More Web Proxy on the site http://driver.im/

research-article

Search-based approaches for software development effort estimation

Author:

Federica SarroAuthors Info & Claims

Profes '11: Proceedings of the 12th International Conference on Product Focused Software Development and Process Improvement

Pages 38 - 43

https://doi.org/10.1145/2181101.2181111

Published: 20 June 2011 Publication History

Abstract

In the last years the use of Search-Based techniques has been suggested to estimate software development effort. These techniques are meta-heuristics able to find optimal or near optimal solutions to problems characterized by large space. In the context of effort estimation Search-Based approaches can be exploited to build estimation models or to enhance the effectiveness of other methods. The preliminary investigations carried out so far have provided promising results. Nevertheless, the capabilities of these approaches have not been fully explored and the empirical analyses carried out so far have not considered the more recent recommendations on how to perform this kind of empirical assessment in the effort estimation context and in Search-Based Software Engineering. The main aim of the PhD dissertation is to provide an insight on the use of Search-Based techniques for effort estimation trying to highlight strengths and weaknesses.

References

[1]

Arcuri, A., Briand, L., A Practical Guide for Using Statistical Tests to Assess Randomized Algorithms in Software Engineering, in Procs of International Conference on Software Engineering, (2011).

Digital Library

[2]

Basili, V. R., The Role of Experimentation in Software Engineering: Past, Current and Future, in Procs of International Conference on Software Engineering, (1996).

Digital Library

[3]

Bailey, J. W., Basili, V. R., A Meta Model for Software Development Resource Expenditure, in Procs of Conference on Software Engineering, pp. 107--115, (1981).

Digital Library

[4]

Braga, P. L., Oliveira, A. L. I., Meira, S. R. L., A GA-based Feature Selection and Parameters Optimization for Support Vector Regression Applied to Software Effort Estimation, in Procs of the ACM Symposium on Applied computing, pp. 1788--1792, (2008).

Digital Library

[5]

Briand, L., Wieczorek, I., Software Resource Estimation, Encyclopedia of Software Engineering, pp. 1160--1196, (2002).

[6]

Burgess C. J., Lefley M., Can genetic programming improve software effort estimation? A comparative evaluation, Information and Software Technology, pp.863--873, 43, (2001).

[7]

Chiu, N. H., Huang, S., The adjusted analogy-based software effort estimation based on similarity distances, Journal of Systems and Software, pp. 628--640, 80(4) (2007).

Digital Library

[8]

Conte, D., Dunsmore, H., Shen, V., Software engineering metrics and models, The Benjamin/Cummings Publishing Company, Inc., (1986).

Digital Library

[9]

Corazza A., Di Martino S., Ferrucci F., Gravino C., Mendes E., Investigating the use of Support Vector Regression for web effort estimation, Empirical Software Engineering, pp. 211--243, 16(2), (2011).

Digital Library

[10]

Corazza, A., Di Martino, S., Ferrucci, F., Gravino, C., Sarro, F., Mendes, E., How Effective is Tabu Search to Configure Support Vector Regression for Effort Estimation? in Procs of PROMISE 2010: 4.

Digital Library

[11]

Dolado, J. J., A validation of the component-based method for software size estimation, IEEE TSE, pp. 1006--1021, 26(10), (2000).

Digital Library

[12]

Ferrucci, F., Gravino, C., Oliveto, R., Sarro, F., Using Evolutionary Based Approaches to Estimate Software Development Effort, in Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques, M. Chis, IGI Global, ISBN13: 9781615208098, (2010).

[13]

Ferrucci, F., Gravino, C., Oliveto, R., Sarro, F., Using Tabu Search to Estimate Software Development Effort, in Procs of IWSM/MENSURA 2009. LNCS, Springer, vol. 5891, pp. 307--320, (2009).

Digital Library

[14]

Ferrucci, F., Gravino, C., Oliveto, R., Sarro, F., Estimating Software Development Effort Using Tabu Search, in Procs of the 12th International Conference on Enterprise Information Systems, pp. 236--241, 1, (2010).

[15]

F. Ferrucci, C. Gravino, E. Mendes, R. Oliveto, F. Sarro, Investigating Tabu Search for Web Effort Estimation, in Procs of the 36th EUROMICRO Conference on Software Engineering and Advanced Applications, IEEE Computer Society, pp.350--357, (2010).

