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Supporting change impact analysis for intelligent agent systems

Journal Article


Abstract


  • Software maintenance and evolution is an important and lengthy phase in the

    software life-cycle which can account for as much as two-thirds of the total

    software development costs. Intelligent agent technology has evolved rapidly

    over the past few years as evidenced by the increasing number of agent systems

    in many different domains. Intelligent agent systems with their distinct

    characteristics and behaviours introduce new problems in software maintenance.

    However, in contrast to a substantial amount of work in providing

    methodologies for analysing, designing and implementing agent-based systems,

    there has been very little work on maintenance and evolution of agent

    systems. A critical issue in software maintenance and evolution is change

    impact analysis: estimating the potential effects of changes before they are

    made as an agent system evolves. In this paper, we propose two distinct approaches

    to change impact analysis for the well-known and widely-developed

    Belief-Desire-Intention agent systems. On the one hand, our static technique

    computes the impact of a change by analysing the source code and identifying

    various dependencies within the agent system. On the other hand,

    our dynamic technique builds a representation of an agent’s behaviour by

    analyzing its execution traces which consist of goals and plans, and uses this

    representation to estimate impacts. We have implemented both techniques

    and in this paper we also report on the experimental results that compare

    their effectiveness in practice.

Publication Date


  • 2013

Citation


  • Dam, H. Khanh. & Ghose, A. (2013). Supporting change impact analysis for intelligent agent systems. Science of Computer Programming, 78 (9), 1728-1750.

Scopus Eid


  • 2-s2.0-84878853923

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/941

Has Global Citation Frequency


Number Of Pages


  • 22

Start Page


  • 1728

End Page


  • 1750

Volume


  • 78

Issue


  • 9

Place Of Publication


  • Netherlands

Abstract


  • Software maintenance and evolution is an important and lengthy phase in the

    software life-cycle which can account for as much as two-thirds of the total

    software development costs. Intelligent agent technology has evolved rapidly

    over the past few years as evidenced by the increasing number of agent systems

    in many different domains. Intelligent agent systems with their distinct

    characteristics and behaviours introduce new problems in software maintenance.

    However, in contrast to a substantial amount of work in providing

    methodologies for analysing, designing and implementing agent-based systems,

    there has been very little work on maintenance and evolution of agent

    systems. A critical issue in software maintenance and evolution is change

    impact analysis: estimating the potential effects of changes before they are

    made as an agent system evolves. In this paper, we propose two distinct approaches

    to change impact analysis for the well-known and widely-developed

    Belief-Desire-Intention agent systems. On the one hand, our static technique

    computes the impact of a change by analysing the source code and identifying

    various dependencies within the agent system. On the other hand,

    our dynamic technique builds a representation of an agent’s behaviour by

    analyzing its execution traces which consist of goals and plans, and uses this

    representation to estimate impacts. We have implemented both techniques

    and in this paper we also report on the experimental results that compare

    their effectiveness in practice.

Publication Date


  • 2013

Citation


  • Dam, H. Khanh. & Ghose, A. (2013). Supporting change impact analysis for intelligent agent systems. Science of Computer Programming, 78 (9), 1728-1750.

Scopus Eid


  • 2-s2.0-84878853923

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/941

Has Global Citation Frequency


Number Of Pages


  • 22

Start Page


  • 1728

End Page


  • 1750

Volume


  • 78

Issue


  • 9

Place Of Publication


  • Netherlands