2019/03/06 by I. P. Wright, Albert Ziegler, Wright, Ian +1
Computer Science · #FOS: Computer and information sciences #Open Source Software Innovations #Software Engineering (cs.SE) #Software Engineering Research #Software Reliability and Analysis Research
paper · pdf · doi:10.48550/arxiv.1903.02436
openalex publication_date 2019/03/06 · 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\neffort required to produce source code changes. We construct a model of a\n`standard coder' trained from examples of code changes produced by actual\nsoftware developers together with the labor time they supplied. The effort of a\ncode change is then defined as the labor hours supplied by the standard coder\nto produce that change. We therefore reduce heterogeneous, structured code\nchanges to a scalar measure of effort derived from large quantities of\nempirical data on the coding behavior of software developers. The standard\ncoder replaces traditional metrics, such as lines-of-code or function point\nanalysis, and yields new insights into what code changes require more or less\neffort.\n