Digital Library

[16]

F. Ferrucci, C. Gravino, R. Oliveto, F. Sarro, Genetic Programming for Effort Estimation: an Analysis of the Impact of Different Fitness Functions, in Procs of the 2nd International Symposium on SBSE, IEEE Computer Society, pp. 89--98, (2010).

Digital Library

[17]

Foss, T., Stensrud, E., Kitchenham, B., Myrtveit, I., A simulation study of the model evaluation criterion MMRE, IEEE TSE, pp. 985--995, 29 (11), (2003).

Digital Library

[18]

Harman, M, Clark, J. A., Metrics Are Fitness Functions Too, IEEE METRICS, pp. 58--69, 2004.

Digital Library

[19]

Harman M., Jones B. F., Search based software engineering, Information and Software Technology, pp. 833--839, 43(14), (2001).

[20]

Harman, M., The Current State and Future of Search Based Software Engineering, in Procs of Future of Software Engineering, pp. 342--357, (2007).

Digital Library

[21]

Huang, S. J., Chiu, N. H., Chen, L. W., Integration of the grey relational analysis with genetic algorithm for software effort estimation, European Journal of Operational Research, pp. 898--909, 188(3), (2008).

[22]

Jørgensen, M., A review of studies on expert estimation of software development effort, Journal of Systems and Software, pp. 37--60, 70(1--2), (2004)

Digital Library

[23]

Kampenes, V., Dybå, T., Hannay, J. E., Sjøberg, D. I. K., A Systematic Review of Effect Size in Software Engineering Experiments, Information and Software Technology, pp.1073--1086, 4(11-12), (2007).

Digital Library

[24]

Kitchenham, B., Mendes, M., Why comparative effort prediction studies may be invalid, in Procs of PROMISE 2009: 4.

Digital Library

[25]

Kitchenham, B., Pickard, L. M., MacDonell, S. G., Shepperd, M. J., What accuracy statistics really measure, IEE Procs Software, pp. 81--85, 148 (3), (2001).

[26]

Kitchenham, B. A., Pfleeger, S. L., Pickard, L. M., Jones, P. W., Hoaglin, D. C, El Emam, K., Rosenberg, J., Preliminary Guidelines for Empirical Research in Software Engineering, IEEE TSE, pp. 721--734, 8 (2002).

Digital Library

[27]

Koch, S., Mitlöhner, J., Software project effort estimation with voting rules, Decision Support Systems, pp. 895--901, 46(4), (2009).

Digital Library

[28]

Lefley M., Shepperd M. J., Using genetic programming to improve software effort estimation based on general data sets, in Procs of GECCO, pp. 2477--2487, (2003).

Digital Library

[29]

Li, Y. F., Xie, M., Goh, T. N., A study of project selection and feature weighting for analogy based software cost estimation, Journal of Systems and Software, pp.241--252, 82(2), (2009).

Digital Library

[30]

Mendes, E. Web Cost Estimation and Productivity Benchmarking, ISSSE, pp. 194--222, (2008).

[31]

Mendes, E., Kitchenham, B. Further comparison of cross-company and within-company effort estimation models for web applications, in: Procs of International Software Metrics Symposium, IEEE press, pp. 348--357, 10 (47), (2004).

Digital Library

[32]

Mendes, E., Mosley, N., Counsell, S., Investigating Web Size Metrics for Early Web Cost Estimation, Journal of Systems and Software, pp. 157--172, 77 (2), (2005).

Digital Library

[33]

PROMISE Repository of empirical software engineering data, http://promisedata.org/repository.

[34]

Shan, Y., Mckay, R. I., Lokan, C. J., Essam, D. L., Software project effort estimation using genetic programming, in Procs of International Conference on Communications Circuits and Systems, IEEE press, pp. 1108--1112, (2002).

[35]

Shepperd, M., Schofield, C., Estimating software project effort using analogies, IEEE TSE, pp. 736--743, 23(11), (2000).

Digital Library

[36]

Shukla, K. K., Neuro-genetic prediction of software development effort, Information and Software Technology, pp. 701--713, 42 (10), (2000).

[37]

Wohlin, C., Runeson, P., Host, M., Regnell, B., Wesslen, A., Experimentation in Software Engineering-An Introduction, Kluwer Academic Publishers Norwell, MA, USA, (2000).

