Duplicate Bug Report detection using Named Entity Recognition

Zheng, Wei; Li, Yunfan; Wu, Xiaoxue; Cheng, Jingyuan

doi:10.1016/j.knosys.2023.111258

Cited by 1 publication

(3 citation statements)

References 47 publications

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“…The used state acronyms are defined in Table 3. As compared with the presented profiles, PL P (P1) and PL P (MongoDB), PL P (Log4J2) comprises a lower number of paths (99) with a lower number of states (2)(3)(4)(5)(6)(7)(8)(9). Two-and three-state paths are dominant (54.2% and 32.5% issue coverage).…”

Section: )mentioning

confidence: 86%

“…There exist a wide variety of approaches to automatically detect duplicate bug reports, such as text mining their descriptions and using similarity measures and some other statistics [5]. The precision of detecting duplicate bug reports can be improved by converting unstructured textual descriptions into structural data [4].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Data collected in software repositories are the basis for diverse practical and research studies in the software engineering domain. Here, we can distinguish the following aspects considered in the literature on software repositories: the detection of duplicate issues [4,5], bug prediction and issue classification [6,7], bug triaging [8], and issue report textual analysis [9,10]. Most papers related to software bugs focus on a single selected problem considering coarse-grained data from repositories.…”

Section: Introductionmentioning

confidence: 99%

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Graph-Driven Exploration of Issue Handling Schemes in Software Projects

Dobrzyński,

Sosnowski

2024

Applied Sciences

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The Issue Tracking System (ITS) repositories are rich sources of software development documentation that are useful in assessing the status and quality of software projects. An original model is proposed for tracing issue handling activities and their impact on project progress. As opposed to classical data mining of software repositories, we consider fine-grained features of issues which provide a better insight into project evolution. A thorough analysis of repository contents allows us to define useful metrics for characterizing issue handling schemes. These metrics are derived from the introduced graph model and developed original data mining algorithms targeting timing, issue flow progress and project actor activity aspects. This study is associated with issue processing states and their sequences (handling paths), leading to problem resolution. The introduced taxonomy of issue processing schemes facilitates the creation of a pertinent knowledge database and the identification of both bad (anomalies) and good practices. The proposed approach is illustrated with experimental results related to a representative set of ITS project repositories. These results enhance experts’ knowledge of the project and can be used for correct decision-making actions. They reveal weak points in project development and possible directions for improvement.

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