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Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?

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Search-Based Software Engineering (SSBSE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11664))

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Abstract

In this keynote I introduce the use of Predictive Analytics for Software Engineering (SE) and then focus on the use of search-based heuristics to tackle long-standing SE prediction problems including (but not limited to) software development effort estimation and software defect prediction. I review recent research in Search-Based Predictive Modelling for SE in order to assess the maturity of the field and point out promising research directions. I conclude my keynote by discussing best practices for a rigorous and realistic empirical evaluation of search-based predictive models, a condicio sine qua non to facilitate the adoption of prediction models in software industry practices.

This paper provides an outline of the keynote talk given by Dr. Federica Sarro at SSBSE 2019, with pointers to the literature for details of the results covered.

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Authors and Affiliations

  1. Department of Computer Science, University College London, London, UK

    Federica Sarro

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  1. Federica Sarro
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Correspondence to Federica Sarro .

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Editors and Affiliations

  1. SnT/University of Luxembourg, Luxembourg, Luxembourg

    Shiva Nejati

  2. University of South Carolina, Columbia, SC, USA

    Gregory Gay

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Sarro, F. (2019). Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?. In: Nejati, S., Gay, G. (eds) Search-Based Software Engineering. SSBSE 2019. Lecture Notes in Computer Science(), vol 11664. Springer, Cham. https://doi.org/10.1007/978-3-030-27455-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-27455-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27454-2

  • Online ISBN: 978-3-030-27455-9

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