A multi-level dataset of linux kernel patchwork

Xu, Yulin; Zhou, Minghui

doi:10.1145/3196398.3196475

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…As illustrated in Figure 2, this link between patch emails in LKML and commits in LK is nonexistent and thus requires an indirect provider. We circumvent this problem by probing the patchwork service 4 according to the process described in Xu and Zhou's paper [40]. Overall, we collected a total of 27411 records matching our patch email criteria.…”

Section: A Activity-based Analysis Of Developer Activity Datamentioning

confidence: 99%

Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI

Sanchez¹,

Hitaj²

2022

Preprint

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Open-source is frequently described as a driver for unprecedented communication and collaboration, and the process works best when projects support teamwork. Yet, open-source cooperation processes in no way protect project contributors from considerations of trust, power, and influence. Indeed, achieving the level of trust necessary to contribute to a project and thus influence its direction is a constant process of change, and developers take many different routes over many communication channels to achieve it. We refer to this process of influenceseeking and trust-building as trust ascendancy.This paper describes a methodology for understanding the notion of trust ascendancy and introduces the capabilities that are needed to localize trust ascendancy operations happening over open-source projects. Much of the prior work in understanding trust in open-source software development has focused on a static view of the problem using different forms of quantity measures. However, trust ascendancy is not static, but rather adapts to changes in the open-source ecosystem in response to new input. This paper is the first attempt to articulate and study these signals from a dynamic view of the problem. In that respect, we identify related work that may help illuminate research challenges, implementation tradeoffs, and complementary solutions. Our preliminary results show the effectiveness of our method at capturing the trust ascendancy developed by individuals involved in a well-documented 2020 social engineering attack. Our future plans highlight research challenges and encourage cross-disciplinary collaboration to create more automated, accurate, and efficient ways to model and then track trust ascendancy in open-source projects.

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Section: A Activity-based Analysis Of Developer Activity Datamentioning

confidence: 99%

Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI

Sanchez¹,

Hitaj²

2022

Preprint

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“…This may include time zone conversion or other actions, and presents a single, unified view of time for analysis and further filtering. This cleaning technique is specifically used by Claes and Mäntylä [37], Xu and Zhou [38], and Baysal, Holmes, and Godfrey [39].…”

Section: Identified Filtering and Cleaning Techniquesmentioning

confidence: 99%

Escaping the Time Pit: Pitfalls and Guidelines for Using Time-Based Git Data

Flint¹,

Chauhan²,

Dyer³

2021

Preprint

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Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents the first survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 690 technical track and data papers published in MSR 2004-2020, we saw at least 35% of papers utilized time-based data. We then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty commit timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories.

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Standing on shoulders or feet? An extended study on the usage of the MSR data papers

et al. 2020

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The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. Objective: Examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Method: Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. Results: We found that 65% of the data papers have been used in other studies, with a longtail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. Users of data sets appreciate high data quality and are discouraged by lack of replicability of data set construction. Data related to machine learning or derived from the manufacturing sector are two suggestions of the respondents for future data papers. Conclusions: Data papers have provided the foundation for a significant number of studies,

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A multi-level dataset of linux kernel patchwork

Cited by 10 publications

References 9 publications

Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI

Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI

Escaping the Time Pit: Pitfalls and Guidelines for Using Time-Based Git Data

Standing on shoulders or feet? An extended study on the usage of the MSR data papers

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