Skip to main content
SpringerLink
Log in

Empirical analysis on productivity prediction and locality for use case points method

  • Published:
Software Quality Journal Aims and scope Submit manuscript

Abstract

Use case points (UCP) method has been around for over two decades. Although there was a substantial criticism concerning the algebraic construction and factor assessment of UCP, it remains an efficient early size estimation method. Predicting software effort from UCP is still an ever-present challenge. The earlier version of UCP method suggested using productivity as a cost driver, where fixed or a few pre-defined productivity ratios have been widely agreed. While this approach was successful when not enough historical data is available, it is no longer acceptable because software projects are different in terms of development aspects. Therefore, it is better to understand the relationship between productivity and other UCP variables. This paper examines the impact of data locality approaches on productivity and effort prediction from multiple UCP variables. The environmental factors are used as partitioning factors to produce local homogeneous data either based on their influential levels or using clustering algorithms. Different machine learning methods, including solo and ensemble methods, are used to construct productivity and effort prediction models based on the local data. The results demonstrate that the prediction models that are created based on local data surpass models that use entire data. Also, the results show that conforming to the hypothetical assumption between productivity and environmental factors is not necessarily a requirement for the success of locality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  • Ali, A., & Gravino, C. (2019). A systematic literature review of software effort prediction using machine learning methods. Journal of Software: Evolution and Process. Wiley. https://doi.org/10.1002/smr.2211

  • Alves, L. M., Sousa, A., Ribeiro, P., & Machado, R. J. (2013a). An empirical study on the estimation of software development effort with use case points. Proceedings - Frontiers in Education Conference, FIE. https://doi.org/10.1109/FIE.2013.6684796

    Article  Google Scholar 

  • Alves, R., Valente, P., & Nunes, N. J. (2013b). Improving software effort estimation with human-centric models. In Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems (pp. 287–296). Association for Computing Machinery (ACM). https://doi.org/10.1145/2494603.2480300

  • Azzeh, M., & Nassif, A. B. (2015). Analogy-based effort estimation: a new method to discover set of analogies from dataset characteristics. IET Software, 9(2), 39–50.

    Article  Google Scholar 

  • Azzeh, M., & Nassif, A. B. (2017). Analyzing the relationship between project productivity and environment factors in the use case points method. Journal of Software: Evolution and Process.

  • Azzeh, M., & Nassif, A. B. A. B. (2016). A hybrid model for estimating software project effort from Use Case Points. Applied Soft Computing Journal, 49, 981–989. https://doi.org/10.1016/j.asoc.2016.05.008

    Article  Google Scholar 

  • Azzeh, M., Nassif, A. B., & Banitaan, S. (2018). A comparative analysis of soft computing techniques for predicting software effort based use case points chagas insect recognition using data mining and deep learning view project a comparative analysis of soft computing techniques for predicting software. IET, 12(1), 19–29. https://doi.org/10.1049/iet-sen.2016.0322

    Article  Google Scholar 

  • Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterey, Calif., USA: Wadsworth.

  • Colomo-Palacios, R., Casado-Lumbreras, C., Soto-Acosta, P., García-Peñalvo, F. J., & Tovar, E. (2014). Project managers in global software development teams: A study of the effects on productivity and performance. Software Quality Journal, 22(1), 3–19. https://doi.org/10.1007/s11219-012-9191-x

    Article  Google Scholar 

  • Foss, T., Stensrud, E., Kitchenham, B., & Myrtveit, I. (2003). A simulation study of the model evaluation criterion MMRE. IEEE Transactions on Software Engineering, 29, no. 11(11), 985–995. https://doi.org/10.1109/TSE.2003.1245300

  • Frohnhoff, S., & Engels, G. (2008). Revised use case point method-effort estimation in development projects for business applications. In 11th International Conference on Quality Engineering in Software Technology (CONQUEST 2008).

