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Application of Function Points and Data Mining Techniques for Software Estimation - A Combined Approach

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Software Measurement (Mensura 2015, IWSM 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 230))

Abstract

Project estimation is recognized as one of the most challenging processes in software project management on which project success is dependable. Traditional estimation methods based on expert knowledge and analogy tend to be error prone and deliver overoptimistic assessments. Methods derived from function points are good sizing tools but do not reflect organizations’ specific project management culture. Due to those deficiencies in recent years data mining techniques are explored as an alternative estimation method. The aim of this paper is to present a combined approach of functional sizing measurement and three data mining techniques for effort and duration estimation at project early stages: generalized linear models, artificial neural networks and CHAID decision trees. The estimation accuracy of these models is compared in order to determine their potential usefulness for deployment within organizations. Moreover a merged approach of combining algorithms’ results is proposed in order to increase prediction accuracy and overcome possibility of overfitting occurrence.

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References

  1. Project Management Institute: A Guide to the Project Management Body of Knowledge - PMBOK Guide. Project Management Institute (2013)

    Google Scholar 

  2. Marchewka, J.: Information Technology Project Managment - Providing Measurable Organizational Value. Wiley, Hoboken (2003)

    Google Scholar 

  3. Standish Group: The CHAOS Manifesto 2011. Standish Gr. Int. EUA. 25 (2011)

    Google Scholar 

  4. Czarnacka-Chrobot, B.: Analysis of the functional size measurement methods usage by polish business software systems providers. In: Abran, A., Braungarten, R., Dumke, R.R., Cuadrado-Gallego, J.J., Brunekreef, J. (eds.) IWSM 2009. LNCS, vol. 5891, pp. 17–34. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. Neimat, T.: Al: Why IT projects fail. Proj. perfect white Pap. Collect., pp. 1–8 (2005)

    Google Scholar 

  6. Tan, S.: How to Increase Your IT Project Success Rate. Gart. Res. Rep. (2011)

    Google Scholar 

  7. Mieritz, L.: Survey Shows Why Projects Fail (2012)

    Google Scholar 

  8. Galorath, D., Evans, M.: Software Sizing, Estimation, and Risk Management. Auerbach Publications, Boca Raton (2006)

    Book  MATH  Google Scholar 

  9. Wells, G.: Why projects fail. Manag. Sci. J. (2001)

    Google Scholar 

  10. International Software Benchmarking Standards Group: ISBSG Repository Data Release 12 - Field Descriptions (2013)

    Google Scholar 

  11. Schwalbe, K.: Information Technology Project Management. Course Technology, Boston (2014)

    Google Scholar 

  12. Boehm, B.W.: Software Engineering Economics. Prentice Hall, Englewood Cliffs (1981). 10, 4–21

    MATH  Google Scholar 

  13. Laird, L.M., Brennan, M.C.: Software Measurement and Estimation: A Practical Approach. Wiley, Hoboken (2006)

    Book  Google Scholar 

  14. Albrecht, A.: Measuring application development productivity. In: IBO Conference on Application Development, pp. 83–92 (1979)

    Google Scholar 

  15. Czarnacka-Chrobot, B.: Standardization of software size measurement. In: Tkacz, E., Kapczynski, A. (eds.) Internet – Technical Development and Applications. AISC, vol. 64, pp. 149–156. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  16. Hill, P.: Practical Software Project Estimation: a Toolkit for Estimating Software Development Effort & Duration. McGraw Hill Professional, New York (2010)

    Google Scholar 

  17. Gasik, S.: A model of project knowledge management. Proj. Manag. J. 42, 23–44 (2011)

    Article  Google Scholar 

  18. Piatetsky-Shapiro, G., Frawley, W.J.: Knowledge Discovery in Databases (1991)

    Google Scholar 

  19. Linoff, G.S., Berry, M.J.A.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley, New York (2011)

    Google Scholar 

  20. International Society of Parametric Analysts: Parametric Estimating Handbook. ISPA (2008)

    Google Scholar 

  21. Iranmanesh, S.H., Mokhtari, Z.: Application of data mining tools to predicate completion time of a project. Proc. World Acad. Sci. Eng. Technol. 32, 234–240 (2008)

    Google Scholar 

  22. Azzeh, M., Cowling, P.I., Neagu, D.: Software stage-effort estimation based on association rule mining and Fuzzy set theory. In: Proceedings - 10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, ScalCom-2010, pp. 249–256 (2010)

    Google Scholar 

  23. Balsera, J.V., Montequin, V.R., Fernandez, F.O., González-Fanjul, C.A.: Data Mining Applied to the Improvement of Project Management. InTech. (2012)

    Google Scholar 

  24. Nagwani, N.K., Bhansali, A.: A data mining model to predict software bug complexity using bug estimation and clustering. In: ITC 2010 - 2010 International Conference on Recent Trends in Information, Telecommunication, and Computing, pp. 13–17 (2010)

    Google Scholar 

  25. Shukla, R., Shukla, M., Misra, A.K., Marwala, T., Clarke, W.A.: Dynamic software maintenance effort estimation modeling using neural network, rule engine and multi-regression approach. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part IV. LNCS, vol. 7336, pp. 157–169. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  26. Jorgensen, M., Shepperd, M.: A systematic review of software development cost estimation studies. IEEE Trans. Softw. Eng. 33, 33–53 (2007)

