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A changeset-based approach to assess source code density and developer efficacy

Published: 27 May 2018 Publication History

Abstract

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.

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    cover image ACM Conferences
    ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings
    May 2018
    231 pages
    ISBN:9781450356633
    DOI:10.1145/3183440
    • Conference Chair:
    • Michel Chaudron,
    • General Chair:
    • Ivica Crnkovic,
    • Program Chairs:
    • Marsha Chechik,
    • Mark Harman
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 27 May 2018

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    Author Tags

    1. clone detection
    2. effort estimation
    3. software repositories
    4. source code density

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