Title:	DATA MINING WITH SOFTWARE INDUSTRY PROJECT DATA: A CASE STUDY
Author(s):	Topi Haapio , Tim Menzies
ISBN:	978-972-8924-97-3
Editors:	Hans Weghorn and Pedro Isaías
Year:	2009
Edition:	V II, 2
Keywords:	Case study, software industry, business intelligence, data mining, small data set.
Type:	Short Paper
First Page:	33
Last Page:	38
Language:	English
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Paper Abstract:	Increasingly, data mining is used to improve an organization’s software process quality, e.g. effort estimations. Data is collected from projects, and data miners are used to discover beneficial knowledge. Mining-suitable software project management data can, however, be difficult to collect, and results frequently in a small data set. This paper addresses the challenges with such a small data set, and how we overcame these challenges. The paper reports, as a case study, a data mining experiment that both failed, and succeeded. While the data did not support answers to the questions that prompted the experiment, we could find answers to other relating important business questions. We offer two conclusions. Firstly, it is important to control research expectations when setting up such a study since not all questions are supported by the available data. Secondly, it may be required to tune the questions to the data, and not the other way around. We offer this second conclusion cautiously since it runs counter to previous empirical software engineering recommendations. Nevertheless, we believe it may be a useful approach when studying real world software engineering data that may be limited inside, noisy or skewed by local factors.
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