In this paper we optimized a software risk management method called Riskit ... Riskit is a method for risk management in software development projects [15].
IADIS International Conference Informatics 2008
USING UML AND FUZZY LOGIC IN OPTIMIZING RISK MANAGEMENT MODELING Farshad Kyoomarsi Islamic Azad University (Shahrekord)
Pooya Khosravyan Dehkordy
Mohammad Hosein Peiravy
Islamic Azad University (Shahrekord)
Islamic Azad University (Sarvestan)
Ehsan Heidary Islamic Azad University (Dorod)
ABSTRACT In this paper we optimized a software risk management method called Riskit method. First we change the Riskit analysis graph by fuzzy logic and then designed other diagrams and tables with UML like risk management cycle, process definition information template and etc. Finally we can optimize Riskit method with using this method. KEYWORDS Riskit, Fuzzy Logic, UML, Riskit Analysis Graph, Stakeholder, Goal.
1. INTRODUCTION Unanticipated problems frequently cause major problems to projects, such as cost overruns, schedule delays, quality problems, and missing functionality. To some degree these problems can be seen as signs of immaturity of our field and we should expect some improvements in our discipline as our methods and knowledge improve. However, as each software development project involves at least some degree of uniqueness and our technology changes continuously, uncertainty about the end results will always accompany software development. While we cannot remove risks from software development, we should learn to manage them better. Risks in software development were not addressed in detail until late 1980’s when Boehm [2] proposed and synthesized an approaches for software risk management. His work was complemented by Charette [5], and on these foundations recent advances in software risk management have produced well-documented approaches for risk management [8], several categories of risks have been identified [2,4], quantitative approaches for risk management have been proposed and used [1,3], and there are several software tools available for risk management. Furthermore, most commonly used software engineering standards [9,10] or assessment frameworks [11] require at least some form of risk management to take place.
2. RISKIT METHOD Riskit is a method for risk management in software development projects [15]. The main features of the method are its sound theoretical foundations and its focus on qualitative understanding of risks [16] before their possible quantification. Furthermore, the Riskit method provides a defined process for conducting risk management. The method is supported by various techniques and guidelines and the use of Riskit does not preclude the use of other risk management approaches.We using the Riskit method version 1.0 and try to correct its ambiguities. This method including these main charts for modeling risk management: Riskit Analysis Graph, Risk Management Process, Stakeholder-Goal Priority, Risk Prioritization, Risk Control Planning .
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ISBN: 978-972-8924-62-1 © 2008 IADIS
3. THE RISKIT ANALYSIS GRAPH The underlying conceptual model – or meta-model – of the Riskit Analysis Graph components is presented in Figure 1, using the UML [20, 21]. This meta-model represents the underlying, conceptual elements and their relationships. Each rectangle in the graph represents a risk element and each arrow describes the possible relationship between risk elements. The relationship arrow is read in the direction of the arrow. For example, the relationship between “risk factor” and “risk event” in Figure 1 should be read as “[a] risk factor influences [the] probability of [a] risk event” [13,14]. In our method relation between risk elements are deterministic but in the real world accident occurrence are probable between 0 and 1 therefore we need fuzzy logic concept [12, 16] to modeling the Riskit analysis graph. For this purpose we must design the Fuzzy Riskit Analysis Graph like this: Each connector must have a number between 0 and 1. Sum of input connector number to each risk element must less or equal 1. Sum of output connector number from each risk element must less or equal 1. With these cases we can design the Fuzzy Riskit Analysis Graph like Figure1.
Figure 1. A Conceptual View of the Elements in the Fuzzy Riskit Analysis Graph. For example for Risk Event: E11+E12+E13
