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Constraint-Based Behavioral Consistency of Evolving Software Systems

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Machine Learning for Dynamic Software Analysis: Potentials and Limits

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

Abstract

Any complex software system exhibits a tension between the technical perspective required for its realization and the user-level perspective. We term this the “how-what gap”, represented by the questions “how is a system implemented” vs. “what is its functionality/usage”. The normative, anticipated behavior of a software system as envisaged during its development and the de facto, observed behavior emerging after its continued operation tends to drift apart, resulting in behavioral inconsistency. We discuss how behavioral consistency in software systems can be captured in technical and formal terms, we sketch a possible tool chain that could support it, and we describe some of the research challenges that must be solved. Our main idea is to combine software analysis approaches represented by various forms of static analysis and formal verification with runtime verification, monitoring, and automata learning in order to optimally leverage the de facto observed behaviour of the deployed systems.

The research reported in this article has been partially supported by Deutsche Forschungsgemeinschaft (DFG) under grant nr. HA 2617/7-1.

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Notes

  1. 1.

    www.abs-models.org.

  2. 2.

    www.compugene.tu-darmstadt.de/compugene/welcome.

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Acknowledgments

We would like to thank Falk Howar and Karl Meinke and for their constructive criticism which helped to improve our paper.

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Authors and Affiliations

  1. Department of Computer Science, Technische Universität Darmstadt, 64289, Darmstadt, Germany

    Reiner Hähnle

  2. Department of Computer Science, Technische Universität Dortmund, 44221, Dortmund, Germany

    Bernhard Steffen

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  1. Reiner Hähnle
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Correspondence to Reiner Hähnle .

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Editors and Affiliations

  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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Hähnle, R., Steffen, B. (2018). Constraint-Based Behavioral Consistency of Evolving Software Systems. 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_8

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