects in the current version. As software development is a dynamic process, the data distribution (such as defects) during version change may get changed. Recent studies utilize machine learning (ML) techniques to detect software defects. However, due to the close dependencies between the updated and unchanged code, prior ML-based methods fail to model the long and deep dependencies, causing a high false positive. Furthermore, traditional defect detection is performed on the entire project, and the detection efficiency is relatively low, especially on large-scale software projects. To this end, we propose BugPre, a CVDP approach to address these two issues. BugPre is a novel framework that only conducts efficient defect prediction on changed modules in the current version. BugPre utilizes variable propagation tree-based associated analysis method to obtain the changed modules in the current version. Besides, BugPre constructs graph leveraging code context dependences and uses a graph convolutional neural network to learn representative characteristics of code, thereby improving defect prediction capability when version changes occur. Through extensive experiments on open-source Apache projects, the experimental results indicate that our BugPre outperforms three state-of-the-art defect detection approaches, and the F1-score has increased by higher than 16%.

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Section: Comparison With Prior Workmentioning

confidence: 99%

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

Wang

Tong

et al. 2022

Complex Intell. Syst.

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“…Unlike designed top-down hand-crafted features, the features are generated bottom-up from the source code, which could represent structural and semantics information of the source code. Recently, many deep learning models, including Deep Belief Networks [4,15], CNN [5][6][7]10,17,19,24,25], LSTM [11,12,14,16,18], Transformers [8], and other deep learning models [13,22] are used in software defect prediction.…”

Section: Deep Transfer Learningmentioning

confidence: 99%

An Empirical Study on Software Defect Prediction Using CodeBERT Model

Pan

2021

Applied Sciences

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Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

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“…Lei Sheng et al [26] proposed Adversarial Discriminative Convolutional Neural Network (ADCNN) which reduces distribution divergence between source and target projects. It consists of two phases.…”

Section: B Deep Learning-based Cpdpmentioning

confidence: 99%

Generative Adversarial Network-based Cross-Project Fault Prediction

Pal

2021

Preprint

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Background: The early stage of defect prediction in the software development life cycle can reduce testing effort and ensure the quality of software. Due to the lack of historical data within the same project, Cross-Project Defect Prediction (CPDP) has become a popular research topic among researchers. CPDP trained classifiers based on labeled data sets of one project to predict fault in another project. Goals: Software Defect Prediction (SDP) data sets consist of manually designed static features, which are software metrics. In CPDP, source and target project data divergence is the major challenge in achieving high performance. In this paper, we propose a Generative Adversarial Network (GAN)-based data transformation to reduce data divergence between source and target projects. Method: We apply the Generative Adversarial Method where label data sets are choosing as real data, while target data sets are choosing as fake data. The Discriminator tries to measure the perfection of domain adaptation through loss function. Through the generator, target data sets try to adapt the source project domain and, finally, apply machine learning classifier (i.e., Naive Bayes) to classify faulty modules. Results: Our result shows that it is possible to predict defects based on the Generative Adversarial Method. Our model performs quite well in a cross-project environment when we choose JDT as a target data sets. However, all chosen data sets are facing a large class imbalance problem which affects the performance of our model.

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An Adversarial Discriminative Convolutional Neural Network for Cross-Project Defect Prediction

Cited by 23 publications

References 39 publications

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

An Empirical Study on Software Defect Prediction Using CodeBERT Model

Generative Adversarial Network-based Cross-Project Fault Prediction

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