Analysis and Detection of Information Types of Open Source Software Issue Discussions

Arya, Deeksha M.; Wang, Wenting; Guo, Jin; Cheng, Jinghui

doi:10.48550/arxiv.1902.07093

Cited by 1 publication

(4 citation statements)

References 20 publications

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“…As shown in Figure 1, Jira allows users to assign distinct labels to issue links to indicate different types of links. Jira provides four default labels: relates to / relates to, duplicates / is duplicated by, blocks / is blocked by and clones / is cloned by 3 . Authorized project stakeholders can also create and customize new labels to express additional associations between issues according to their needs.…”

Section: A Issue Links and Their Labels For Apache Projectsmentioning

confidence: 99%

“…Textual features are heavily used in the literature to assess the existence of trace link [18], [20]. Term Frequency-Inverse Document Frequency (TF-IDF) model, one of the most common ways to make decisions on document relevancy, serves as a strong baseline when analyzing issue content [3], [44]. Intuitively, TF-IDF model capture how important a word is to each document in a collection of documents.…”

Section: A Text Encoding Using Issue Contentmentioning

confidence: 99%

“…In this work we experiment with the following supervised machine learning models due to their wide adoption in text mining tasks [48] and the improved results reported in recent work on software artifacts [3], [38].…”

Section: A Supervised Machine Learning Classificationmentioning

confidence: 99%

“…contains, is blocked by, is duplicated by. Software stakeholders rely on this information to identify all the tightly related issues and their particular relationships to perform different tasks such as prioritizing the new features, understanding the current status of development, communicating the progress towards completing certain goals [3], [11], just to name a few.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Issue Link Label Recovery and Prediction for Open Source Software

Nicholson¹,

Guo²

2021

Preprint

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Modern open source software development heavily relies on the issue tracking systems to manage their feature requests, bug reports, tasks, and other similar artifacts. Together, those "issues" form a complex network with links to each other. The heterogeneous character of issues inherently results in varied link types and therefore poses a great challenge for users to create and maintain the label of the link manually. The goal of most existing automated issue link construction techniques ceases with only examining the existence of links between issues. In this work, we focus on the next important question of whether we can assess the type of issue link automatically through a data-driven method. We analyze the links between issues and their labels used the issue tracking system for 66 open source projects. Using three projects, we demonstrate promising results when using supervised machine learning classification for the task of link label recovery with careful model selection and tuning, achieving F1 scores of between 0.56-0.70 for the three studied projects. Further, the performance of our method for future link label prediction is convincing when there is sufficient historical data. Our work signifies the first step in systematically manage and maintain issue links faced in practice.

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Section: A Issue Links and Their Labels For Apache Projectsmentioning

confidence: 99%