GiLA: GitHub label analyzer

Izquierdo, Javier Luis Cánovas; Cosentino, Valerio; Rolandi, Belen; Bergel, Alexandre; Cabot, Jordi

doi:10.1109/saner.2015.7081860

Cited by 26 publications

(14 citation statements)

References 7 publications

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“…To cope with these problems, GitHub also offers a customizable labeling system, which can be used by developers to mark and manage issue reports. In particular, labels can give immediate clues about the issues (e.g., what sort of topic the issue is about, what development task the issue is related to, or what priority the issue has) and are also useful for project administrators, since they can serve both as classification and filtering mechanism, thus facilitating the managing of the project [8]. However, manually assigning labels to issues is a labor-intensive and time-consuming task for project managers [5].…”

Section: Introductionmentioning

confidence: 99%

Ticket Tagger: Machine Learning Driven Issue Classification

Kallis

Sorbo

Canfora

et al. 2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Software maintenance is crucial for software projects evolution and success: code should be kept up-to-date and error-free, this with little effort and continuous updates for the end-users. In this context, issue trackers are essential tools for creating, managing and addressing the several (often hundreds of) issues that occur in software systems. A critical aspect for handling and prioritizing issues involves the assignment of labels to them (e.g., for projects hosted on GitHub), in order to determine the type (e.g., bug report, feature request and so on) of each specific issue. Although this labeling process has a positive impact on the effectiveness of issue processing, the current labeling mechanism is scarcely used on GitHub. In this demo, we introduce a tool, called Ticket Tagger, which leverages machine learning strategies on issue titles and descriptions for automatically labeling GitHub issues. Ticket Tagger automatically predicts the labels to assign to issues, with the aim of stimulating the use of labeling mechanisms in software projects, this to facilitate the issue management and prioritization processes. Along with the presentation of the tool's architecture and usage, we also evaluate its effectiveness in performing the issue labeling/classification process, which is critical to help maintainers to keep control of their workloads by focusing on the most critical issue tickets.

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Section: Introductionmentioning

confidence: 99%

Ticket Tagger: Machine Learning Driven Issue Classification

Kallis

Sorbo

Canfora

et al. 2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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“…GitHub is the world's most popular site for storing code [2] and thus is a popular place for software engineering research. Researchers have analyzed GitHub data to see how software engineers track issues [7], [10], [37], resolve bugs [54], use pull requests [61], [66], and even investigate gender bias in open-source projects [57]. Due to GitHub's research popularity, researchers have created tools such as GHTorrent [31] and Boa [13] to assist others, and Google maintains a snapshot of open-source repositories in BigQuery [28], [36].…”

Section: Related Workmentioning

confidence: 99%

How Bad Can It Git? Characterizing Secret Leakage in Public GitHub Repositories

Meli

McNiece

Reaves

2019

Proceedings 2019 Network and Distributed System Security Symposium

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GitHub and similar platforms have made public collaborative development of software commonplace. However, a problem arises when this public code must manage authentication secrets, such as API keys or cryptographic secrets. These secrets must be kept private for security, yet common development practices like adding these secrets to code make accidental leakage frequent. In this paper, we present the first large-scale and longitudinal analysis of secret leakage on GitHub. We examine billions of files collected using two complementary approaches: a nearly six-month scan of real-time public GitHub commits and a public snapshot covering 13% of open-source repositories. We focus on private key files and 11 high-impact platforms with distinctive API key formats. This focus allows us to develop conservative detection techniques that we manually and automatically evaluate to ensure accurate results. We find that not only is secret leakage pervasive-affecting over 100,000 repositories-but that thousands of new, unique secrets are leaked every day. We also use our data to explore possible root causes of leakage and to evaluate potential mitigation strategies. This work shows that secret leakage on public repository platforms is rampant and far from a solved problem, placing developers and services at persistent risk of compromise and abuse.

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“…Recently, many studies have been conducted on the integrated GitHub features and tools, to investigate their adoption and influences. In particular, issue‐tracking systems, an important helper for assisting developers in development and maintenance, have been investigated by many researches . Bissyande et al investigated the use of issue‐tracking systems from 100 000 projects to shed light to many research questions on the involvement of users and developers in reporting issues.…”

Section: Preliminariesmentioning

confidence: 99%

“…They found that even if the label mechanism is scarcely used, using labels favors the resolution of issues. They also developed a tool GiLA to generate a set of visualizations, which aims to facilitate the analysis of issues in a project depending on their label‐based categorization . Recently, Zhang et al presented a mixed‐methods study of the relationship between the practice of issue linking and issue resolution in the Rails ecosystems of open‐source projects and found that linking across projects will not retard issue resolution, but it is associated with more discussion.…”

Section: Preliminariesmentioning

confidence: 99%

GitHub's milestone tool: A mixed‐methods analysis on its use

Zhang

Wang

et al. 2019

J Software Evolu Process

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Social coding site GitHub provides developers with many management tools to facilitate project maintenance and developer collaboration. Milestone tool, in particular, plays an important role in organizing and tracking progress on groups of issues or pull requests in a project. However, few research has analyzed the milestone tool, even though it has been used in practice for a long time. In this paper, we want to address this literature gap and present an ongoing work aimed at investigating the use of the milestone tool in GitHub open‐source projects. We conduct a mixed‐methods analysis in a large‐scale dataset of GitHub projects, to help developers gain some insights into the milestone tool, including its usage, benefits, and limitations. We quantitatively investigate the basic adoption of milestone tool and its correlation with project properties. We also survey developers to understand the reasons for using milestone tool or not and their perceptions of the milestone tool. We find that certain types of projects use milestone tool more than others. Adopting the milestone tool is associated with more commits, more releases, and more project popularity, but the current milestone tool also has some limitations. These observations can then be forwarded to the GitHub community for follow‐up and can result in them potentially making a better milestone tool.

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GiLA: GitHub label analyzer

Cited by 26 publications

References 7 publications

Ticket Tagger: Machine Learning Driven Issue Classification

Ticket Tagger: Machine Learning Driven Issue Classification

How Bad Can It Git? Characterizing Secret Leakage in Public GitHub Repositories

GitHub's milestone tool: A mixed‐methods analysis on its use

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