S-DABT: Schedule and Dependency-aware Bug Triage in open-source bug tracking systems

Jahanshahi, Hadi; Çevik, Mücahit

doi:10.1016/j.infsof.2022.107025

Cited by 10 publications

(11 citation statements)

References 19 publications

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“…The developers are chosen according to their technical expertise scores in relevant topics. Jahanshahi and Cevik [2] introduce a framework that finds the suitability of developers with an SVM model. Then, the framework uses integer programming to assign the bugs to developers according to the schedules of the developers and the dependencies of the bugs.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, automatic bug assignment based on software bug reports is an essential issue for software development. Most of the papers [2,3,4,5,6] published for the software bug report assignment task work on open-source repositories since they are easy to access. On the other hand, few papers [7,8,9] focus on closed-source software repositories.…”

Section: Introductionmentioning

confidence: 99%

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Bug Report Classification with Ensemble Learning for Closed-Source Software

Yılmaz,

Öztürk,

Köksal

2023

Preprint

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This paper introduces a set of datasets grouped under the name Turkish Software Bug Reports (TSBR), which comprises commercial software bug reports from a closed-source project. We investigate and report the statistical properties and classification difficulty of the TSBR datasets. We employ various methods from the text classification literature to apply several classification tasks related to software development on the TSBR datasets. The methods we employ include traditional machine learning (ML) methods such as k-nearest neighbors (KNN) and random forest (RF); sequential deep learning (DL) models such as gated recurrent unit (GRU) and convolutional neural network (CNN); transformer-based language models; and ensembles of the employed models. Our work is among the first efforts in automated bug report classification literature that uses ensembles of DL models.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bug Report Classification with Ensemble Learning for Closed-Source Software

Yılmaz,

Öztürk,

Köksal

2023

Preprint

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“…Bug triage has another connotation in some cases: setting reducing the bug fix time cost as the primary optimization point, regardless of the similarity between bugs. For example, the goal of [8] was to distribute bugs to the right developers at the right time. Considering random errors that may occur at any time and the specific reality of developers who plan to change at any time, this work is based on the Markov decision process model (MDP), using approximate dynamic programming (ADP) to minimize entropy, with the optimization goal of minimizing bug repair time decision making.…”

Section: Reportermentioning

confidence: 99%

“…The concept of uncertainty can be quantified using entropy. The mutual information between X and Y can be calculated using Equation (8), where p(x, y) represents the joint probability of x and y occurring simultaneously.…”

Section: Feature Extraction and Selectionmentioning

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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“…However, with over 200 million open-source projects and 1.2 billion issues hosted on the GitHub platform worldwide, there are a large number of software defects that need to be fixed every day. Manually assigning developers to software defects by managers alone can be a tedious and time-consuming task [9][10][11][12]. To streamline this process, researchers have conducted studies on the task of assigning or recommending developers [13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Developer Assignment Method for Software Defects Based on Related Issue Prediction

Liu,

Zhang,

Liu

et al. 2024

Mathematics

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The open-source software platform hosts a large number of software defects, and the task of relying on administrators to manually assign developers is often time consuming. Thus, it is crucial to determine how to assign software defects to appropriate developers. This paper presents DARIP, a method for assigning developers to address software defects. First, the correlation between software defects and issues is considered, predicting related issues for each defect and comprehensively calculating the textual characteristics of the defect using the BERT model. Second, a heterogeneous collaborative network is constructed based on the three development behaviors of developers: reporting, commenting, and fixing. The meta-paths are defined based on the four collaborative relationships between developers: report–comment, report–fix, comment–comment, and comment–fix. The graph-embedding algorithm metapath2vec extracts developer characteristics from the heterogeneous collaborative network. Then, a classifier based on a deep learning model calculates the probability assigned to each developer category. Finally, the assignment list is obtained according to the probability ranking. Experiments on a dataset of 20,280 defects from 9 popular projects show that the DARIP method improves the average of the Recall@5, the Recall@10, and the MRR by 31.13%, 21.40%, and 25.45%, respectively, compared to the state-of-the-art method.

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S-DABT: Schedule and Dependency-aware Bug Triage in open-source bug tracking systems

Cited by 10 publications

References 19 publications

Bug Report Classification with Ensemble Learning for Closed-Source Software

Bug Report Classification with Ensemble Learning for Closed-Source Software

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

Developer Assignment Method for Software Defects Based on Related Issue Prediction

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