One-Class Classification Based Bug Triage System to Assign a Newly Added Developer

Zaidi, Syed Farhan Alam; Lee, Chan-Gun

doi:10.1109/icoin50884.2021.9334002

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…Experimental results showed that although DABT had a decrease in triage accuracy, it significantly reduced the time required for bug fixes by less than 50%. Zaidi et al used one-class SVM to address the issue of new developers who cannot effectively participate in bug triage [111]. They trained an independent model for each developer to determine whether a bug can be assigned to them.…”

Section: Machine Learning Approaches For Deduplication and Triagementioning

confidence: 99%

A Survey on Bug Deduplication and Triage Methods from Multiple Points of View

Qian,

Zhang,

Nie

et al. 2023

Applied Sciences

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To address the issue of insufficient testing caused by the continuous reduction of software development cycles, many organizations maintain bug repositories and bug tracking systems to ensure real-time updates of bugs. However, each day, a large number of bugs is discovered and sent to the repository, which imposes a heavy workload on bug fixers. Therefore, effective bug deduplication and triage are of great significance in software development. This paper provides a comprehensive investigation and survey of the recent developments in bug deduplication and triage. The study begins by outlining the roadmap of the existing literature, including the research trends, mathematical models, methods, and commonly used datasets in recent years. Subsequently, the paper summarizes the general process of the methods from two perspectives—runtime information-based and bug report-based perspectives—and provides a detailed overview of the methodologies employed in relevant works. Finally, this paper presents a detailed comparison of the experimental results of various works in terms of usage methods, datasets, accuracy, recall rate, and F1 score. Drawing on key findings, such as the need to improve the accuracy of runtime information collection and refine the description information in bug reports, we propose several potential future research directions in the field, such as stack trace enrichment and the combination of new NLP models.

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Section: Machine Learning Approaches For Deduplication and Triagementioning

confidence: 99%

A Survey on Bug Deduplication and Triage Methods from Multiple Points of View

Qian,

Zhang,

Nie

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Moreover, these datasets are experimented with in the earlier studies [13], [15]. The Sun Firefox, JDT, Netbeans, GUO Firefox, and GCC datasets are publicly available here [44]. The rest of the datasets are available here [45].…”

Section: B Datasetsmentioning

confidence: 99%

On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

Dipongkor¹,

Islam

Hussain

et al. 2023

IEEE Access

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Automated bug report assignment is critical for large-scale software projects where reported bugs are frequent and expert developers are required to fix them on time. Finding an appropriate developer with the necessary skill sets and prior experience in fixing similar bugs is difficult and can be an expensive process, depending on the severity of the reported bug. To address this issue, researchers have proposed several machine learning and deep learning-based automated bug report assignment techniques that make use of historical data on reported bugs as well as fixer information. However, there is still room for improvement in the performance of these techniques. In this paper, we propose a novel deep learning-based approach that utilizes two sets of features from the reported bugs' textual data, namely contextual information and the occurrence of repeating keywords. We develop convolutional neural network and artificial neural network modules to mine these features. We then fuse these two sets of extracted features to assign a bug to an appropriate developer. We conduct extensive experiments on eight benchmark datasets of open-source, realworld software projects to assess the effectiveness of our approach. The experimental results demonstrate that our information fusion-based approach outperforms previous models and improves automated bug report assignment. Furthermore, we debug the errors of our proposed model and publish all source code so that future researchers can contribute to this problem.

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“…In the literature, we did not find an appropriate bug triage solution based on deep learning techniques that can add Journal of Sensors newly hired developers to the existing model without retraining from scratch. Zaidi et al [6] aimed to solve this problem using a one-class classification technique, which is a machine learning technique. They used a one-class support vector machine (SVM) classifier for one-class classification and trained a separate classifier for each developer.…”

Section: Introductionmentioning

confidence: 99%

Toward an Effective Bug Triage System Using Transformers to Add New Developers

2022

Self Cite

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As defects become more widespread in software development and advancement, bug triaging has become imperative for software testing and maintenance. The bug triage process assigns an appropriate developer to a bug report. Many automated and semiautomated systems have been proposed in the last decade, and some recent techniques have provided direction for developing an effective triage system. However, these techniques still require improvement. Another open challenge related to this problem is adding new developers to the existing triage system, which is challenging because the developers have no listed triage history. This paper proposes a transformer-based bug triage system that uses bidirectional encoder representation from transformers (BERT) for word representation. The proposed model can add a new developer to the existing system without building a training model from scratch. To add new developers, we assumed that new developers had a triage history created by a manual triager or human triage manager after learning their skills from the existing developer history. Then, the existing model was fine-tuned to add new developers using the manual triage history. Experiments were conducted using datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was used as a criterion for assessment. The experimental outcome suggests that the proposed triage system is better than other word-embedding-based triage methods for the bug triage problem. Additionally, the proposed method performs the best for adding new developers to an existing bug triage system without requiring retraining using a whole dataset.

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One-Class Classification Based Bug Triage System to Assign a Newly Added Developer

Cited by 10 publications

References 16 publications

A Survey on Bug Deduplication and Triage Methods from Multiple Points of View

A Survey on Bug Deduplication and Triage Methods from Multiple Points of View

On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

Toward an Effective Bug Triage System Using Transformers to Add New Developers

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