research-article

Data accumulation and software effort prediction

Authors:

Stephen G. MacDonell,

Martin ShepperdAuthors Info & Claims

ESEM '10: Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

Article No.: 31, Pages 1 - 4

https://doi.org/10.1145/1852786.1852828

Published: 16 September 2010 Publication History

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Abstract

BACKGROUND: 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.

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Cited By

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Malgonde OChari K(2019)An ensemble-based model for predicting agile software development effortEmpirical Software Engineering10.1007/s10664-018-9647-024:2(1017-1055)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10664-018-9647-0
Nguyen VHuynh TBoehm BHuang LTruong T(2019)Investigating the use of duration‐based windows and estimation by analogy for COCOMOJournal of Software: Evolution and Process10.1002/smr.217631:10Online publication date: 25-Oct-2019
https://dl.acm.org/doi/10.1002/smr.2176
Minku LYao X(2017)Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past modelsAutomated Software Engineering10.1007/s10515-016-0209-724:3(499-542)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10515-016-0209-7
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Index Terms

Data accumulation and software effort prediction
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Implementation management
        Pricing and resource allocation

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Information & Contributors

Information

Published In

ESEM '10: Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 2010

423 pages

ISBN:9781450300391

DOI:10.1145/1852786

General Chair:
Giancarlo Succi
Free University of Bolzano, Italy
,
Program Chairs:
Maurizio Morisio
Politecnico di Torino, Italy
,
Nachi Nagappan
Microsoft Research

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2010

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SIGSOFT

ESEM '10: 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 16 - 17, 2010

Bolzano-Bozen, Italy

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ESEM '10 Paper Acceptance Rate 30 of 102 submissions, 29%;

Overall Acceptance Rate 130 of 594 submissions, 22%

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Cited By

View all

Malgonde OChari K(2019)An ensemble-based model for predicting agile software development effortEmpirical Software Engineering10.1007/s10664-018-9647-024:2(1017-1055)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10664-018-9647-0
Nguyen VHuynh TBoehm BHuang LTruong T(2019)Investigating the use of duration‐based windows and estimation by analogy for COCOMOJournal of Software: Evolution and Process10.1002/smr.217631:10Online publication date: 25-Oct-2019
https://dl.acm.org/doi/10.1002/smr.2176
Minku LYao X(2017)Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past modelsAutomated Software Engineering10.1007/s10515-016-0209-724:3(499-542)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10515-016-0209-7
Papatheocharous EBibi SStamelos IAndreou A(2017)An investigation of effort distribution among development phases: A four‐stage progressive software cost estimation modelJournal of Software: Evolution and Process10.1002/smr.188129:10Online publication date: 21-Jun-2017
https://doi.org/10.1002/smr.1881
Sigweni BShepperd MTurchi TBeecham SKitchenham BMacDonell S(2016)Realistic assessment of software effort estimation modelsProceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering10.1145/2915970.2916005(1-6)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1145/2915970.2916005
Amasaki SLokan C(2016)On Applicability of Fixed-Size Moving Windows for ANN-Based Effort Estimation2016 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (IWSM-MENSURA)10.1109/IWSM-Mensura.2016.041(213-218)Online publication date: Oct-2016
https://doi.org/10.1109/IWSM-Mensura.2016.041
Amasaki SLokan C(2016)Evaluation of Moving Window Policies with CART2016 7th International Workshop on Empirical Software Engineering in Practice (IWESEP)10.1109/IWESEP.2016.10(24-29)Online publication date: Mar-2016
https://doi.org/10.1109/IWESEP.2016.10
Amasaki SLokan CBener AMinku LTurhan B(2015)A Replication of Comparative Study of Moving Windows on Linear Regression and Estimation by AnalogyProceedings of the 11th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/2810146.2810153(1-10)Online publication date: 21-Oct-2015
https://dl.acm.org/doi/10.1145/2810146.2810153
Amasaki SLokan C(2015)The Effects of Duration-Based Moving Windows with Estimation by AnalogySoftware Measurement10.1007/978-3-319-24285-9_2(14-29)Online publication date: 25-Dec-2015
https://doi.org/10.1007/978-3-319-24285-9_2
Amasaki SLokan C(2015)On the effectiveness of weighted moving windowsJournal of Software: Evolution and Process10.1002/smr.167227:7(488-507)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1002/smr.1672
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Software development effort prediction: A study on the factors impacting the accuracy of fuzzy logic systems

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