دانلود رایگان مقاله انگلیسی مدیریت ریسک سریع با استفاده از عوامل نرم افزاری - اشپرینگر 2018

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 management 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 where software agents are used to support identification, assessment and monitoring of risk. It demonstrates the interaction between agents, agents’ compliance with designated rules and how agents can react to changes in project environment data. The results, demonstrated using case studies, show that agents are of use for detecting risk and reacting dynamically to changes in project environment thus, help to minimize the human effort in managing risk.

6 Conclusion

In this paper we presented a novel approach to manage risk in agile projects. The work offers contributions in two areas (1) on the use of case studies for assessing new methods and tools and provides an example of how student teams can be used to gather information not feasible in industrial settings. (2) on the use of agents to semi-autonomic ally manage software risk.

This work provides several significant investigations on the problems and issues in risk management specifically in agile projects. The development of the ART model and tool support has been demonstrated to help by at least reducing the problems previously identified with risk management. The approach is necessarily supported by a prototype tool which has been shown to manage risks in example agile projects. The role of risk management in iterative and agile processes has to date been neglected but this model integrates risk management model with agile methods in a way that does not bloat the agile process.

This approach however, to the authors’ knowledge and understanding has never been applied in risk management, especially with the specific aim of reduction of human effort. In addition, the resulting risk management process is naturally lightweight since each software agent is design to achieve a designated goal i.e. to identify, assess, prioritize or monitor risk. This paper has led to use designated software agents to facilitate the risk management process. Therefore, this work demonstrates the potential of autonomous computing being applied to risk management where software agents have been used to assist the human oriented and complex risk management process. In future, this work aimed to comprehend the physical implementation of the ART model and tool support, where there is a need to integrate this with existing Agile Project Management tools, perhaps as a plug-in, so that automated risk management can be fully realised. This would allow more practical risk management while a project runs in the foreground, software agents are in the background ready to manage emerging risks.