IEEE Access (Jan 2020)

A Novel Case Base Reasoning and Frequent Pattern Based Decision Support System for Mitigating Software Risk Factors

  • Muhammad Asif,
  • Jamil Ahmed

DOI
https://doi.org/10.1109/ACCESS.2020.2999036
Journal volume & issue
Vol. 8
pp. 102278 – 102291

Abstract

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Software risk management is crucial for the success of software project development. The existing literature has models for risk management, but is too complex to be used in practice. The information in the existing studies is scattered over different articles which makes it difficult to find relevant knowledge to establish relationship between risk factors and mitigations. This paper presents a novel model which identifies the relationship between risk factors and mitigations automatically by using intelligent Decision Support System (DSS). The proposed model has four steps. Firstly, the input of the system has been designed where risk factors and mitigations have been inputted into it. Secondly, rule based machine learning approach has been used for mining of associations between risks and mitigations. Thirdly, Case Based Reasoning (CBR) approach has been used to determine the previous cases as rules. Finally, automated rules have been generated to develop an intelligent DSS to mitigate the software risks. The proposed technique copes with the highly cited existing limitations of risk handling like, lack of generic DSS and intelligent relationship between software risks and mitigations. Automated rules have been discovered with a novel idea of CBR and frequent pattern. The proposed model is capable of mitigating upcoming risks in future. Star schema has been implemented to support our proposed DSS. Moreover, from highly cited literature 40 studies were identified from which 26 risk factors, 57 mitigations, 14 questions and 26 automated rules have been extracted. According to the validation of IT industry experts, the average of the effectiveness of DSS is 51-55%. The novelty of the proposed research is that it uses two state of the art methods (Rule Based Machine Learning and CBR) to identify software risk mitigations. The results of the proposed model show that the chances of risks in software development have been reduced significantly.

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