  • Gallego, J. J. C., Rodríguez, D., Sicilia, M. Á., Rubio, M. G., & Crespo, A. G. (2007). Software project effort estimation based on multiple parametric models generated through data clustering. Journal of Computer Science and Technology, 22(3), 371–378. https://doi.org/10.1007/s11390-007-9043-5

    Article  Google Scholar 

  • Gautam, S. S., & Singh, V. (2018). The state-of-the-art in software development effort estimation. Journal of Software: Evolution and Process, 30(12). https://doi.org/10.1002/smr.1983

  • Henderson-Sellers, B., Zowghi, D., Klemola, T., & Parasuram, S. (2002). Sizing use cases: How to create a standard metrical approach. In International Conference on Object-Oriented Information Systems (pp. 409–421). Springer. https://doi.org/10.1007/3-540-46102-7_43

  • Humphrey, W. S. (2002). Personal Software Process (PSP). In Encyclopedia of Software Engineering. Hoboken, NJ, USA: John Wiley & Sons, Inc. https://doi.org/10.1002/0471028959.sof238

  • Kamal, M. W., Ahmed, M. A., & El-Attar, M. (2011). Use case-based effort estimation approaches: A comparison criteria. In Software Engineering and Computer Systems (pp. 735–754). Springer.

  • Karner, G. (1993). Resource estimation for objectory projects. Objective Systems SF AB, 17, 1–9.

    Google Scholar 

  • Kitchenham, B., & Mendes, E. (2004). Software productivity measurement using multiple size measures. Software Engineering, IEEE Transactions On, 30(12), 1023–1035.

    Article  Google Scholar 

  • Kocaguneli, E., & Menzies, T. (2013a). Software effort models should be assessed via leave-one-out validation. Journal of Systems and Software, 86(7), 1879–1890.

    Article  Google Scholar 

  • Kocaguneli, E., & Menzies, T. (2013b). Software effort models should be assessed via leave-one-out validation. Journal of Systems and Software, 26(7), 1879–1890.

    Article  Google Scholar 

  • Kocaguneli, E., Kultur, Y., & Bener, A. B. (2009). Combining multiple learners induced on multiple datasets for software effort prediction. In 20th international symposium on software reliability engineering (ISSRE).

  • Lagerström, R., von Würtemberg, L. M., Holm, H., & Luczak, O. (2012). Identifying factors affecting software development cost and productivity. Software Quality Journal, 20(2), 395–417. https://doi.org/10.1007/s11219-011-9137-8

    Article  Google Scholar 

  • Masson, M. E. J., & Loftus, G. R. (2003). Using confidence intervals for graphically based data interpretation. Canadian Journal of Experimental Psychology, 57(3), 203–220. https://doi.org/10.1037/h0087426

    Article  Google Scholar 

  • Mendes, E. (2004). Software productivity measurement using multiple size measures. IEEE Transactions on Software Engineering, 30(12), 1023–1035. https://doi.org/10.1109/TSE.2004.104

    Article  Google Scholar 

  • Mendes, E., Watson, I., Triggs, C., Mosley, N., & Counsell, S. (2003). A comparative study of cost estimation models for web hypermedia applications. Empirical Software Engineering, 8(2), 163–196. https://doi.org/10.1023/A:1023062629183

    Article  Google Scholar 

  • Minku, L. L., & Yao, X. (2013). Ensembles and locality: Insight on improving software effort estimation. Information and Software Technology, 55(8), 1512–1528.

    Article  Google Scholar 

  • Nassif, A. B., Capretz, L. F., & Ho, D. (2010). Enhancing use case points estimation method using soft computing techniques. Journal of Global Research in Computer Science, 1(4), 12–21.

    Google Scholar 

  • Nassif, A. B., Capretz, L. F., & Ho, D. (2011a). A regression model with mamdani fuzzy inference system for early software effort estimation based on use case diagrams. In Third International Conference on Intelligent Computing and Intelligent Systems (Vol. Guangzhou, pp. 615–620). Retrieved from http://www.researchgate.net/publication/233824054_A_Regression_Model_with_Mamdani_Fuzzy_Inference_System_for_Early_Software_Effort_Estimation_Based_on_Use_Case_Diagrams/file/9fcfd50bea8d4b39c6.pdf

  • Nassif, A. B., Capretz, L. F., & Ho, D. (2011b). Estimating software effort based on use case point model using sugeno fuzzy inference system. 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence. https://doi.org/10.1109/ICTAI.2011.64

  • Nassif, A. B., Capretz, L. F., & Ho, D. (2014). Calibrating use case points (pp. 612–613). https://doi.org/10.1145/2591062.2591141

  • Nassif, A. B., Ho, D., & Capretz, L. F. (2013). Towards an early software estimation using log-linear regression and a multilayer perceptron model. Journal of Systems and Software, 86(1). https://doi.org/10.1016/j.jss.2012.07.050

  • Ochodek, M., Alchimowicz, B., Jurkiewicz, J., & Nawrocki, J. (2011a). Improving the reliability of transaction identification in use cases. Information and Software Technology, 53(8), 885–897.

