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http://bura.brunel.ac.uk/handle/2438/1556
Title: | Predicting with sparse data |
Authors: | Shepperd, MJ Cartwright, MH |
Keywords: | Prediction;Software project effort;Expert judgement;Empirical data;Sparse data;Cost estimation |
Issue Date: | 2001 |
Publisher: | IEEE Computer Society |
Citation: | IEEE Transactions on Software Engineering 27(11): 1014-1022, Nov 2001 |
Abstract: | It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction. |
URI: | http://bura.brunel.ac.uk/handle/2438/1556 |
DOI: | https://doi.org/10.1109/32.965339 |
ISSN: | 0098-5589 |
Appears in Collections: | Computer Science Dept of Computer Science Research Papers Software Engineering (B-SERC) |
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