Software Development Productivity Metrics, Measurements and Implications

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dc.contributor.advisorNorris, Boyana
dc.contributor.authorGupta, Shweta
dc.date.accessioned2018-09-06T22:02:19Z
dc.date.available2018-09-06T22:02:19Z
dc.date.issued2018-09-06
dc.description.abstractThe 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.en_US
dc.identifier.urihttps://hdl.handle.net/1794/23816
dc.language.isoen_US
dc.publisherUniversity of Oregon
dc.rightsAll Rights Reserved.
dc.subjectData analysisen_US
dc.subjectNatural language processingen_US
dc.subjectScientific softwareen_US
dc.subjectSoftware engineeringen_US
dc.subjectText miningen_US
dc.titleSoftware Development Productivity Metrics, Measurements and Implications
dc.typeElectronic Thesis or Dissertation
thesis.degree.disciplineDepartment of Computer and Information Science
thesis.degree.grantorUniversity of Oregon
thesis.degree.levelmasters
thesis.degree.nameM.S.

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