Show simple item record

dc.contributor.authorMacDonell, SG
dc.contributor.authorShepperd, M
dc.date.accessioned2011-10-31T07:56:46Z
dc.date.available2011-10-31T07:56:46Z
dc.date.copyright2010-09-16
dc.date.issued2011-10-31
dc.identifier.citationPresentation at the 4th International Symposium on Empirical Software Engineering and Measurement, Bolzano-Bozen, Italy and published in Proceeding ESEM '10 Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement ACM New York, NY, USA
dc.identifier.isbn978-1-4503-0039-1
dc.identifier.urihttp://hdl.handle.net/10292/2441
dc.description.abstractBACKGROUND: In reality project managers are constrained by the incremental nature of data collection. Specifically, project observations are accumulated one project at a time. Likewise within-project data are accumulated one stage or phase at a time. However, empirical researchers have given limited attention to this perspective. PROBLEM: Consequently, our analyses may be biased. On the one hand, our predictions may be optimistic due to the availability of the entire data set, but on the other hand pessimistic due to the failure to capitalize upon the temporal nature of the data. Our goals are (i) to explore the impact of ignoring time when building cost prediction models and (ii) to show the benefits of re-estimating using completed phase data during a project. METHOD: Using a small industrial data set of sixteen software projects from a single organization we compare predictive models developed using a time-aware approach with a more traditional leave-one-out analysis. We then investigate the impact of using requirements, design and implementation phase data on estimating subsequent phase effort. RESULTS: First, we find that failure to take the temporal nature of data into account leads to unreliable estimates of their predictive efficacy. Second, for this organization, prior-phase effort data could be used to improve the management of subsequent process tasks. CONCLUSION: We should collect time-related data and use it in our analyses. Failure to do so may lead to incorrect conclusions being drawn, and may also inhibit industrial take up of our research work.
dc.publisherIEEE Computer Society Press
dc.relation.urihttp://dx.doi.org/10.1145/1852786.1852828
dc.relation.urihttp://esem2010.case.unibz.it/program.php
dc.rightsCopyright © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleData accumulation and software effort prediction
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1145/1852786.1852828


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record