A universal data augmentation approach for fault localization

Xie, Huan; Lei, Yan; Yan, Meng; Yu, Yue; Xia, Xin; Mao, Xiaoguang

doi:10.1145/3510003.3510136

Cited by 25 publications

(8 citation statements)

References 48 publications

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“…• RImp (relative improvement) 32 is the total number of statements that need to be checked for locating all faults with our approach divided by that using other techniques. If the value of RIMP is less than 1, it means that the total number of statements needed to be checked by our approach is less than that by comparison techniques.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Improving fault localization via weighted execution graph and graph attention network

Yan,

Jiang,

Zhang

et al. 2023

J Software Evolu Process

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Software fault localization is commonly recognized as arduous and time consuming. Spectrum‐based fault localization (SBFL) has been widely used due to its lightness. However, the effectiveness of SBFL is limited since it only considers simple statistics on the coverage information, ignoring the tie problem that the spectrum matrixes of some statements are the same. Most existing deep learning‐based fault localization (DLFL) techniques convert the coverage information into a vector, which utilizes the spectrum in a simplified manner and still has limitations in practice. To solve the above problem, we propose an approach via the weighted execution graph and graph attention network (WEGAT). We use a graph structure to represent the coverage information between test cases and program elements. Then, we generate a weighted execution graph by applying the predicate execution sequence. Furthermore, we combine the weighted execution graph with the AST as an integrated graph, which is the input of the GAT for fault localization. We evaluate WEGAT in within‐project and cross‐project prediction scenarios on the Defects4J benchmark. Experimental results show that our approach outperforms traditional SBFL (Ochiai, DStar and Tarantula) and DLFL (TraPT, CNN‐FL, Grace, and AGFL) methods, effectively improving the accuracy of fault localization.

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Section: Evaluation Metricsmentioning

confidence: 99%

Improving fault localization via weighted execution graph and graph attention network

Yan,

Jiang,

Zhang

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…To enhance the impact of failed test cases on fault localization, Xie et al (2022) used a universal data augmentation method that generates synthesized failing test cases from reduced feature space for improving fault localization. Zeng et al (2022) introduced a probabilistic approach to model program semantics and utilize information from static analysis and dynamic execution traces for fault localization, which balance could be reached between effectiveness and scalability.…”

Section: Related Workmentioning

confidence: 99%

The improved dynamic slicing for spectrum-based fault localization

Cao

Wang

Deng

et al. 2022

PeerJ Computer Science

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Background Spectrum-based Fault localization have proven to be useful in the process of software testing and debugging. However, how to improve the effectiveness of software fault localization has always been a research hot spot in the field of software engineering. Dynamic slicing can extract program dependencies under certain conditions. Thus, this technology is expected to benefit for locating fault. Methods We propose an improved dynamic slicing for spectrum-based fault localization under a general framework. We first obtain the dynamic slice of program execution. Secondly, we construct a mixed slice spectrum matrix from the dynamic slice of each test case and the corresponding test results. Finally, we compute the suspiciousness value of each statement in the mixed-slice spectram matrix. Results To verify the performance of our method, we conduct an empirical study on 15 widely used open-source programs. Experimental results show that our approach achieves significant improvement than the compared techniques. Conclusions Our approach can reduce approximately 1% to 17.79% of the average cost of code examined significantly.

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“…Zhao et al [9] and Che et al [10] directly introduced the VAE for vibration signal generation, which improved the performance of the fault diagnosis method with unbalanced data. Xie et al [11] proposed a conditional VAE and principle component analysis combined framework for data augmentation to achieve accurate fault identification. Through implicitly learning the data distribution via an adversarial learning framework, new samples with a similar distribution to the original data can be generated by GAN [12].…”

Section: Introductionmentioning

confidence: 99%

A dual-view network for fault diagnosis in rotating machinery using unbalanced data

Chen¹,

Yu²,

Kong³

et al. 2023

Meas. Sci. Technol.

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Data-driven intelligent methods have demonstrated their effectiveness in the area of fault diagnosis. However, most existing studies are based on the assumption that the distributions of normal and faulty samples are balanced during the diagnostic process. This assumption significantly decreases the application range of a diagnostic model as the samples in most real-world scenarios are highly unbalanced. To cope with the limitations caused by unbalanced data, this paper proposed an original dual-view network (DVN). Firstly, an interactive graph modeling strategy is introduced for relationship information modeling of multi-sensor data. Meanwhile, the graph convolution operation is used as the baseline for feature extraction of the constructed interactive graph to mine for fault representations. Secondly, an original dual-view classifier consisting of a binary classifier and a multi-class classifier is proposed, which divides fault diagnosis into two stages. Specifically, in the first stage, the binary classifier performs the binary inference from the view of fault detection. In the second stage, the multi-class classifier performs the full-state inference from the view of fine-grained fault classification. Then, based on the dual-view classifier, a weight activation module is designed to alleviate training bias toward majority classes by sample-level re-weighting. Finally, the diagnosis results can be obtained according to the output of the multi-class classifier. Fault diagnosis experiments using two different datasets with varying data unbalance ratios were conducted to validate the effectiveness of the proposed method. The superiority of the proposed DVN is verified through comparisons with state-of-the-art methods. The effectiveness of the DVN is further validated through ablation studies with some ablative models. The DVN code is available at: https://github.com/CQU-ZixuChen/DualViewNetwork.

show abstract

A universal data augmentation approach for fault localization

Cited by 25 publications

References 48 publications

Improving fault localization via weighted execution graph and graph attention network

Improving fault localization via weighted execution graph and graph attention network

The improved dynamic slicing for spectrum-based fault localization

A dual-view network for fault diagnosis in rotating machinery using unbalanced data

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