A scheduling-driven approach to efficiently assign bug fixing tasks to developers

Etemadi, Vahid; Bushehrian, Omid; Akbari, Reza; Robles, Gregório

doi:10.1016/j.jss.2021.110967

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…number of tossings happening within a software project) [29][30][31]. A similar direction is that of scheduling bugs, that is, evaluating all possible orders with which bugs should be resolved [32]. The main rationale in this case lies in taking into account not only the profiles but also the workload of the developers.…”

Section: Related Workmentioning

confidence: 99%

Automated issue assignment using topic modelling on Jira issue tracking data

Diamantopoulos,

Saoulidis,

Symeonidis

2023

IET Software

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As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non-textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non-textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment.

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Section: Related Workmentioning

confidence: 99%

Automated issue assignment using topic modelling on Jira issue tracking data

Diamantopoulos,

Saoulidis,

Symeonidis

2023

IET Software

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“…The method used an embedded greedy search that operates over the schedules to explore more parts of search space. Their approach showed good accuracy as well as reduced the bug-fixing time [25].…”

Section: Related Workmentioning

confidence: 99%

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

2022

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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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