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Logic-Based Learning: Theory and Application

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Book cover Machine Learning for Dynamic Software Analysis: Potentials and Limits

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11026))

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Abstract

In recent years, research efforts have been directed towards the use of Machine Learning (ML) techniques to support and automate activities such as specification mining, risk assessment, program analysis, and program repair. The focus has largely been on the use of machine learning black box methods whose inference mechanisms are not easily interpretable and whose outputs are not declarative and guaranteed to be correct. Hence, they cannot readily be used to inform the elaboration and revision of declarative software models identified to be incorrect or incomplete. On the other hand, recent advances in ML have witnessed the emergence of new logic-based machine learning approaches that overcome such limitations and which have been proven to be well-suited for many software engineering tasks. In this chapter, we present a survey of the state-of-the-art of logic-based machine learning techniques, highlight their expressivity, define their different underlying semantics, and discuss their efficiency and the heuristics they adopt to guide the search for solutions. We then demonstrate the application of this type of machine learning to (declarative) specification refinement and revision as a complementary task to program analysis.

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Notes

  1. 1.

    This follows the standard Occam’s razor principle for which simpler hypotheses are better.

  2. 2.

    We omit a full definition of an abductive algorithm as this would fall outside the scope of this chapter. The reader is referred to [20] for further details.

  3. 3.

    The parameter \(\textit{max}^{h}_{i}\) is included as variables in a body literal can either be from input variables in the head literal or output variables in other body literals, giving \(\textit{max}^{h}_{i} + \textit{max}_{o} \times (d-1)\) as the total number of variables that can serve as a inputs to a body literal.

  4. 4.

    We focus here on specifications with a single stable model.

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Acknowledgement

We would like to acknowledge Duangtida Athakravi, Krysia Broda, Domenico Corapi, Tim Kimber, Jeff Kramer, Jiefei Ma, Oliver Ray and Sebastián Uchitel for their contributions to the material presented in this Chapter.

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  1. Department of Computing, Imperial College London, 180 Queen’s Gate, London, UK

    Dalal Alrajeh & Alessandra Russo

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  1. Dalal Alrajeh
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Correspondence to Dalal Alrajeh .

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  1. The Open University, Milton Keynes, United Kingdom

    Amel Bennaceur

  2. Technische Universität Darmstadt, Darmstadt, Germany

    Reiner Hähnle

  3. KTH Royal Institute of Technology, Stockholm, Sweden

    Karl Meinke

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Alrajeh, D., Russo, A. (2018). Logic-Based Learning: Theory and Application. In: Bennaceur, A., Hähnle, R., Meinke, K. (eds) Machine Learning for Dynamic Software Analysis: Potentials and Limits. Lecture Notes in Computer Science(), vol 11026. Springer, Cham. https://doi.org/10.1007/978-3-319-96562-8_9

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