Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/337180.337223acmconferencesArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
Article
Free access

A replicated assessment and comparison of common software cost modeling techniques

Published: 01 June 2000 Publication History
  • Get Citation Alerts
  • Abstract

    Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality of the delivered software and thus on a company's business reputation and competitiveness. On the other hand, overestimation of software cost can result in missed opportunities to funds in other projects. In response to industry demand, a myriad of estimation techniques has been proposed during the last three decades. In order to assess the suitability of a technique from a diverse selection, its performance and relative merits must be compared.
    The current study replicates a comprehensive comparison of common estimation techniques within different organizational contexts, using data from the European Space Agency. Our study is motivated by the challenge to assess the feasibility of using multi-organization data to build cost models and the benefits gained from company-specific data collection. Using the European Space Agency data set, we investigated a yet unexplored application domain, including military and space projects. The results showed that traditional techniques, namely, ordinary least-squares regression and analysis of variance outperformed Analogy-based estimation and regression trees. Consistent with the results of the replicated study no significant difference was found in accuracy between estimates derived from company-specific data and estimates derived from multi-organizational data.

    References

    [1]
    Boehm, B. Software Engineering Economics. Englewood Cliffs, NJ Prentice Hall (1981).
    [2]
    Boehm B., Clark, B., Horowitz, E. Westland, C. Cost models for future software life cycle processes: COCOMO 2.0. Annals of Software Engineering, 1 (1995), 57-94.
    [3]
    Bohrnstedt G., Carter, T. Robustness in Regression Analysis. In: Costner, H. (ed). Chapter 5, Sociological Methodology. Jossey-Bass (1971).
    [4]
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. Classification and Regression Trees. Wadsworth & Books/Cole Advanced Books & Software (1984).
    [5]
    Briand, L.C., Basili, V.R., Thomas, W.M. A pattern recognition approach for software engineering data analysis. IEEE Transactions on Software Engineering, 18, 11 (1992) 931-942.
    [6]
    Briand, L.C., El Emam K., Bomarius, F. A Hybrid Method for Software Cost Estimation, Benchmarking, and Risk Assessment. Proceedings of the 20 th International Conference on Software Engineering, ICSE-20 (April 1998) 390-399.
    [7]
    Briand, L.C., El Emam, K., Wieczorek I. Explaining the Cost of European Space and Military Projects. In: Proceedings of the 21 st International Conference on Software Engineering, ICSE 99, (Los Angeles, USA 1999) 303-312.
    [8]
    Briand, L.C. El Emam K., Maxwell, K., Surmann, D., Wieczorek, I. An Assessment and Comparison of Common Software Cost Estimation Models. In: Proceedings of the 21 st International Conference on Software Engineering, ICSE 99, (Los Angeles, USA 1998) 313-322.
    [9]
    Briand, L.C., Langley, T., Wieczorek, I. Using the European Space Agency Database: A replicated Assessment of Common Software Cost Estimation Techniques. Technical Report, ISERN TR-99-15, International Software Engineering Research Network (1999).
    [10]
    CBR-Works, 4.0 beta. Research group "Artificial Intelligence- Knowledge-Based Systems", University of Kaiserslautern <http://wwwagr.informatik.uni-kl. de/-lsa/CBRatUKL.html>
    [11]
    Conte, S.D., Dunsmore, H.E., Shen, V. Y. Software engineering metrics and models. The Benjamin/Cummings Publishing Company, Inc. (1986).
    [12]
    Chulani S., Boehm B., Steece B. Bayesian Analysis of Empirical Software Engineering Cost Models. IEEE Transactions on Software Engineering, 25,4 (1999)
    [13]
    Davidson, R., McKinnon, J. Several Tests for Model Specification in the Presence of Alternative Hypotheses. Econometrica, 49, 3 (1981), 781-93.
    [14]
    Finnie, G. R., Wittig, G. E. A comparison of software effort estimation techniques: using function points with neural networks, case based reasoning and regression models. J. Systems Software, 39 (1997) 281-289.
    [15]
    Gibbons, J.D. S. Nonparametric Statistics. Series: Quantitative Application in the Social Sciences 90, SAGE University Paper (1993).
    [16]
    Gray, A., MacDonell, D. A comparison of techniques for developing predictive models of software metrics. Information and Software Technology, 39, (1997) 425-437
    [17]
    Greves, D., Schreiber, B. The ESA Initiative for Software Productivity Benchmarking and Effort Estimation. ESA Bulletin, 87 (August 1996). <http://esapub.esrin.esa.it/bulletin/bulett87/greves87.ht m>
    [18]
    H~st, M., Wohlin, C. An Experimental Study of Individual Subjective Effort Estimation and Combinations of the Estimates. In: Proceedings of the 20 st International Conference on Software Engineering, ICSE 98, (Japan, 1998) 332-339.
    [19]
    <http://dec.bournemouth.ac.uk/dec_ind/decind22/web/ Angel.html>.
    [20]
    Jones, C. Applied Software Measurement: Assuring Productivity and Quality, (2 nd Ed.), Mc-Graw-Hill, NY, USA, (1996).
    [21]
    J~rgensen, M. Experience with the accuracy of Software Maintenance Task Effort Prediction Models. IEEE Transactions on Software Engineering, 21, 8 (August, 1995) 674-681.
    [22]
    Kitchenham, B. A. Procedure for Analyzing Unbalanced Data Sets, IEEE Transactions on Software Engineering, 24, 4 (April 1998) 278-301.
    [23]
    Schroeder, L. Sjoquist, D., Stephan, P. Understanding Regression Analysis: An Introductory Guide. No. 57 In Series: Quantitative Applications in the Social Sciences, Sage Publications, Newbury Park CA, USA, (1986)
    [24]
    Maxwell, K., Van Wassenhove, L. and Dutta, S. Software Development Productivity of European Space, Military and Industrial Applications. IEEE Transactions on Software Engineering, 22, 10 (1996).
    [25]
    Shepperd, M., Schofield, C. Estimating software project effort using analogies. IEEE Transactions on Software Engineering, 23, 12 (November 1997) 736- 743.
    [26]
    Stensrud, E., Myrtveit, I. Human Performance Estimation with Analogy and Regression Models. In: Proceedings of the METRICS 98 Symposium, (1998) 205-213.
    [27]
    Spector, P. Ratings of Equal and unequal Response Choice Intervals. The Journal of Social Psychology, 112 (1980) 115-119.
    [28]
    Srinivasan, K., Fisher, D. Machine learning approaches to estimating software development effort. IEEE Transactions on Software Engineering, 21, 2 (February 1995) 126-137.
    [29]
    StataCorp, Stata Statistical Software: Release 5.0. Stata Corporation, College Station, (Texas 1997). <http://www.stata.com>
    [30]
    Steinberg, D., Colla, P. CART, Classification and Regression Trees, Tree Structured Non-parametric Data Analysis, Interface Documentation. Saflord Systems (1995), <http://www.salfordsystems.com/ index.html>
    [31]
    Walkerden F., Jeffery R. An Empirical Study of Analogy-based Software Effort Estimation. Empirical Software Engineering, 4, 2, (June 1999) 135-158.
    [32]
    Weiss, S., Kulikowski, C. Computer Systems that Learn. Morgan Kaufmann Publishers, Inc. San Francisco, CA, (1991).

