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A changeset-based approach to assess source code density and developer efficacy
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA ; DSIQ)ORCID iD: 0000-0003-1173-5187
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA ; DSIQ)ORCID iD: 0000-0002-7565-3714
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-0835-823X
2018 (English)In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE , 2018, p. 220-221Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The productivity of a (team of) developer(s) can be expressed as a ratio between effort and delivered functionality. Several different estimation models have been proposed. These are based on statistical analysis of real development projects; their accuracy depends on the number and the precision of data points. We propose a data-driven method to automate the generation of precise data points. Functionality is proportional to the code size and Lines of Code (LoC) is a fundamental metric of code size. However, code size and LoC are not well defined as they could include or exclude lines that do not affect the delivered functionality. We present a new approach to measure the density of code in software repositories. We demonstrate how the accuracy of development time spent in relation to delivered code can be improved when basing it on net-instead of the gross-size measurements. We validated our tool by studying ca. 1,650 open-source software projects.

Place, publisher, year, edition, pages
IEEE , 2018. p. 220-221
Series
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Keywords [en]
Software Repositories, Clone Detection, Source code density, Effort estimation
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-79016DOI: 10.1145/3183440.3195105ISI: 000450109000080Scopus ID: 2-s2.0-85049691648ISBN: 978-1-4503-5663-3 (print)OAI: oai:DiVA.org:lnu-79016DiVA, id: diva2:1268519
Conference
40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-08-29Bibliographically approved

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Hönel, SebastianEricsson, MorganLöwe, WelfWingkvist, Anna

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