An integrative framework for intelligent software project risk planning

  • Authors:

  • Yong Hu;Jianfeng Du;Xiangzhou Zhang;Xiaoling Hao;E. W. T. Ngai;Ming Fan;Mei Liu

  • Affiliations:

  • Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Sun Yat-sen University, Guangzhou 510006, PR China;Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Sun Yat-sen University, Guangzhou 510006, PR China;School of Business, Sun Yat-sen University, Guangzhou 510006, PR China;Shanghai University of Finance and Economics, Shanghai 200433, PR China;Department of Management and Marketing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China;Foster School of Business, University of Washington, Seattle, WA 353226, USA;Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA

  • Venue:
  • Decision Support Systems
  • Year:
  • 2013

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Abstract

Software projects have inherent uncertainties and risks. Social software projects suffer even more requirement changes and require more attention to risk management. Risk analysis and planning are complex, making it difficult to manage risks effectively through subjective judgment. At present, ample empirical research on intelligent decision-support models for risk analysis in software projects exists. However, to the best of our knowledge, empirical models for software project risk planning, or those related to integrative software risk analysis and planning are not available. Thus, the current study proposes an integrative framework for intelligent software project risk planning (IF-ISPRP) to help in minimizing the impacts of project risks and achieving a better foreseeable project outcome. IF-ISPRP includes two core components, namely, risk analysis module and risk planning module. The risk analysis module is to predict whether a project will be successful or not. The risk planning module is to produce a cost-minimal action set for risk control based on the risk analysis module. For integrative risk analysis and planning, we propose a novel many-to-many actionable knowledge discovery (MMAKD) method for complex risk planning. We also apply the framework on a social media platform project, Guangzhou Wireless City, and demonstrate how the model can generate a cost-minimal action set to mitigate the project risk. The risk-control actions found may help develop strategies on mitigating the risks of other social software projects. We hope that the proposed framework will provide an intelligent decision-support tool for project stakeholders to effectively control project risks by integrating risk analysis and planning.