A framework to support behavioral design pattern detection from software execution data

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    6 Citations (Scopus)

    Abstract

    The detection of design patterns provides useful insights to help understanding not only the code but also the design and architecture of the underlying software system. Most existing design pattern detection approaches and tools rely on source code as input. However, if the source code is not available (e.g., in case of legacy software systems) these approaches are not applicable anymore. During the execution of software, tremendous amounts of data can be recorded. This provides rich information on the runtime behavior analysis of software. This paper presents a general framework to detect behavioral design patterns by analyzing sequences of the method calls and interactions of the objects that are collected in software execution data. To demonstrate the applicability, the framework is instantiated for three well-known behavioral design patterns, i.e., observer, state and strategy patterns. Using the open-source process mining toolkit ProM, we have developed a tool that supports the whole detection process. We applied and validated the framework using software execution data containing around 1000.000 method calls generated from both synthetic and open-source software systems.

    Original languageEnglish
    Title of host publicationENASE 2018 - Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering
    EditorsErnesto Damiani, George Spanoudakis, Leszek Maciaszek
    PublisherSCITEPRESS-Science and Technology Publications, Lda.
    Pages65-76
    Number of pages12
    ISBN (Electronic)978-989-758-300-1
    DOIs
    Publication statusPublished - 1 Jan 2018
    Event13th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2018 - Funchal, Madeira, Portugal
    Duration: 23 Mar 201824 Mar 2018

    Conference

    Conference13th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2018
    CountryPortugal
    CityFunchal, Madeira
    Period23/03/1824/03/18

    Keywords

    • Behavioral Design Pattern
    • General Framework
    • Pattern Instance Detection
    • Software Execution Data

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