Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks

Fang, Sen; Tan, Youshuai; Zhang, Tao; Zhou, Xu; Hui, Lam

doi:10.1109/tr.2021.3074412

Cited by 27 publications

(10 citation statements)

References 40 publications

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“…Dataset To pre-trial CoLoc by masked language model, we utilize the public dataset released by Fang et al [2]. Fang et al collected more than 270,000 bug reports from four projects in BugZilla, including Mozilla, Eclipse, Netbeans, and GNU compiler collection (GCC).…”

Section: B Dataset and Baselinesmentioning

confidence: 99%

“…They thus must have a comprehensive understanding of the bug report and retrieve lots of source code files. According to prior research [2], there are more and more bug reports submitted to the bug tracker system, e.g., more than 10K submitted bug reports in the Mozilla 1 project in two months, which brings huge time cost to developers for locating newly produced bugs. It reduces the efficiency of software maintenance, causing a negative effect on the quality of software production.…”

Section: Introductionmentioning

confidence: 99%

“…According to the prior work [8], we split it into training, validation, and test sets. Specifically, we first pre-train CoLoc on a large-scale bug report corpus released by Fang et al [2]. Afterward, we further pre-train CoLoc on the training set with our designed contrastive learning objective.…”

Section: Introductionmentioning

confidence: 99%

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Improving Bug Localization With Effective Contrastive Learning Representation

Luo¹,

Wang

Cen

2023

IEEE Access

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Automated localization of buggy files can accelerate developers' efficiency of software maintenance, improving the quality of software products. State-of-the-art approaches for bug localization is based on neural networks, e.g., RNN or CNN, and can learn semantic feature from the given bug report. However, these simple neural architectures are difficult to learn the deep contextual feature from bug reports, which hurts the semantic mapping between bug reports and their corresponding buggy files. To resolve the above problem, in this paper we propose a bug localization approach that combines pre-trained language models and contrastive learning, namely CoLoc. Specifically, CoLoc first is pre-trained on a large-scale bug report corpus in an unsupervised way, to learn the deep contextual feature of each token in the bug report according to its context. Afterward, CoLoc is further pre-trained by a contrastive learning objective to learn the contrastive learning representations both of bug reports and buggy files. Contrastive learning can help CoLoc to learn the semantic differences between different bug reports and buggy files. To evaluate the effectiveness of CoLoc, we choose five baseline approaches and compare their performance on a public dataset. The experimental results show that CoLoc outperforms all baseline approaches and achieves new results for bug localization.

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Section: B Dataset and Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Bug Localization With Effective Contrastive Learning Representation

Luo¹,

Wang

Cen

2023

IEEE Access

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show abstract

“…By comparing Figure 2 a,b, it can be found that the use of subaperture segmentation technology to improve the refocusing effect of dominant waves will lead to a decrease in the signal-to-noise ratio of the SAR image. Therefore, to obtain the optimum subaperture, this paper proposed a new evaluation, namely,

, referring to the calculation method of the F -measure [ 36 , 37 ] in machine learning, which considers the peak-to-background ratio and the equivalent number of looks. The

can be calculated by:

among them,

…”

Section: Sar Imaging Algorithm Of Ocean Waves Based On Optimum Subape...mentioning

confidence: 99%

SAR Imaging Algorithm of Ocean Waves Based on Optimum Subaperture

Zhao

Wei

Chong

et al. 2022

Sensors

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Synthetic Aperture Radar (SAR) is widely applied to the field of ocean remote sensing. Clear SAR images are the basis for ocean information acquisitions, such as parameter retrieval of ocean waves and wind field inversion of the ocean surface. However, the SAR ocean images are usually blurred, which seriously affects the acquisition of ocean information. The reasons for the wave blurring in SAR images mainly include the following two aspects. One is that when SAR observes the ocean, the motion of ocean waves will have a greater impact on imaging quality. The other is that the ocean’s surface is seriously decorrelated within the integration time. In order to obtain clear SAR images of ocean waves, a SAR imaging algorithm of ocean waves based on the optimum subaperture is proposed, aiming at the above two aspects. The optimum focus setting of the ocean waves is calculated, drawing support from the azimuth phase velocity of the dominant wave. The optimum subaperture is further calculated according to the proposed new evaluation, namely, F. Finally, according to the optimum focus setting and the optimum subaperture, the dominant wave is refocused, and a clear SAR image of the dominant wave can be obtained. The proposed algorithm was applied to airborne L-band and P-band SAR data. Furthermore, the proposed algorithm was compared with present methods, and the results sufficiently demonstrated the effectiveness and superiority of the proposed algorithm.

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“…Bug reports play a pivotal role in the software maintenance lifecycle [1]. However, the surge in bug reports submitted to tracking systems like Bugzilla 1 and Jira 2 , as mentioned in Fang et al's work [2], has rendered activities such as severity and priority assessment of bugs increasingly time-consuming and cumbersome for triagers. This escalation in workload adversely affects the efficiency of the entire software maintenance cycle.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Bug Report Summaries Through Knowledge-Specific and Contrastive Learning Pre-Training

Shao,

Xiang

2024

IEEE Access

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Bug reports are crucial in software maintenance, with concise summaries significantly enhancing the efficiency of bug triagers and ultimately contributing to the development of high-quality software products. Contemporary methods for automatic bug report summarization primarily utilize neural networks' robust learning capabilities. However, these approaches often produce suboptimal summaries due to two primary limitations: 1) the difficulty in assimilating the domain-specific knowledge inherent in bug reports, and 2) the limitations of purely supervised learning in comprehending the comprehensive context of bug reports. To address the above two problems, in this paper, we propose a new approach for bug report summarization, namely KSCLP, which leverages large language models and domain-specific pre-training strategies, i.e., Knowledge-Specific and Contrastive Learning Pre-training. Specifically, the Knowledge-Specific strategy allows to pre-train KSCLP on project-specific bug reports corpus, by which the model can fully learn internal knowledge of bug reports, learning bug report-aware representation. As for the Contrastive Learning strategy, it performs a sequence-level pre-training for KSCLP, helping it capture the semantic information of bug reports on a global level. Upon completion of the pre-training phase, KSCLP undergoes further refinement through a Sequence-to-Sequence framework specifically tailored for bug report summarization. The efficacy of KSCLP is rigorously evaluated against five baseline models using a publicly available dataset. The empirical results demonstrate that KSCLP outperforms all baselines, achieving remarkable improvements by up to 23.73, 13.97, and 20.89 points in ROUGE-1, ROUGE-2, and ROUGE-L metrics, thereby setting new benchmarks in the field of bug report summarization.

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Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks

Cited by 27 publications

References 40 publications

Improving Bug Localization With Effective Contrastive Learning Representation

Improving Bug Localization With Effective Contrastive Learning Representation

SAR Imaging Algorithm of Ocean Waves Based on Optimum Subaperture

Enhancing Bug Report Summaries Through Knowledge-Specific and Contrastive Learning Pre-Training

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