From run-time behavior to usage scenarios: an interaction-pattern mining approach

ABSTRACT

A key challenge facing IT organizations today is their evolution towards adopting e-business practices that gives rise to the need for reengineering their underlying software systems. Any reengineering effort has to be aware of the functional requirements of the subject system, in order not to violate the integrity of its intended uses. However, as software systems get regularly maintained throughout their lifecycle, the documentation of their requirements often become obsolete or get lost. To address this problem of "software requirements loss", we have developed an interaction-pattern mining method for the recovery of functional requirements as usage scenarios. Our method analyzes traces of the run-time system-user interaction to discover frequently recurring patterns; these patterns correspond to the functionality currently exercised by the system users, represented as usage scenarios. The discovered scenarios provide the basis for reengineering the software system into web-accessible components, each one supporting one of the discovered scenarios. In this paper, we describe IPM2, our interaction-pattern discovery algorithm, we illustrate it with a case study from a real application and we give an overview of the reengineering process in the context of which it is employed.

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning approaches

      • Classification and regression trees

  • Symbolic and algebraic manipulation

    • Symbolic and algebraic algorithms

• Information systems

  • Information systems applications

    • Data mining

• Theory of computation

  • Design and analysis of algorithms

    • Data structures design and analysis

      • Pattern matching