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http://bura.brunel.ac.uk/handle/2438/1556
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DC Field | Value | Language |
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dc.contributor.author | Shepperd, MJ | - |
dc.contributor.author | Cartwright, MH | - |
dc.coverage.spatial | 15 | en |
dc.date.accessioned | 2008-01-22T15:18:29Z | - |
dc.date.available | 2008-01-22T15:18:29Z | - |
dc.date.issued | 2001 | - |
dc.identifier.citation | IEEE Transactions on Software Engineering 27(11): 1014-1022, Nov 2001 | en |
dc.identifier.issn | 0098-5589 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/1556 | - |
dc.description.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. | en |
dc.format.extent | 456614 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | en |
dc.subject | Prediction | en |
dc.subject | Software project effort | en |
dc.subject | Expert judgement | en |
dc.subject | Empirical data | en |
dc.subject | Sparse data | en |
dc.subject | Cost estimation | en |
dc.title | Predicting with sparse data | en |
dc.type | Research Paper | en |
dc.identifier.doi | https://doi.org/10.1109/32.965339 | - |
Appears in Collections: | Computer Science Dept of Computer Science Research Papers Software Engineering (B-SERC) |
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File | Description | Size | Format | |
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FullText.pdf | 445.91 kB | Adobe PDF | View/Open |
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