Digital Library

Cited By

Sarmad Ali SRen JWu JZhang KChao L(2024)Advancing Software Project Effort Estimation: Leveraging a NIVIM for Enhanced PreprocessingJournal of Software: Evolution and Process10.1002/smr.2745Online publication date: 18-Dec-2024
https://doi.org/10.1002/smr.2745
Tawosi VSarro FPetrozziello AHarman M(2022)Multi-Objective Software Effort Estimation: A Replication StudyIEEE Transactions on Software Engineering10.1109/TSE.2021.308336048:8(3185-3205)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3083360
Ali AGravino C(2021)Improving Software Effort Estimation using Bio-Inspired Algorithms to select relevant features: An Empirical StudyScience of Computer Programming10.1016/j.scico.2021.102621(102621)Online publication date: Jan-2021
https://doi.org/10.1016/j.scico.2021.102621
Show More Cited By

Index Terms

Search-based approaches for software development effort estimation

Recommendations

Empirical evaluation of fuzzy analogy for software development effort estimation
SAC '17: Proceedings of the Symposium on Applied Computing

Software Development Effort Estimation (SDEE) plays a primary role in software project management. Among several techniques suggested for estimating software development effort, analogy-based software effort estimation approaches stand out as promising ...
Relative estimates of software development effort: Are they more accurate or less time-consuming to produce than absolute estimates, and to what extent are they person-independent?
Abstract Context
Estimates of software development effort may be given as judgments of relationships between the use of efforts on different tasks-that is, as relative estimates. The use of relative estimates has increased with the ...
Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation
Abstract
Software development effort estimation (SDEE) plays a primary role in software project management. But choosing the appropriate SDEE technique remains elusive for many project managers and researchers. Moreover, the choice of a reliable estimation ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

Profes '11: Proceedings of the 12th International Conference on Product Focused Software Development and Process Improvement

June 2011

159 pages

ISBN:9781450307833

DOI:10.1145/2181101

General Chair:
Giuseppe Visaggio
University of Bari and Daisy-Net, Italy
,
Program Chairs:
Danilo Caivano
University of Bari and SER&Practices, Italy
,
Markku Oivo
University of Oulu, Finland

Copyright © 2011 ACM.

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]

Sponsors

Dipartimento di Informatica "Renato M. Capocelli", Università degli Studi di Salerno, Italy: Dipartimento di Informatica "Renato M. Capocelli", Università degli Studi di Salerno, Italy
SER&Practices: SER&Practices
Università di Bari: Università di Bari
Daisy-Net: Daisy-Net
Exprevia: Exprevia SpA
Project Management Institute: Project Management Institute
UNIBA: Department of Informatics, UNIBA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

Profes '11

Sponsor:

Dipartimento di Informatica "Renato M. Capocelli", Università degli Studi di Salerno, Italy
SER&Practices
Università di Bari
Daisy-Net
Exprevia
Project Management Institute
UNIBA

Profes '11: Conference on Product Focused Software Development and Process Improvement

June 20 - 22, 2011

Brindisi, Torre Canne, Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
248
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Sarmad Ali SRen JWu JZhang KChao L(2024)Advancing Software Project Effort Estimation: Leveraging a NIVIM for Enhanced PreprocessingJournal of Software: Evolution and Process10.1002/smr.2745Online publication date: 18-Dec-2024
https://doi.org/10.1002/smr.2745
Tawosi VSarro FPetrozziello AHarman M(2022)Multi-Objective Software Effort Estimation: A Replication StudyIEEE Transactions on Software Engineering10.1109/TSE.2021.308336048:8(3185-3205)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3083360
Ali AGravino C(2021)Improving Software Effort Estimation using Bio-Inspired Algorithms to select relevant features: An Empirical StudyScience of Computer Programming10.1016/j.scico.2021.102621(102621)Online publication date: Jan-2021
https://doi.org/10.1016/j.scico.2021.102621
Sarro F(2019)Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?Search-Based Software Engineering10.1007/978-3-030-27455-9_1(3-7)Online publication date: 3-Aug-2019
https://doi.org/10.1007/978-3-030-27455-9_1
de A. Cabral Jde A. Araujo RNobrega Jde Oliveira A(2017)Heterogeneous Ensemble Dynamic Selection for Software Development Effort Estimation2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2017.00042(210-217)Online publication date: Nov-2017
https://doi.org/10.1109/ICTAI.2017.00042
Ferrucci FHarman MSarro F(2014)Search-Based Software Project ManagementSoftware Project Management in a Changing World10.1007/978-3-642-55035-5_15(373-399)Online publication date: 21-May-2014
https://doi.org/10.1007/978-3-642-55035-5_15

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents