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From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation

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Product-Focused Software Process Improvement (PROFES 2015)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9459))

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Abstract

Software companies exploit data about completed projects to estimate the development effort required for new projects. Software size is one of the most important information used to this end. However, different methods for sizing software exist and companies may require to migrate to a new method at a certain point. In this case, in order to exploit historical data they need to resize the past projects with the new method. Besides to be expensive, resizing is also often not possible due to the lack of adequate documentation. To support size measurement migration, we propose a transfer learning approach that allows to avoid resizing and is able to estimate the effort of new projects based on the combined use of data about past projects measured with the previous measurement method and projects measured with the new one. To assess our proposal, an empirical analysis is carried out using an industrial dataset of 25 projects. Function Point Analysis and COSMIC are the measurement methods taken into account in the study.

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Notes

  1. 1.

    Raw data cannot be revealed because of a Non Disclosure Agreement with the software company.

References

  1. Albrecht, A.: Measuring application development productivity. In: Proceedings of the Joint SHARE/GUIDE/IBM Application Development Symposium, pp. 83–92 (1979)

    Google Scholar 

  2. Abran, A., Desharnais, J., Lesterhuis, A., Londeix, B., Meli, R., Morris, P., Oligny, S., O’Neill, M., Rollo, T., Rule, G., Santillo, L., Symons, C., Toivonen, H.: The COSMIC Functional Size Measurement Method Measurement Manual, version 4.0.1 (2015)

    Google Scholar 

  3. Mendes, E., Kalinowski, M., Martins, D., Ferrucci, F., Sarro, F.: Cross- vs. within-company cost estimation studies revisited: an extended systematic review. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014, pp. 12:1–12:10 (2014)

    Google Scholar 

  4. Cuadrado-Gallego, J.J., Buglione, L., Domínguez-Alda, M.J., Sevilla, M.F., de Mesa, J.A.G., Demirors, O.: An experimental study on the conversion between IFPUG and COSMIC functional size measurement units. Inf. Softw. Technol. 52, 347–357 (2010)

    Article  Google Scholar 

  5. Lavazza, L.: An evaluation of the statistical convertibility of function points into cosmic function points. Empir. Softw. Eng. 19, 1075–1110 (2014)

    Article  Google Scholar 

  6. Di Martino, S., Ferrucci, F., Gravino, C., Sarro, F.: Web Effort Estimation: Function Points Analysis vs COSMIC (submitted to an International Journal for review)

    Google Scholar 

  7. Ferrucci, F., Gravino, C., Sarro, F.: Conversion from IFPUG FPA to COSMIC: within-vs without-company equations. In: 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications, pp. 293–300 (2014)

    Google Scholar 

  8. IFPUG: International Function Point Users Group. www.ifpug.org

  9. Gencel, Ç., Demirörs, O.: Functional size measurement revisited. ACM Trans. Softw. Engi. Methodol. 17(3), 71–106 (2008)

    Google Scholar 

  10. Van Heeringen, H.: Changing from FPA to COSMIC- a transition framework. In: Software Measurement European Forum (2007)

    Google Scholar 

  11. Abran, A., Londeix, B., O’Neill, M., Santillo, L., Vogelezang, F., Desharnais, J.M., Morris, P., Rollo, T., Symons, C., Lesterhuis, A., Oligny, S., Rule, G., Toivonen, H.: The COSMIC Functional Size Measurement Method, Version 3.0, Advanced and Related Topics (2007)

    Google Scholar 

  12. Lavazza, L., Morasca, S.: Convertibility of function points into COSMIC function points: a study using piecewise linear regression. Inf. Softw. Technol. 53(8), 874–884 (2011)

    Article  Google Scholar 

  13. Ferrucci, F., Gravino, C., Sarro, F.: A case study on the conversion of function points into cosmic. In: The Proceedings of the 37th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA), pp. 461–464 (2011)

    Google Scholar 

  14. Abran, A.: Convertibility Across Measurement Methods, pp. 269–280. Wiley, New York (2010)

    Google Scholar 

  15. Ho, V., Abran, A., Fetcke, T.: A comparative study case of COSMIC, full function Point and IFPUG methods. Technical report, Département dinformatique, Université du Quebec á Montréal, Canada (1999)

    Google Scholar 

  16. Desharnais, J., Abran, A., Cuadrado-Gallego, J.: Convertibility of function points to COSMIC: identification and analysis of functional outliers. In: Proceedings of the International Workshop on Software Measurement, pp. 130–146. Shaker-Verlag (2007)

    Google Scholar 

  17. Abran, A., Desharnais, J., Azziz, F.: Measurement convertibility: from function points to COSMIC. In: Proceedings of the International Workshop on Software Measurement, pp. 227–240. Shaker-Verlag (2005)

    Google Scholar 

  18. Abualkishik, A.Z., Desharnais, J.M., Khelifi, A., Ghani, A.A.A., Atan, R.B., Selamat, M.H.: An exploratory study on the accuracy of FPA to COSMIC measurement method conversion types. Inf. Softw. Technol. 54, 1250–1264 (2012)

    Article  Google Scholar 

  19. Gencel, Ç., Bideau, C.: Exploring the convertibility between IFPUG and COSMIC function points: preliminary findings. In: Proceedings of International Conference on Software Process and Product Measurement, pp. 170–177 (2012)

    Google Scholar 

  20. Lavazza, L., Bianco, V.D., Liu, G.: Analytical convertibility of functional size measures: a tool-based approach. In: Proceedings of International Conference on Software Process and Product Measurement, pp. 160–169 (2012)

    Google Scholar 

  21. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010)

    Article  Google Scholar 

  22. Kocaguneli, E., Menzies, T., Mendes, E.: Transfer learning in effort estimation. Empir. Softw. Engg. 20, 813–843 (2015)

