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How to Build a Recommendation System for Software Engineering

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Software Engineering (LASER 2013, LASER 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8987))

Abstract

Software developers must interact with large amounts of different types of information and perform many different activities to build a software system. To ease the finding of information and hone workflows, there has been growing interest in building recommenders that are intended to help software developers work more effectively. Building an effective recommender requires a deep understanding of the problem that is the target of a recommender, analysis of different aspects of the approach taken to perform the recommendations and design and evaluation of the mechanisms used to present recommendations to a developer. In this chapter, we outline the different steps that must be taken to develop an effective recommender system to aid software development.

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Notes

  1. 1.

    www.eclipse.org, verified 15/11/14.

  2. 2.

    stackoverflow.com, verified 18/02/15.

  3. 3.

    http://www.eclipse.org/jdt/overview.php#JDT_Text, verified 02/14/15.

  4. 4.

    http://msdn.microsoft.com/en-us/library/hcw1s69b.aspx, verified 02/14/15.

  5. 5.

    QuickFix is an Eclipse tool that can be triggered to get context sensitive support.

  6. 6.

    Also called the ramp-up problem [97].

  7. 7.

    Counted from an eclipse cheat-sheet at http://de.scribd.com/doc/60629986/Eclipse-Keyboard-Shortcuts.

  8. 8.

    Following the argument that too many context switches between the use of different tools can significantly impact the productivity of a developer [73].

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Acknowledgements

We thank the organizers of the Laser 2014 Summer School for an invigorating week of discussion, which has carried through the writing of this chapter. We also thank the anonymous reviewer for the valuable comments and suggestions to improve the quality of the paper.

The work presented in this paper was partially funded by NSERC and by the German Federal Ministry of Education and Research (BMBF) within the Software Campus projects KaVE (grant no. 01IS12054), and IndRe (grant no. 01IS12057).

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Authors and Affiliations

  1. TU Darmstadt, Darmstadt, Germany

    Sebastian Proksch

  2. TU München, Munich, Germany

    Veronika Bauer

  3. UBC, Vancouver, Canada

    Gail C. Murphy

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  1. ETH Zurich , Zürich, Switzerland

    Bertrand Meyer

  2. ETH Zurich , Zürich, Switzerland

    Martin Nordio

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Proksch, S., Bauer, V., Murphy, G.C. (2015). How to Build a Recommendation System for Software Engineering. In: Meyer, B., Nordio, M. (eds) Software Engineering. LASER LASER 2013 2014. Lecture Notes in Computer Science(), vol 8987. Springer, Cham. https://doi.org/10.1007/978-3-319-28406-4_1

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