    Article  Google Scholar 

  • Ochodek, M., Nawrocki, J., & Kwarciak, K. (2011b). Simplifying effort estimation based on Use Case Points. Information and Software Technology, 53(3), 200–213. https://doi.org/10.1016/j.infsof.2010.10.005

    Article  Google Scholar 

  • Ochodek, M., & Nawrocki, J. (2008). Automatic transactions identification in use cases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5082 LNCS, pp. 55–68). https://doi.org/10.1007/978-3-540-85279-7_5

  • Petersen, K. (2011). Measuring and predicting software productivity: A systematic map and review. Information and Software Technology, 53(4), 317–343. https://doi.org/10.1016/j.infsof.2010.12.001

    Article  Google Scholar 

  • Robiolo, G., Badano, C., & Orosco, R. (2009). Transactions and paths: Two use case based metrics which improve the early effort estimation. In International Symposium on Empirical Software Engineering and Measurement (Vol. 0, pp. 422–425). Los Alamitos, CA, USA: IEEE Computer Society. https://doi.org/10.1109/ESEM.2009.5316021

  • Robiolo, G., & Orosco, R. (2008). Employing use cases to early estimate effort with simpler metrics. Innovations in Systems and Software Engineering, 4(1), 31–43. https://doi.org/10.1007/s11334-007-0043-y

    Article  Google Scholar 

  • Rodriguez, D., Sicilia, M. Á., García, E., & Harrison, R. (2012). Empirical findings on team size and productivity in software development. Journal of Systems and Software, 85(3), 562–570. https://doi.org/10.1016/j.jss.2011.09.009

    Article  Google Scholar 

  • Satapathy, S. M., & Rath, S. K. (2014). Use case point approach based software effort estimation using various support vector regression kernel methods. ArXiv Preprint ArXiv: 1401.3069, 1(1), 1–13.

  • Schneider, G. (2001). Applied use Cases, A Practical Guide.

  • Shepperd, M., & MacDonell, S. (2012). Evaluating prediction systems in software project estimation. Information and Software Technology 54.8 (2012): 820–827., 54(8), 820–827.

  • Silhavy, R., Silhavy, P., & Prokopova, Z. (2017). Analysis and selection of a regression model for the Use Case Points method using a stepwise approach. Journal of Systems and Software, 125, 1–14. https://doi.org/10.1016/j.jss.2016.11.029

    Article  Google Scholar 

  • Silhavy, R., Silhavy, P., & Prokopova, Z. (2018a). Evaluating subset selection methods for use case points estimation. Information and Software Technology, 97, 1–9.

  • Silhavy, R., Silhavy, P., & Prokopova, Z. (2018b). Evaluating subset selection methods for use case points estimation. Information and Software Technology, 97, 1–9. https://doi.org/10.1016/J.INFSOF.2017.12.009

    Article  Google Scholar 

  • Silhavy, P., Silhavy R., & Prokopova, Z. (2019). Categorical variable segmentation model for software development effort estimation. IEEE Access, 7, 9618–9626.

    Article  Google Scholar 

  • Vapnik, Vladmir N. (1995). The nature of statistical learning theory. Google Scholar Google Scholar Digital Library Digital Library.

Download references

Acknowledgements

Mohammad Azzeh is grateful to the Applied Science Private University, Amman, Jordan, for the financial support granted to cover the publication fee of this research article. Ali Bou Nassif would like to thank the University of Sharjah for supporting this research. Cuauhtémoc López-Martín would like to thank the CUCEA, Universidad de Guadalajara, México for its support during the development of this research.

Author information

Authors and Affiliations

  1. Department of Software Engineering, Applied Science Private University, Amman, Jordan

    Mohammad Azzeh

  2. Department of Computer Engineering, University of Sharjah, Sharjah, UAE

    Ali Bou Nassif

  3. Department of Information Systems, Universidad de Guadalajara, Zapopan, Jalisco, México

    Cuauhtémoc López Martín

Authors
  1. Mohammad Azzeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Bou Nassif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cuauhtémoc López Martín
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Azzeh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Azzeh, M., Nassif, A.B. & Martín, C.L. Empirical analysis on productivity prediction and locality for use case points method. Software Qual J 29, 309–336 (2021). https://doi.org/10.1007/s11219-021-09547-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11219-021-09547-0

Keywords

Search

Navigation