Representation of Developer Expertise in Open Source Software

Dey, Tapajit; Karnauch, Andrey; Mockus, Audris

doi:10.1109/icse43902.2021.00094

Cited by 17 publications

(6 citation statements)

References 49 publications

(64 reference statements)

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“…Other efforts [64][65][66][67][68][69][70][71] have been done in context of recommendation projects for developers and vice-versa. For instance, Liu et al 72 recommend software features from the users' perspective for designers of the project.…”

Section: Related Workmentioning

confidence: 99%

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

2023

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Summary Software developers must collaborate at all stages of the software life‐cycle to create successful complex software systems. To enable this collaboration, social coding platforms, for example, GitHub, include an increasing number of tools to support collaboration. However, for large projects with hundreds of dynamic developers, such as several successful open–source projects, it can be complex to find developers with the same interest and familiarity and thus, gain suitable collaborations and new insights. In this context, resources and efforts may be wasted, discouraging many developers from contributing. Moreover, it can be costly to manage many contributions, which is another challenge for the maintainer who wants to take advantage of this small, timid, but valuable contribution made by a volunteer developer in a short time. In this context, this paper presents an empirical study aiming to evaluate two strategies to recommend collaborators based on co‐changed files. Inspired in the TF–IDF (Term Frequency–Inverse Document Frequency) weighting scheme established in the Information Retrieval field, these strategies first estimate the importance of relevant files modified by developers and use these estimates to represent each developer “profile”. As a second step, they estimate the similarity between developers using the Cosine metric, providing top‐ranked developers according to this measure as recommendations. We evaluated these strategies based on an extensive survey with 102 real–world developers. We observed that developers have interest and familiarity with the co‐changed files for all strategies evaluated. These considerations are of relevance because many opportunities for contributions to the project are linked to coding. Thus, theses results may indicate one less barrier for improving collaboration among developers. Overall, the strategies present an acceptance rate of up to 81%, contributing to the discovery of further collaborators.

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Section: Related Workmentioning

confidence: 99%

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

2023

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“…Dey et al [15] proposed Skill Space to conceptualize domain expertise. The proposed method can be applied to individual developers, projects, programming languages, and APIs.…”

Section: Representation Of Domain Expertisementioning

confidence: 99%

Balanced Knowledge Distribution among Software Development Teams -- Observations from Open-Source and Closed-Source Software Development

Shafiq¹,

Mayr-Dorn²,

Mashkoor³

et al. 2022

Preprint

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In software development teams, developer turnover is among the primary reasons for project failures as it leads to a great void of knowledge and strain for the newcomers. Unfortunately, no established methods exist to measure how knowledge is distributed among development teams. Knowing how this knowledge evolves and is owned by key developers in a project helps managers reduce risks caused by turnover. To this end, this paper introduces a novel, realistic representation of domain knowledge distribution: the ConceptRealm. To construct the ConceptRealm, we employ a latent Dirichlet allocation model to represent textual features obtained from 300k issues and 1.3M comments from 518 open-source projects. We analyze whether the newly emerged issues and developers share similar concepts or how aligned the developers' concepts are with the team over time. We also investigate the impact of leaving members on the frequency of concepts. Finally, we evaluate the soundness of our approach to closed-source software, thus allowing the validation of the results from a practical standpoint. We find out that the ConceptRealm can represent the high-level domain knowledge within a team and can be utilized to predict the alignment of developers with issues. We also observe that projects exhibit many keepers independent of project maturity and that abruptly leaving keepers harm the team's concept familiarity.

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“…Since we have the first commit which introduced this blob from the b2a map, we collected the projects for these commits using c2P map and then the author list of these projects using p2a map. We used the approach outlined by [11] for author ID disambiguation to merge all of the different IDs belonging to one developer together, which is a common occurrence, as discussed in [12].…”

Section: B Data Collection and Analysismentioning

confidence: 99%

Tracking Hackathon Code Creation and Reuse

Imam¹,

Dey²

2021

Preprint

Self Cite

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Background: Hackathons have become popular events for teams to collaborate on projects and develop software prototypes. Most existing research focuses on activities during an event with limited attention to the evolution of the code brought to or created during a hackathon. Aim: We aim to understand the evolution of hackathon-related code, specifically, how much hackathon teams rely on pre-existing code or how much new code they develop during a hackathon. Moreover, we aim to understand if and where that code gets reused. Method: We collected information about 22,183 hackathon projects from DEVPOST-a hackathon database -and obtained related code (blobs), authors, and project characteristics from the WORLD OF CODE. We investigated if code blobs in hackathon projects were created before, during, or after an event by identifying the original blob creation date and author, and also checked if the original author was a hackathon project member. We tracked code reuse by first identifying all commits containing blobs created during an event before determining all projects that contain those commits. Result: While only approximately 9.14% of the code blobs are created during hackathons, this amount is still significant considering time and member constraints of such events. Approximately a third of these code blobs get reused in other projects. Conclusion: Our study demonstrates to what extent pre-existing code is used and new code is created during a hackathon and how much of it is reused elsewhere afterwards. Our findings help to better understand code reuse as a phenomenon and the role of hackathons in this context and can serve as a starting point for further studies in this area.

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Representation of Developer Expertise in Open Source Software

Cited by 17 publications

References 49 publications

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

Balanced Knowledge Distribution among Software Development Teams -- Observations from Open-Source and Closed-Source Software Development

Tracking Hackathon Code Creation and Reuse

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