    Article  Google Scholar 

  27. Wen, J., Li, S., Lin, Z., Hu, Y., Huang, C.: Systematic literature review of machine learning based software development effort estimation models. Inf. Softw. Technol. 54, 41–59 (2012)

    Article  Google Scholar 

  28. Kobyliński, A., Pospieszny, P.: Zastosowanie technik eksploracji danych do estymacji pracochłonności projektów informatycznych. Studia i Materiały Polskiego Stowarzyszenia Zarządzania Wiedzą, pp. 67–82, Bydgoszcz (2015)

    Google Scholar 

  29. Dzega, D., Pietruszkiewicz, W.: Classification and metaclassification in large scale data mining application for estimation of software projects. In: 2010 IEEE 9th International Conference on Cybernetic Intelligent Systems, CIS 2010 (2010)

    Google Scholar 

  30. Dejaeger, K., Verbeke, W., Martens, D., Baesens, B.: Data mining techniques for software effort estimation: A comparative study. IEEE Trans. Softw. Eng. 38, 375–397 (2012)

    Article  Google Scholar 

  31. Brewer, J., Dittman, K.: Methods of IT Project Management. Prentice Hal, New York (2009)

    Google Scholar 

  32. Ruchika Malhotra, A.J.: Software effort prediction using statistical and machine learning methods. Int. J. Adv. Comput. Sci. Appl. 2, 145–152 (2011)

    Google Scholar 

  33. Pai, D.R., McFall, K.S., Subramanian, G.H.: Software effort estimation using a neural network ensemble. J. Comput. Inf. Syst. 53, 49–58 (2013)

    Google Scholar 

  34. Lopez-Martin, C., Isaza, C., Chavoya, A.: Software development effort prediction of industrial projects applying a general regression neural network. Empir. Softw. Eng. 17, 738–756 (2012)

    Article  Google Scholar 

  35. Mittas, N., Angelis, L.: Ranking and clustering software cost estimation models through a multiple comparisons algorithm. IEEE Trans. Softw. Eng. 39, 537–551 (2013)

    Article  Google Scholar 

  36. Kocaguneli, E., Menzies, T., Keung, J.W.: On the value of ensemble effort estimation. IEEE Trans. Softw. Eng. 38, 1403–1416 (2012)

    Article  Google Scholar 

  37. Reifer, D.J., Boehm, B.W., Chulani, S.: The Rosetta stone: Making COCOMO 81 Files Work With COCOMO II. Univ. South Calif. 1–10 (1998)

    Google Scholar 

  38. PROMISE Software Engineering Repository. http://promise.site.uottawa.ca/SERepository/

  39. SourceForge. http://sourceforge.net

  40. Albrecht, A.J., Gaffney, J.E.J.: Software function, source lines of code, and development effort prediction: a software science validation. IEEE Trans. Softw. Eng. SE-9, 639–648 (1983)

    Article  Google Scholar 

  41. International Software Benchmarking Standards Group. http://www.isbsg.org

  42. Villanueva-Balsera, J., Ortega-Fernandez, F., Rodríguez-Montequín, V., Concepción-Suárez, R.: Effort estimation in information systems projects using data mining techniques. In: Proceedings of the 13th WSEAS International Conference on Computers - Held as part of the 13th WSEAS CSCC Multiconference, pp. 652–657 (2009)

    Google Scholar 

  43. Pete, C., Julian, C., Randy, K., Thomas, K., Thomas, R., Colin, S., Wirth, R.: CRISP-DM 1.0 (2000)

    Google Scholar 

  44. Giudici, P., Figini, S.: Applied Data Mining for Business and Industry. Wiley, New York (2009)

    Book  MATH  Google Scholar 

  45. Larose, D.T.: Data Mining Methods and Models. Wiley, New York (2007)

    MATH  Google Scholar 

  46. Boehm, B.W., Abts, C., Brown, A.W., Chulani, S., Clark, B.K., Horowitz, E., Madachy, R., Reifer, D.J., Steece, B.: Software Cost Estimation with Cocomo II. Prentice Hall PTR, Upper Saddle River (2000)

    Google Scholar 

  47. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)

    MATH  Google Scholar 

  48. Conte, S.D., Dunsmore, H.E., Shen, V.Y.: Software Engineering Metrics and Models. Benjamin/Cummings Pub. Co., Menlo Park (1986)

    Google Scholar 

  49. Jorgensen, M.: A critique of how we measure and interpret the accuracy of software development effort estimation. In: 1st International Workshop on Software Productivity Analysis and Cost Estimation. ss. 15–22 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information Systems and Digital Economy, Warsaw School of Economics, ul. Madalińskiego 6/8, 02-513, Warsaw, Poland

    Przemysław Pospieszny, Beata Czarnacka-Chrobot & Andrzej Kobyliński

Authors
  1. Przemysław Pospieszny
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  2. Beata Czarnacka-Chrobot
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  3. Andrzej Kobyliński
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Corresponding author

Correspondence to Przemysław Pospieszny .

Editor information

Editors and Affiliations

  1. Institute of Information Systems and Digital Economy, Warsaw School of Economics, Warsaw, Poland

    Andrzej Kobyliński

  2. Institute of Information Systems and Digital Economy, Warsaw School of Economics, