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Automatic Detection of Incomplete Requirements Using Symbolic Analysis and Evolutionary Computation

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Search Based Software Engineering (SSBSE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10452))

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Abstract

The usefulness of a system specification depends on the completeness of the requirements specified. Unfortunately it is difficult to ensure a requirements specification includes all necessary requirements, especially when the system interacts with an unpredictable and often idealized environment. Worse yet, a single completeness counterexample may not clearly indicate the extent that incomplete requirements impacts the system or what range of environmental scenarios are affected. This paper introduces Ares-EC, a design-time approach for detecting incomplete requirements decomposition using a combination of evolutionary computation and symbolic analysis of hierarchical requirements models to detect a set of representative incompleteness counterexamples. We illustrate our approach by applying Ares-EC to a requirements model of an industry-based automotive adaptive cruise control system. Ares-EC is able to apply symbolic analysis and evolutionary computation to automatically detect diverse and representative sets of requirements incompleteness counterexamples at design time.

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Notes

  1. 1.

    Ares is the Greek god of war, especially the untamed aspects of war.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48823, USA

    Byron DeVries & Betty H. C. Cheng

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  1. Byron DeVries
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  2. Betty H. C. Cheng
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Correspondence to Byron DeVries .

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Editors and Affiliations

  1. North Carolina State University, Morgantown, West Virginia, USA

    Tim Menzies

  2. Department of Computer Science, University College London, London, United Kingdom

    Justyna Petke

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DeVries, B., Cheng, B.H.C. (2017). Automatic Detection of Incomplete Requirements Using Symbolic Analysis and Evolutionary Computation. In: Menzies, T., Petke, J. (eds) Search Based Software Engineering. SSBSE 2017. Lecture Notes in Computer Science(), vol 10452. Springer, Cham. https://doi.org/10.1007/978-3-319-66299-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-66299-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66298-5

  • Online ISBN: 978-3-319-66299-2

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