Mining software repositories

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The mining software repositories[citation needed] (MSR) field [1] analyzes the rich data available in software repositories, such as version control repositories, mailing list archives, bug tracking systems, issue tracking systems, etc. to uncover interesting and actionable information about software systems, projects and software engineering.

Definition[edit]

Herzig and Zeller define ”mining software archives” as a process to ”obtain lots of initial evidence” by extracting data from software repositories. Further they define ”data sources” as product-based artefacts like source code, requirement artefacts or version archives and claim that these sources are unbiased, but noisy and incomplete.[2]

Techniques[edit]

Coupled Change Analysis[edit]

The idea in coupled change analysis is that developers change code entities (e.g. files) together frequently for fixing defects or introducing new features. These couplings between the entities are often not made explicit in the code or other documents. Especially developers new on the project do not know which entities need to be changed together. Coupled change analysis aims to extract the coupling out of the version control system for a project. By the commits and the timing of changes, we might be able to identify which entities frequently change together. This information could then be presented to developers about to change one of the entities to support them in their further changes.[3]

Commit Analysis[edit]

There are many different kinds of commits in version control systems, e.g. bug fix commits, new feature commits, documentation commits, etc. To take data-driven decisions based on past commits, one needs to select subsets of commits that meet a given criterion. That can be done based on the commit message,[4] or based on the commit content.[5]

Documentation generation[edit]

It is possible to generate useful documentation from mining software repositories. For instance, Jadeite computes usage statistics and helps newcomers to quickly identify commonly used classes.[6] When one focuses on certain kinds of structured documentation such as subclassing directives, more advanced techniques can synthesize full sentences.[7]

Data & Tools[edit]

The primary mining data comes from version control systems. Early mining experiments were done on CVS repositories.[8] Then, researchers have extensively analyzed SVN repositories. [9] Now, Git repositories are dominant[10], but special care must be given to handle branches and forks[11].

Tools:

  • LibVCS4j is a Java library that allows existing tools to analyse the evolution of software systems by providing a common API for different version control systems and issue trackers.
  • pydriller is a Python Framework to analyse Git repositories.
  • Coming is a Java tool to search for patterns in past commits.[12]
  • CVSAnalY extracts information out of source code repository logs and stores it into a database.

See also[edit]

References[edit]

  1. ^ Working Conference on Mining Software Repositories, the main software engineering conference in the area
  2. ^ K. S. Herzig and A. Zeller, “Mining your own evidence,” in Making Software, pp. 517–529, Sebastopol, Calif., USA: O’Reilly, 2011.
  3. ^ Gall, H.; Hajek, K.; Jazayeri, M. (1998). "Detection of logical coupling based on product release history". Proceedings. International Conference on Software Maintenance (Cat. No. 98CB36272). pp. 190–198. CiteSeerX 10.1.1.199.7754. doi:10.1109/icsm.1998.738508. ISBN 978-0-8186-8779-2.
  4. ^ Hindle, Abram; German, Daniel M.; Godfrey, Michael W.; Holt, Richard C. (2009). "Automatic classication of large changes into maintenance categories". 2009 IEEE 17th International Conference on Program Comprehension. pp. 30–39. doi:10.1109/ICPC.2009.5090025. ISBN 978-1-4244-3998-0.
  5. ^ Martinez, Matias; Duchien, Laurence; Monperrus, Martin (2013). "Automatically Extracting Instances of Code Change Patterns with AST Analysis". 2013 IEEE International Conference on Software Maintenance. pp. 388–391. arXiv:1309.3730. doi:10.1109/ICSM.2013.54. ISBN 978-0-7695-4981-1.
  6. ^ Stylos, Jeffrey; Faulring, Andrew; Yang, Zizhuang; Myers, Brad A. (2009). "Improving API documentation using API usage information". 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). pp. 119–126. doi:10.1109/VLHCC.2009.5295283. ISBN 978-1-4244-4876-0.
  7. ^ Bruch, Marcel; Mezini, Mira; Monperrus, Martin (2010). "Mining subclassing directives to improve framework reuse". 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) (PDF). pp. 141–150. doi:10.1109/MSR.2010.5463347. ISBN 978-1-4244-6802-7.
  8. ^ Canfora, G.; Cerulo, L. (2005). "Impact Analysis by Mining Software and Change Request Repositories". 11th IEEE International Software Metrics Symposium (METRICS'05). p. 29. doi:10.1109/METRICS.2005.28. ISBN 978-0-7695-2371-2.
  9. ^ d'Ambros, Marco; Gall, Harald; Lanza, Michele; Pinzger, Martin (2008). "Analysing Software Repositories to Understand Software Evolution". Software Evolution. pp. 37–67. doi:10.1007/978-3-540-76440-3_3. ISBN 978-3-540-76439-7.
  10. ^ Kalliamvakou, Eirini; Gousios, Georgios; Blincoe, Kelly; Singer, Leif; German, Daniel M.; Damian, Daniela (2014). "The promises and perils of mining GitHub". Proceedings of the 11th Working Conference on Mining Software Repositories - MSR 2014. pp. 92–101. doi:10.1145/2597073.2597074. ISBN 9781450328630.
  11. ^ Biazzini, Marco; Monperrus, Martin; Baudry, Benoit (2014). "On Analyzing the Topology of Commit Histories in Decentralized Version Control Systems" (PDF). 2014 IEEE International Conference on Software Maintenance and Evolution. pp. 261–270. doi:10.1109/ICSME.2014.48. ISBN 978-1-4799-6146-7.
  12. ^ Martinez, Matias; Monperrus, Martin; Monperrus, Martin (2019). "Coming: A Tool for Mining Change Pattern Instances from Git Commits". 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). pp. 79–82. arXiv:1810.08532. doi:10.1109/ICSE-Companion.2019.00043. ISBN 978-1-7281-1764-5.