    Article  Google Scholar 

  23. Arnold, A., Nallapati, R., Cohen, W.W.: A comparative study of methods for transductive transfer learning. In: Seventh IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007, pp. 77–82. IEEE (2007)

    Google Scholar 

  24. Kitchenham, B., Mendes, E., Travassos, G.: Cross versus within-company cost estimation studies: a systematic review. IEEE Trans. Softw. Eng. 33, 316–329 (2007)

    Article  Google Scholar 

  25. Mendes, E., Di Martino, S., Ferrucci, F., Gravino, C.: Effort estimation: how valuable is it for a Web company to use a cross-company data set, compared to using its own single-company data set? In: Proceedings of the 6th International World Wide Web Conference, pp. 83–93. ACM press (2007)

    Google Scholar 

  26. Menzies, T., Chen, Z., Hihn, J., Lum, K.: Selecting best practices for effort estimation. IEEE Trans. Softw. Eng. 32, 883–895 (2006)

    Article  Google Scholar 

  27. Daumé, H.: Frustratingly easy domain adaptation. In: Proceedings of ACL 2007 (2007)

    Google Scholar 

  28. Chelba, C., Acero, A.: Adaptation of maximum entropy capitalizer: Little data can help a lot. In: Lin, D., Wu, D. (eds.) Proceedings of EMNLP 2004, pp. 285–292. Association for Computational Linguistics, Barcelona, Spain (2004)

    Google Scholar 

  29. Shepperd, M.J., MacDonell, S.G.: Evaluating prediction systems in software project estimation. Inf. Softw. Technol. 54, 820–827 (2012)

    Article  Google Scholar 

  30. Kampenes, V., Dyba, T., Hannay, J., Sjoberg, I.: A systematic review of effect size in software engineering experiments. Inf. Softw. Technol. 4, 1073–1086 (2007)

    Article  Google Scholar 

  31. Mendes, E., Counsell, S., Mosley, N., Triggs, C., Watson, I.: A comparative study of cost estimation models for web hypermedia applications. Empir. Softw. Eng. 8, 163–196 (2003)

    Article  Google Scholar 

  32. Kaner, C., Bond, W.: Software engineering metrics: what do they measure and how do we know? In: Proceedings of the International Software Metrics Symposium. IEEE press (2004)

    Google Scholar 

  33. Mendes, E., Counsell, S., Mosley, N.: Comparison of Web size measures for predicting Web design and authoring effort. IEE Proc. Softw. 149, 86–92 (2002)

    Article  Google Scholar 

  34. Kitchenham, B., Pickard, L., MacDonell, S., Shepperd, M.: What accuracy statistics really measure. IEE Proc. Softw. 148, 81–85 (2001)

    Article  Google Scholar 

  35. Briand, L.C., Wüst, J.: Modeling development effort in object-oriented systems using design properties. IEEE Trans. Softw. Eng. 27, 963–986 (2001)

    Article  Google Scholar 

  36. Zimmermann, T., Nagappan, N., Gall, H., Giger, E., Murphy, B.: Cross-project defect prediction: a large scale experiment on data vs. domain vs. process. In: Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2009, pp. 91–100 (2009)

    Google Scholar 

  37. Ma, Y., Luo, G., Zeng, X., Chen, A.: Transfer learning for cross-company software defect prediction. Inf. Softw. Technol. 54, 248–256 (2012)

    Article  Google Scholar 

  38. Nam, J., Pan, S.J., Kim, S.: Transfer defect learning. In: Proceedings of the 2013 International Conference on Software Engineering, pp. 382–391. IEEE Press (2013)

    Google Scholar 

  39. Ferrucci, F., Mendes, E., Sarro, F.: Web effort estimation: the value of cross-company data set compared to single-company data set. In: Proceedings of the 8th International Conference on Predictive Models in Software Engineering, PROMISE 2012, pp. 29–38 (2012)

    Google Scholar 

  40. Minku, L.L., Yao, X.: How to make best use of cross-company data in software effort estimation? In: Proceedings of the 36th International Conference on Software Engineering, ICSE 2014, pp. 446–456 (2014)

    Google Scholar 

  41. Minku, L.L., Sarro, F., Mendes, E., Ferrucci, F.: How to make best use of cross-company data for web effort estimation? In: Proceedings of the 9th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2015 (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli “Federico II”, Naples, Italy

    Anna Corazza & Sergio Di Martino

  2. Dipartimento di Informatica, Università di Salerno, Fisciano, Italy

    Filomena Ferrucci & Carmine Gravino

  3. CREST, Department of Computer Science, University College London, London, UK

    Federica Sarro

Authors
  1. Anna Corazza
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  2. Sergio Di Martino
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  3. Filomena Ferrucci
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  4. Carmine Gravino
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  5. Federica Sarro
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Corresponding author

Correspondence to Sergio Di Martino .

Editor information

Editors and Affiliations

  1. Norwegian Univ. of Science and Technol., Trondheim, Norway

    Pekka Abrahamsson

  2. Free University of Bolzano, Bolzano, Italy

    Luis Corral

  3. University of Oulu, Oulu, Finland

    Markku Oivo

  4. Free University of Bolzano, Bozen, Italy

    Barbara Russo

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Corazza, A., Di Martino, S., Ferrucci, F., Gravino, C., Sarro, F. (2015). From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation. In: Abrahamsson, P., Corral, L., Oivo, M., Russo, B. (eds) Product-Focused Software Process Improvement. PROFES 2015. Lecture Notes in Computer Science(), vol 9459. Springer, Cham. https://doi.org/10.1007/978-3-319-26844-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-26844-6_19

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