Towards Prioritizing GitHub Issues

Dhasade, Akash; Venigalla, Akhila Sri Manasa; Chimalakonda, Sridhar

doi:10.1145/3385032.3385052

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…For this reason, we decided to not recreate the methods presented in the works of Izadi et al [32] and Noei et al [36]. As a consequence, our comparison was only based on the method described in Dhasade et al [31].…”

Section: B Evaluating the Resultsmentioning

confidence: 99%

“…Successes on PR prioritization triggered further research. For example, Dhasade et al [31] proposed an algorithm for predicting issue priority. This approach is based on the concept of "issue hotness."…”

Section: Related Workmentioning

confidence: 99%

“…However, this approach does not consider the entire backlog but focuses only on individual tasks. This limitation may be the underlying cause of low predictive power, as stated in [31] or of the inability to achieve high precision [32]. Furthermore, Plaksin [37] showed that it is possible to reasonably predict (with MAP of 0.51%) the order of task execution based on the textual description of an issue.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Automatically Prioritizing Tasks in Software Development

Bugayenko¹,

Farina

Kruglov

et al. 2023

IEEE Access

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Context: Within the domain of managing software development teams, effective task prioritization is a critical responsibility that should not be underestimated, particularly for larger organizations with significant backlogs. Current approaches primarily rely on predicting task priority without considering information about other tasks, potentially resulting in inaccurate priority predictions.Objective: This paper presents the benefits of considering the entire backlog when prioritizing tasks.Method: We employ an iterative approach using particle swarm optimization to optimize a linear model with various preprocessing methods to determine the optimal model for task prioritization within a backlog. Results:The findings of our study demonstrate the usefulness of constructing a task prioritization model based on complete information from the backlog. Conclusion:The method proposed in our study can serve as a valuable resource for future researchers and can also facilitate the development of new tools to aid IT management teams.

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Section: B Evaluating the Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Automatically Prioritizing Tasks in Software Development

Bugayenko¹,

Farina

Kruglov

et al. 2023

IEEE Access

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“…It also includes information regarding the severity which previous work has greatly exploited. A recent study proposed by Dhasade et al [9] has addressed the need for priority prediction models in GitHub. However, they use both LDA and classifiers to predict hotness of issue reports.…”

Section: Issue Report Prioritizationmentioning

confidence: 99%

Predicting the Objective and Priority of Issue Reports in Software Repositories

Izadi¹,

Akbari²,

Heydarnoori³

2020

Preprint

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Proper documentation plays an important role in successful software management and maintenance. Software repositories such as GitHub host a large number of software entities. Developers collaboratively discuss, implement, use, and share these entities. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and finally, to document the highlight of their team's effort. An issue report can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage them manually. GitHub provides labels for tagging issues, as a means of issue management. However, about half of the issues in GitHub's top 1000 repositories do not have any labels.In this work, we aim at automating the process of managing issue reports for software teams. We propose a two-stage approach to predict both the objective behind opening an issue and its priority level using feature engineering methods and stateof-the-art text classifiers. We train and evaluate our models in both project-based and cross-project settings. The latter approach provides a generic prediction model applicable for any unseen software project or projects with little historical data. Our proposed approach can successfully predict the objective and priority level of issue reports with 83% and 75% accuracy, respectively.

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“…Such sites allow any user to clone a project, modify it, use it, and provide feedback in form of a bug report. With the increasing number of users relying on these open‐source projects hosted by GitHub, the number of reported issues has rapidly grown to outnumber contributors for open‐source repositories 3 . This indicates the need to derive a system that can relieve the burden of developers to resolve these issues.…”

Section: Introductionmentioning

confidence: 99%

CrossFix: Resolution of GitHub issues via similar bugs recommendation

Tan

Yan

2023

J Software Evolu Process

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SummaryWith the increasing popularity of Open‐Source Software (OSS), the number of GitHub issues reported daily in these OSS projects has been growing rapidly. To resolve these issues, developers need to spend time and effort in debugging and fixing these issues. Meanwhile, a recent approach shows that similar bugs exist across different projects, and one could use the GitHub issues from a different project for finding new bugs for a related project. To locate similar bugs for our approach, we first conduct a study of similar bugs in GitHub. Our study redefines similar bugs as bugs that share the (1) same libraries, (2) same functionalities, (3) same reproduction steps, (4) same configurations, (5) same outcomes, or (6) same errors. Moreover, our study revealed the usefulness of similar bugs in helping developers to find more contexts about the bug and fixing. Based on our study, we design CrossFix, a tool that automatically suggests relevant GitHub issues based on an open GitHub issue. The suggested GitHub issues may contain solutions written in natural language or pull requests that help developers in resolving the given issue. Our evaluation on 249 open issues from Java and Android projects shows that CrossFix could suggest similar bugs to help developers in debugging and fixing.

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Towards Prioritizing GitHub Issues

Cited by 12 publications

References 15 publications

Automatically Prioritizing Tasks in Software Development

Automatically Prioritizing Tasks in Software Development

Predicting the Objective and Priority of Issue Reports in Software Repositories

CrossFix: Resolution of GitHub issues via similar bugs recommendation

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