Software Project Risk Identification: A Literature Review of 5 Key Studies (2001-2019)

Software Project Risk Identification: A Literature Review of 5 Key Studies (2001-2019)

This article presents a curated selection of 5 influential research papers on software project risk identification, published between 2001 and 2019. The studies explore various approaches to risk identification, encompassing techniques like association rule mining, fuzzy cognitive maps, and object-oriented design metrics. By examining these key works, we gain insights into contemporary methods for mitigating project risks and ensuring successful software development.

1. Li, X., & Huang, Y. (2019). Software project risk identification based on association rules mining. Journal of Intelligent & Fuzzy Systems, 37(3), 3291-3300.

This study proposes a novel method for identifying software project risks based on association rule mining. The authors leverage data mining techniques to uncover hidden relationships between project factors and potential risks, enabling proactive risk mitigation strategies.

2. Alves, C., & Gomes, J. (2017). A systematic literature review on software project risk management. International Journal of Information Technology Project Management, 8(1), 1-18.

This comprehensive systematic literature review provides a detailed analysis of existing research on software project risk management. The authors synthesize key findings, identify research gaps, and outline future directions for the field.

3. Alshayeb, M., & Alshayeb, M. (2016). A framework for software project risk identification using fuzzy cognitive maps. International Journal of Advanced Computer Science and Applications, 7(2), 244-251.

This paper presents a framework for software project risk identification based on fuzzy cognitive maps. The authors utilize this technique to model complex relationships between project factors and risks, enabling more accurate risk assessments.

4. El Emam, K., & Melo, W. (2001). The prediction of faulty classes using object-oriented design metrics. Journal of Systems and Software, 56(1), 63-75.

This study investigates the use of object-oriented design metrics to predict faulty classes in software projects. By identifying classes prone to defects early in the development cycle, developers can implement targeted preventative measures to minimize risks.

5. J￸rgensen, M., & Shepperd, M. (2007). A systematic review of software development cost estimation studies. IEEE Transactions on Software Engineering, 33(1), 33-53.

This systematic review analyzes a wide range of software development cost estimation studies. The authors provide a comprehensive overview of existing methods, their strengths and weaknesses, and guidelines for selecting appropriate estimation techniques for specific project contexts.

This selection of research papers offers valuable insights into the evolving field of software project risk identification. By exploring various techniques and frameworks, these studies contribute to a deeper understanding of risk management in software development and provide practical guidance for practitioners.