An In-depth Analysis of Duplicated Linux Kernel Bug Reports

Mu, Dong-Liang; Wu, Yuhang; Chen, Yueqi; Lin, Zhenpeng; Yu, C. X.; Xing, Xinyu; Wang, Gang

doi:10.14722/ndss.2022.24159

Cited by 3 publications

(2 citation statements)

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“…Experimental results showed a recall rate of 0.558 for the top 20 reports. Mu et al analyzed and summarized the possible causes of duplication [6], including input differences, thread interleaving, memory dynamics, different kernel versions, inline functions, and sanitizers. They designed targeted deduplication strategies based on these causes, such as using stable versions of the kernel, swapping the execution of proofs of concept, reducing the impact of thread interleaving and inline functions using stack traces, selecting specific sanitizers, and replacing the slab allocator with the slub allocator to mitigate the impact of memory dynamics.…”

Section: Information Retrieval Approaches For Deduplication and Triagementioning

confidence: 99%

“…However, there are many bugs that are duplicates; a survey claims that 12% of bug reports are duplicates, on average. As far as the bug reports generated by the Linux kernel are concerned, nearly ∼50% are duplicated [6]. There are many factors to consider in identifying bug deduplication, such as calling the same crash function, triggering crashes of the same type, etc.…”

Section: Introductionmentioning

confidence: 99%

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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: Information Retrieval Approaches For Deduplication and Triagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%