Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm. (October 2018)

• Record Type: Journal Article
• Authors: Sangaiah, Arun Kumar; Samuel, Oluwarotimi Williams; Li, Xiong; Abdel-Basset, Mohamed; Wang, Haoxiang
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 833
• Page End: 846
• Publication Date: 2018-10
• Subjects: Project risk -- Project performance -- Fuzzy DEMATEL -- FMCDM -- TODIM; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.07.022
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Effective prioritization of software project risks play an important role in determining whether the project will be successful in terms of performance characteristics or not. In this context, this study aims at hybridizing fuzzy multi criteria decision making approaches for the development of an assessment framework that can be used to efficiently identify and rank notable software project risks will aid decision making during the lifecycle of a software product. Specifically, for the assessment of project risk, we have integrated fuzzy Decision Making Trial and Evaluation Laboratory, Fuzzy Multi-Criteria Decision Making and TODIM (an acronym in Portuguese for Interactive and Multiple Attribute Decision Making) approaches. The proposed methodology is used for providing an effective rating mechanism to measure software risk factors. The effectiveness of the proposed approach results have been compared with earlier methods for the assessment of software project risks. The comparison was done based on the OMRON data set of 40 software projects. Evaluation results reveal that the integration of fuzzy approaches could be effective and as well accurate compared to existing approaches for evaluating software project risks.