Software Development Productivity Metrics, Measurements and Implications

• Main Author: Gupta, Shweta
• Other Authors: Norris, Boyana
• Language: en_US
• Published: University of Oregon 2018
• Subjects: Data analysis; Natural language processing; Scientific software; Software engineering; Text mining
• Online Access: http://hdl.handle.net/1794/23816

The rapidly increasing capabilities and complexity of numerical software present a growing challenge to software development productivity. While many open source projects enable the community to share experiences, learn and collaborate; estimating individual developer productivity becomes more difficult as projects expand. In this work, we analyze some HPC software Git repositories with issue trackers and compute productivity metrics that can be used to better understand and potentially improve development processes. Evaluating productivity in these communities presents additional challenges because bug reports and feature requests are often done by using mailing lists instead of issue tracking, resulting in difficult-to-analyze unstructured data. For such data, we investigate automatic tag generation by using natural language processing techniques. We aim to produce metrics that help quantify productivity improvement or degradation over the projects lifetimes. We also provide an objective measurement of productivity based on the effort estimation for the developer's work.