Using temporal and semantic developer-level information to predict maintenance activity profiles

Stanislav Levin, Amiram Yehudai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Predictive models for software projects' characteristics have been traditionally based on project-level metrics, employing only little developer-level information, or none at all. In this work we suggest novel metrics that capture temporal and semantic developer-level information collected on a per developer basis. To address the scalability challenges involved in computing these metrics for each and every developer for a large number of source code repositories, we have built a designated repository mining platform. This platform was used to create a metrics dataset based on processing nearly 1000 highly popular open source GitHub repositories, consisting of 147 million LOC, and maintained by 30,000 developers. The computed metrics were then employed to predict the corrective, perfective, and adaptive maintenance activity profiles identified in previous works. Our results show both strong correlation and promising predictive power with R2 values of 0.83, 0.64, and 0.75. We also show how these results may help project managers to detect anomalies in the development process and to build better development teams. In addition, the platform we built has the potential to yield further predictive models leveraging developer-level metrics at scale.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages463-467
Number of pages5
ISBN (Electronic)9781509038060
DOIs
StatePublished - 12 Jan 2017
Event32nd IEEE International Conference on Software Maintenance and Evolution, ICSME 2016 - Raleigh, United States
Duration: 2 Oct 201610 Oct 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016

Conference

Conference32nd IEEE International Conference on Software Maintenance and Evolution, ICSME 2016
Country/TerritoryUnited States
CityRaleigh
Period2/10/1610/10/16

Keywords

  • Human factors
  • Mining software repositories
  • Predictive models
  • Software maintenance
  • Software metrics

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