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Recommendation Systems in Software Engineering pp 421–453Cite as

Recommending Program Transformations

Automating Repetitive Software Changes

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Abstract

Adding features and fixing bugs in software often require systematic edits which are similar but not identical changes to multiple code locations. Finding all relevant locations and making the correct edits is a tedious and error-prone process. This chapter presents several state-of-the art approaches to recommending program transformation in order to automate repetitive software changes. First, it discusses programming-by-demonstration (PBD) approaches that automate repetitive tasks by inferring a generalized action script from a user’s recorded actions. Second, it presents edit location suggestion approaches that only recommend candidate edit locations but do not apply necessary code transformations. Finally, it describes program transformation approaches that take code examples or version histories as input, automatically identify candidate edit locations, and apply context awareness, customization program transformations to generate a new program version. In particular, this chapter describes two concrete example-based program transformation approaches in detail, Sydit and Lase. These two approaches are selected for an in-depth discussion, because they handle the issue of both recommending change locations and applying transformations, and they are specifically designed to update programs as opposed to regular text documents. The chapter is then concluded with open issues and challenges of recommending program transformations.

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Acknowledgments

We thank Kathryn McKinley who contributed to designing and evaluating key algorithms of Sydit and Lase. We also thank her for our fruitful collaboration and numerous discussions on example-based program transformation approaches. We thank John Jacobellis who is a main contributor of developing the Lase Eclipse plugin. This work was supported in part by the National Science Foundation under grants CCF-1149391, CCF-1117902, SHF-0910818, and CCF-0811524 and by a Microsoft SEIF award.

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  1. The University of Texas at Austin, Austin, TX, USA

    Miryung Kim & Na Meng

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  1. Miryung Kim
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  2. Na Meng
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Correspondence to Miryung Kim .

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Editors and Affiliations

  1. McGill University, Montréal, Québec, Canada

    Martin P. Robillard

  2. University of Hamburg, Hamburg, Germany

    Walid Maalej

  3. University of Calgary, Calgary, Alberta, Canada

    Robert J. Walker

  4. Microsoft Research, Redmond, Washington, USA

    Thomas Zimmermann

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Kim, M., Meng, N. (2014). Recommending Program Transformations. In: Robillard, M., Maalej, W., Walker, R., Zimmermann, T. (eds) Recommendation Systems in Software Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45135-5_16

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