article

On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation

Authors:

C. LokanAuthors Info & Claims

Journal of Software: Evolution and Process, Volume 27, Issue 7

Pages 488 - 507

https://doi.org/10.1002/smr.1672

Published: 01 July 2015 Publication History

Abstract

In construction of an effort estimation model, it seems effective to use a window of training data so that the model is trained with only recent projects. Considering the chronological order of projects within the window, and weighting projects according to their order within the window, may also affect estimation accuracy. In this study, we examined the effects of weighted moving windows on effort estimation accuracy. We compared weighted and non-weighted moving windows under the same experimental settings. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, although the methods also significantly worsened accuracy in smaller windows. This result contributes to understanding properties of moving windows. Copyright © 2014 John Wiley & Sons, Ltd.

References

[1]

Port D, Korte M. Comparative studies of the model evaluation criterions mmre and pred in software cost estimation research. Proc. of the 2nd ACM-IEEE international symposium on Empirical software engineering and measurement. ACM, 2008.

Digital Library

[2]

Jørgensen M, Shepperd M. A systematic review of software development cost estimation studies. IEEE Transactions on Software Engineering 2007; Volume 33 Issue 1: pp.33-53.

[3]

Lokan C, Mendes E. Applying moving windows to software effort estimation. Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement. IEEE Computer Society, 2009; pp.111-122.

[4]

Auer M, Biffl S. Increasing the accuracy and reliability of analogy-based cost estimation with extensive project feature dimension weighting. Proc. of International Symposium on Empirical Software Engineering. IEEE, 2004; pp.147-155.

[5]

Mendes E, Lokan C. Investigating the use of chronological splitting to compare software cross-company and single-company effort predictions: a replicated study. Proceedings of the 13th Conference on Evaluation & Assessment in Software Engineering EASE 2009. BCS, 2009.

[6]

Keung JW, Kitchenham BA, Jeffery DR. Analogy-X: providing statistical inference to analogy-based software cost estimation. IEEE Transactions on Software Engineering 2008; Volume 34 Issue 4: pp.471-484.

[7]

Li J, Ruhe G. Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+. Empirical Software Engineering 2007; Volume 13 Issue 1: pp.63-96.

[8]

Maxwell KD. Applied Statistics for Software Managers. Prentice Hall</publisherLoc>: <publisherLoc>U.S, 2002.

[9]

Kitchenham B, Lawrence Pfleeger S, McColl B, Eagan S. An empirical study of maintenance and development estimation accuracy. Journal of Systems and Software 2002; Volume 64 Issue 1: pp.57-77.

[10]

MacDonell SG, Shepperd M. Data accumulation and software effort prediction. Proc. of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. ACM, 2010.

[11]

Amasaki S, Lokan C. The effects of moving windows to software estimation: comparative study on linear regression and estimation by analogy. 2012 Joint Conf of 22nd Int'l Workshop on Software Measurement and the 7th Int'l Conference on Software Process and Product Measurement IWSM-MENSURA. IEEE, 2012; pp.23-32.

[12]

Lokan C, Mendes E. Investigating the Use of duration-based moving windows to improve software effort prediction. Software Engineering Conference APSEC, 2012 19th Asia-Pacific, Hong Kong, 2012; pp.818-827.

Digital Library

[13]

Amasaki S, Lokan C. The evaluation of weighted moving windows for software effort estimation. PROFES '13: Proceedings of the 14th International Conference on Product Focused Software, Cyprus, 2013; pp.214-228.

[14]

Mendes E, Lokan C. Replicating studies on cross- vs single-company effort models using the ISBSG Database. Empirical Software Engineering 2008; Volume 13 Issue 1; pp.3-37.

[15]

Loader C. Local Regression and Likelihood. Statistics and Computing, Springer</publisherLoc>: <publisherLoc>U.S, 1999.

[16]

Tabachnick BG, Fidell LS. Using Multivariate Statistics. Harper-Collins: U.S, 1996.

[17]

Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 1996; Volume 58 Issue 1; pp.267-288.

Cited By

Li YMeng LChen LYu LWu DZhou YXu BDwyer MDamian DZeller A(2022)Training data debugging for the fairness of machine learning softwareProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510091(2215-2227)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510091
Minku LMcIntosh SXia XAmasaki S(2021)Multi-stream online transfer learning for software effort estimation: is it necessary?Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3475960.3475988(11-20)Online publication date: 19-Aug-2021
https://dl.acm.org/doi/10.1145/3475960.3475988
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
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Index Terms

On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation
1. Networks
  1. Network types
    1. Data center networks
2. Software and its engineering
  1. Software creation and management
    1. Software development process management

Index terms have been assigned to the content through auto-classification.

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Investigating the Use of Duration-Based Moving Windows to Improve Software Effort Prediction
APSEC '12: Proceedings of the 2012 19th Asia-Pacific Software Engineering Conference - Volume 01

To date most research in software effort estimation has not taken into account any form of chronological split when selecting projects for training and testing sets. A chronological split represents the use of a project's starting and completion dates, ...
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cover image Journal of Software: Evolution and Process

Journal of Software: Evolution and Process Volume 27, Issue 7

July 2015

43 pages

ISSN:2047-7473

EISSN:2047-7481

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 July 2015

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Cited By

Li YMeng LChen LYu LWu DZhou YXu BDwyer MDamian DZeller A(2022)Training data debugging for the fairness of machine learning softwareProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510091(2215-2227)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510091
Minku LMcIntosh SXia XAmasaki S(2021)Multi-stream online transfer learning for software effort estimation: is it necessary?Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3475960.3475988(11-20)Online publication date: 19-Aug-2021
https://dl.acm.org/doi/10.1145/3475960.3475988
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
Hosni MIdri AAbran A(2019)Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimationJournal of Software: Evolution and Process10.1002/smr.211731:2Online publication date: 14-Feb-2019
https://dl.acm.org/doi/10.1002/smr.2117
(2018)Duplex output software effort estimation model with self-guided interpretationInformation and Software Technology10.1016/j.infsof.2017.09.01094:C(1-13)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.infsof.2017.09.010
Silhavy RSilhavy PProkopova Z(2017)Analysis and selection of a regression model for the Use Case Points method using a stepwise approachJournal of Systems and Software10.1016/j.jss.2016.11.029125:C(1-14)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.jss.2016.11.029
(2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.infsof.2017.06.002
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
Amasaki SLokan CBeecham SKitchenham BMacDonell S(2016)A replication study on the effects of weighted moving windows for software effort estimationProceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering10.1145/2915970.2915983(1-9)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1145/2915970.2915983

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