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research-article

Identifying key developers using artifact traceability graphs

Authors:

H. Alperen Çetin,

Eray TüzünAuthors Info & Claims

PROMISE 2020: Proceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering

Pages 51 - 60

https://doi.org/10.1145/3416508.3417116

Published: 08 November 2020 Publication History

Abstract

Developers are the most important resource to build and maintain software projects. Due to various reasons, some developers take more responsibility, and this type of developers are more valuable and indispensable for the project. Without them, the success of the project would be at risk. We use the term key developers for these essential and valuable developers, and identifying them is a crucial task for managerial decisions such as risk assessment for potential developer resignations. We study key developers under three categories: jacks, mavens and connectors. A typical jack (of all trades) has a broad knowledge of the project, they are familiar with different parts of the source code, whereas mavens represent the developers who are the sole experts in specific parts of the projects. Connectors are the developers who involve different groups of developers or teams. They are like bridges between teams.

To identify key developers in a software project, we propose to use traceable links among software artifacts such as the links between change sets and files. First, we build an artifact traceability graph, then we define various metrics to find key developers. We conduct experiments on three open source projects: Hadoop, Hive and Pig. To validate our approach, we use developer comments in issue tracking systems and demonstrate that the identified key developers by our approach match the top commenters up to 92%.

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Cited By

Shafiq SMayr‐Dorn CMashkoor AEgyed A(2024)Balanced knowledge distribution among software development teams—Observations from open‐ and closed‐source software developmentJournal of Software: Evolution and Process10.1002/smr.2655Online publication date: 13-Feb-2024
https://doi.org/10.1002/smr.2655
Brasil-Silva RSiqueira FHong JBures MPark JCerny T(2022)Metrics to quantify software developer experienceProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507304(1562-1569)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507304
Fucci DAlégroth EAxelsson T(2022)When traceability goes awryJournal of Systems and Software10.1016/j.jss.2022.111389192:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.jss.2022.111389
Show More Cited By

Index Terms

Identifying key developers using artifact traceability graphs
1. Software and its engineering
  1. Software creation and management
    1. Collaboration in software development
      1. Programming teams

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Published In

cover image ACM Conferences

PROMISE 2020: Proceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering

November 2020

80 pages

ISBN:9781450381277

DOI:10.1145/3416508

General Chair:
Leandro Minku,
Program Chairs:
Tim Menzies,
Mei Nagappan

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 November 2020

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PROMISE '20

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SIGSOFT

PROMISE '20: 16th International Conference on Predictive Models and Data Analytics in Software Engineering

November 8 - 9, 2020

Virtual, USA

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Cited By

Shafiq SMayr‐Dorn CMashkoor AEgyed A(2024)Balanced knowledge distribution among software development teams—Observations from open‐ and closed‐source software developmentJournal of Software: Evolution and Process10.1002/smr.2655Online publication date: 13-Feb-2024
https://doi.org/10.1002/smr.2655
Brasil-Silva RSiqueira FHong JBures MPark JCerny T(2022)Metrics to quantify software developer experienceProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507304(1562-1569)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507304
Fucci DAlégroth EAxelsson T(2022)When traceability goes awryJournal of Systems and Software10.1016/j.jss.2022.111389192:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.jss.2022.111389
Çetin HTüzün E(2022)Analyzing developer contributions using artifact traceability graphsEmpirical Software Engineering10.1007/s10664-022-10129-227:3Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10664-022-10129-2

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