On Deriving Actions for Improving Cost Overrun by Applying Association Rule Mining to Industrial Project Repository
For software project management, it is very important to identify risk factors which make project into runaway. In this study, we propose a method to extract improvement action items for a software project by applying association rule mining to the software project repository for a metric of “cost overrun”. We first mine a number of association rules affecting cost overrun. We then group compatible rules, which include several common metrics having different values, from the mined rules and extract improvement action items of project improvement. In order to evaluate the applicability of our method, we applied our method to the project data repository collected from plural companies in Japan. The result of experiment showed that project improvement actions for cost overrun were semi-automatically extracted from the mined association rules. We can confirm feasibility of our method by comparing these actions with the results in the previous research.
Keywordsassociation rule mining project improvement actions cost overrun
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- 3.Software Engineering Center, Information-technology Promotion Agency (ed.): The 2006 White Paper on Software Development Projects (in Japanese). Nikkei Business Publications (2006)Google Scholar
- 4.Masticola, S.P.: A simple estimate of the cost of software project failures and the breakeven effectiveness of project risk management. In: Proceedings of the First International Workshop on The Economics of Software and Computation (ESC 2007), p. 6 (2007)Google Scholar
- 6.Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
- 7.Michail, A.: Data mining library reuse patterns using generalized association rules. In: Proceedings of the 22nd international conference on Software engineering, pp. 167–176 (2000)Google Scholar
- 11.Mitani, Y., Kikuchi, N., Matsumura, T., Ohsugi, N., Monden, A., Higo, Y., Inoue, K., Barker, M., Matsumoto, K.: A proposal for analysis and prediction for software projects using collaborative filtering, in-process measurements and a benchmarks database. In: Proc. of International Conference on Software Process and Product Measurement, pp. 98–107 (2006)Google Scholar
- 12.Ohsugi, N., Monden, A., Kikuchi, N., Barker, M.D., Tsunoda, M., Kakimoto, T., Matsumoto, K.: Is this cost estimate reliable? – the relationship between homogeneity of analogues and estimation reliability. In: Proc. of First International Symposium on Empirical Software Engineering and Measurement, pp. 384–392 (2007)Google Scholar
- 13.Furuyama, T., Kikuchi, N., Yasuda, M., Tsuruho, M.: Analysis of the factors that affect the performance of software projects (in Japanese). Trans. of Information Processing Society of Japan 48(8), 2608–2619 (2007)Google Scholar