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An Introduction to Recommendation Systems in Software Engineering

  • Martin P. Robillard
  • Robert J. Walker
Chapter

Abstract

Software engineering is a knowledge-intensive activity that presents many information navigation challenges. Information spaces in software engineering include the source code and change history of the software, discussion lists and forums, issue databases, component technologies and their learning resources, and the development environment. The technical nature, size, and dynamicity of these information spaces motivate the development of a special class of applications to support developers: recommendation systems in software engineering (RSSEs), which are software applications that provide information items estimated to be valuable for a software engineering task in a given context. In this introduction, we review the characteristics of information spaces in software engineering, describe the unique aspects of RSSEs, present an overview of the issues and considerations involved in creating, evaluating, and using RSSEs, and present a general outlook on the current state of research and development in the field of recommendation systems for highly technical domains.

Keywords

Source Code Software Engineering Recommendation System Information Space Recommendation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Arnold, R.S., Bohner, S.A.: Impact analysis: Towards a framework for comparison. In: Proceedings of the Conference on Software Maintenance, pp. 292–301 (1993). DOI 10.1109/ICSM.1993.366933Google Scholar
  2. 2.
    Brandt, J., Guo, P.J., Lewenstein, J., Dontcheva, M., Klemmer, S.R.: Two studies of opportunistic programming: Interleaving web foraging, learning, and writing code. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 1589–1598 (2009). DOI 10.1145/1518701.1518944Google Scholar
  3. 3.
    Čubranić, D., Murphy, G.C., Singer, J., Booth, K.S.: Hipikat: A project memory for software development. IEEE Trans. Software Eng. 31(6), 446–465 (2005). DOI 10.1109/TSE.2005.71CrossRefGoogle Scholar
  4. 4.
    Dagenais, B., Ossher, H., Bellamy, R.K., Robillard, M.P.: Moving into a new software project landscape. In: Proceedings of the ACM/IEEE International Conference on Software Engineering, pp. 275–284 (2010)Google Scholar
  5. 5.
    Dagenais, B., Robillard, M.P.: Creating and evolving developer documentation: Understanding the decisions of open source contributors. In: Proceedings of the ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 127–136 (2010). DOI 10.1145/1882291.1882312Google Scholar
  6. 6.
    Duala-Ekoko, E., Robillard, M.P.: Asking and answering questions about unfamiliar APIs: An exploratory study. In: Proceedings of the ACM/IEEE International Conference on Software Engineering, pp. 266–276 (2012)Google Scholar
  7. 7.
    Hemmati, H., Nadi, S., Baysal, O., Kononenko, O., Wang, W., Holmes, R., Godfrey, M.W.: The MSR cookbook: Mining a decade of research. In: Proceedings of the International Working Conference on Mining Software Repositories, pp. 343–352 (2013). DOI 10.1109/MSR.2013.6624048Google Scholar
  8. 8.
    Hill, W.C., Hollan, J.D., Wroblewski, D.A., McCandless, T.: Edit wear and read wear. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 3–9 (1992). DOI 10.1145/142750.142751Google Scholar
  9. 9.
    Holmes, R., Walker, R.J.: Customized awareness: Recommending relevant external change events. In: Proceedings of the ACM/IEEE International Conference on Software Engineering, pp. 465–474 (2010). DOI 10.1145/1806799.1806867Google Scholar
  10. 10.
    Kersten, M., Murphy, G.C.: Mylar: A degree-of-interest model for IDEs. In: Proceedings of the International Conference on Aspect-Oriented Software Deveopment, pp. 159–168 (2005). DOI 10.1145/1052898.1052912Google Scholar
  11. 11.
    Ko, A.J., Myers, B.A., Coblenz, M.J., Aung, H.H.: An exploratory study of how developers seek, relate, and collect relevant information during software maintenance tasks. IEEE Trans. Software Eng. 32(12), 971–987 (2006). DOI 10.1109/TSE.2006.116CrossRefGoogle Scholar
  12. 12.
    Kononenko, O., Dietrich, D., Sharma, R., Holmes, R.: Automatically locating relevant programming help online. In: Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 127–134 (2012). DOI 10.1109/VLHCC.2012.6344497Google Scholar
  13. 13.
    Mockus, A., Herbsleb, J.D.: Expertise Browser: A quantitative approach to identifying expertise. In: Proceedings of the ACM/IEEE International Conference on Software Engineering, pp. 503–512 (2002). DOI 10.1145/581339.581401Google Scholar
  14. 14.
    Murphy-Hill, E., Jiresal, R., Murphy, G.C.: Improving software developers’ fluency by recommending development environment commands. In: Proceedings of the ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 42:1–42:11 (2012). DOI 10.1145/2393596.2393645Google Scholar
  15. 15.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, New York (2011). DOI  10.1007/978-0-387-85820-3_1 CrossRefGoogle Scholar
  16. 16.
    Robillard, M.P., Bodden, E., Kawrykow, D., Mezini, M., Ratchford, T.: Automated API property inference techniques. IEEE Trans. Software Eng. 39(5), 613–637 (2013). DOI 10.1109/TSE.2012.63CrossRefGoogle Scholar
  17. 17.
    Robillard, M.P., Coelho, W., Murphy, G.C.: How effective developers investigate source code: An exploratory study. IEEE Trans. Software Eng. 30(12), 889–903 (2004). DOI 10.1109/TSE.2004.101CrossRefGoogle Scholar
  18. 18.
    Robillard, M.P., Walker, R.J., Zimmermann, T.: Recommendation systems for software engineering. IEEE Software 27(4), 80–86 (2010). DOI 10.1109/MS.2009.161CrossRefGoogle Scholar
  19. 19.
    Sillito, J., Murphy, G.C., De Volder, K.: Asking and answering questions during a programming change task. IEEE Trans. Software Eng. 34(4), 434–451 (2008). DOI 10.1109/TSE.2008.26CrossRefGoogle Scholar
  20. 20.
    Weiser, M.: Program slicing. IEEE Trans. Software Eng. 10(4), 352–357 (1984). DOI 10.1109/TSE.1984.5010248CrossRefzbMATHGoogle Scholar
  21. 21.
    Zimmermann, T., Weißgerber, P.: Preprocessing CVS data for fine-grained analysis. In: Proceedings of the International Workshop on Mining Software Repositories, pp. 2–6 (2004)Google Scholar
  22. 22.
    Zimmermann, T., Weißgerber, P., Diehl, S., Zeller, A.: Mining version histories to guide software changes. IEEE Trans. Software Eng. 31(6), 429–445 (2005). DOI 10.1109/TSE.2005.72CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.McGill UniversityMontréalCanada
  2. 2.University of CalgaryCalgaryCanada

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