2019/03/06 by Ian Wright, I. P. Wright, Albert Ziegler +2
Computer Science · #FOS: Computer and information sciences #Open Source Software Innovations #Software Engineering (cs.SE) #Software Engineering Research #Software Reliability and Analysis Research #cs.SE
paper · pdf · doi:10.48550/arxiv.1903.02436
7 pages, 9 figures
arxiv created 2019/03/06 · openalex publication_date 2019/03/06 · arxiv updated 2019/03/07 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
We apply machine learning to version control data to measure the quantity of effort required to produce source code changes. We construct a model of a `standard coder' trained from examples of code changes produced by actual software developers together with the labor time they supplied. The effort of a code change is then defined as the labor hours supplied by the standard coder to produce that change. We therefore reduce heterogeneous, structured code changes to a scalar measure of effort derived from large quantities of empirical data on the coding behavior of software developers. The standard coder replaces traditional metrics, such as lines-of-code or function point analysis, and yields new insights into what code changes require more or less effort.