    Cited By

    View all
    • (2023)CoBRA without experts: New paradigm for software development effort estimation using COCOMO metricsJournal of Software: Evolution and Process10.1002/smr.2569Online publication date: 25-Apr-2023
    • (2021)Machine Learning Modeling of Forest Road Construction CostsForests10.3390/f1209116912:9(1169)Online publication date: 28-Aug-2021
    • (2019)A Generic Data Mining Model for Software Cost Estimation Based on Novel Input Selection ProcedureInternational Journal of Information Retrieval Research10.4018/IJIRR.20190101029:1(16-32)Online publication date: 1-Jan-2019
    • Show More Cited By

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ICSE '00: Proceedings of the 22nd international conference on Software engineering
    June 2000
    843 pages
    ISBN:1581132069
    DOI:10.1145/337180
    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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 June 2000

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. analogy
    2. analysis of variance
    3. classification and regression trees
    4. cost estimation
    5. ordinary least-squares regression
    6. replication

    Qualifiers

    • Article

    Conference

    ICSE00
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 276 of 1,856 submissions, 15%

    Upcoming Conference

    ICSE 2025

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)78
    • Downloads (Last 6 weeks)8
    Reflects downloads up to 11 Aug 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)CoBRA without experts: New paradigm for software development effort estimation using COCOMO metricsJournal of Software: Evolution and Process10.1002/smr.2569Online publication date: 25-Apr-2023
    • (2021)Machine Learning Modeling of Forest Road Construction CostsForests10.3390/f1209116912:9(1169)Online publication date: 28-Aug-2021
    • (2019)A Generic Data Mining Model for Software Cost Estimation Based on Novel Input Selection ProcedureInternational Journal of Information Retrieval Research10.4018/IJIRR.20190101029:1(16-32)Online publication date: 1-Jan-2019
    • (2019)Data Smoothing for Software Effort Estimation2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)10.1109/SNPD.2019.8935841(501-506)Online publication date: Jul-2019
    • (2019)Search Strategy to Update Systematic Literature Reviews in Software Engineering2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA.2019.00061(355-362)Online publication date: Aug-2019
    • (2019)Decision Trees Based Software Development Effort Estimation: A Systematic Mapping Study2019 International Conference of Computer Science and Renewable Energies (ICCSRE)10.1109/ICCSRE.2019.8807544(1-6)Online publication date: Jul-2019
    • (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
    • (2018)A Framework of Statistical and Visualization Techniques for Missing Data Analysis in Software Cost EstimationIntelligent Systems10.4018/978-1-5225-5643-5.ch014(345-372)Online publication date: 2018
    • (2018)A Framework of Statistical and Visualization Techniques for Missing Data Analysis in Software Cost EstimationComputer Systems and Software Engineering10.4018/978-1-5225-3923-0.ch017(433-460)Online publication date: 2018
    • (2018)Web Effort Estimation Using FP and WO: A Critical Study2018 Second International Conference on Computing Methodologies and Communication (ICCMC)10.1109/ICCMC.2018.8487472(357-361)Online publication date: Feb-2018
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media