Survey Based Classification of Bug Triage Approaches

Yadav, Asmita; Singh, Sandeep Kumar

doi:10.11591/aptikom.j.csit.94

Cited by 6 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the coverage of these articles is not comprehensive, and they do not specifically focus on runtime information for classifica-tion. Yadav et al surveyed classification methods based on machine learning, profiles, or metadata, comparing and discussing the pros and cons of different approaches [16]. They concluded that no single method has advantages in all dimensions and provided insights into potential research points such as the cold activation problem and load balancing.…”

Section: Related Surveymentioning

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

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.

show abstract

Section: Related Surveymentioning

confidence: 99%