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M. Mo?ina, J. Demsar, M. Kattan and B. Zupan, “Nomograms for Visualization of Naive Bayesian Classifier,” Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’04), Vol. 3202, 2004, pp. 337-348.

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• TITLE: Case-Based Reasoning for Reducing Software Development Effort

  AUTHORS: Adam Brady, Tim Menzies, Oussama El-Rawas, Ekrem Kocaguneli, Jacky W. Keung

  KEYWORDS: Software Effort Estimation, Case Based Reasoning, Effort Estimation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.3 No.11, November 29, 2010

  ABSTRACT: How can we best find project changes that most improve project estimates? Prior solutions to this problem required the use of standard software process models that may not be relevant to some new project. Also, those prior solutions suffered from limited verification (the only way to assess the results of those studies was to run the recommendations back through the standard process models). Combining case-based reasoning and contrast set learning, the W system requires no underlying model. Hence, it is widely applicable (since there is no need for data to conform to some software process models). Also, W’s results can be verified (using holdout sets). For example, in the experiments reported here, W found changes to projects that greatly reduced estimate median and variance by up to 95% and 83% (respectively).