First EAI International Conference on Computer Science and Engineering

Research Article

Lightweight Risk Management: The Development of Agile Risk Tool Agents

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  • @INPROCEEDINGS{10.4108/eai.27-2-2017.152346,
        author={Edzreena Edza Odzaly and Des Greer and Darryl Stewart},
        title={Lightweight Risk Management: The Development of Agile Risk Tool Agents},
        proceedings={First EAI International Conference on Computer Science and Engineering},
        publisher={EAI},
        proceedings_a={COMPSE},
        year={2017},
        month={3},
        keywords={Software Risk Management Agile Risks Agile Projects Software Agents.},
        doi={10.4108/eai.27-2-2017.152346}
    }
    
  • Edzreena Edza Odzaly
    Des Greer
    Darryl Stewart
    Year: 2017
    Lightweight Risk Management: The Development of Agile Risk Tool Agents
    COMPSE
    EAI
    DOI: 10.4108/eai.27-2-2017.152346
Edzreena Edza Odzaly1,*, Des Greer2,*, Darryl Stewart2,*
  • 1: Queens University Belfast, University Road, Belfast, Northern Ireland, UNITED KINGDOM, Universiti Teknologi MARA, Faculty of Computer and Mathematical Sciences, 77300 Merlimau, Malacca, MALAYSIA
  • 2: Queens University Belfast, University Road, Belfast, Northern Ireland, UNITED KINGDOM
*Contact email: eodzaly01@qub.ac.uk, des.greer@qub.ac.uk, dw.stewart@qub.ac.uk

Abstract

Risk management is an important process in Software Engineering. However, it can be perceived as somewhat contrary to the more lightweight processes used in Agile methods. Thus an appropriate and realistic risk man-agement model is required as well as tool support that minimizes human effort. We propose the use of software agents to carry out risk management tasks and make use of the data collected from the project environment to detect risks. This paper describes the underlying risk management model in an Agile Risk Tool (ART) where software agents are used to support identification, assess-ment and monitoring of risk. It demonstrates the interaction between agents, agents’ compliance with the designated rules and how agents react to changes in project environment data. The result shows that agents are of use for detect-ing risk and reacting dynamically to changes in project environment thus, help to minimize the human effort in managing risk.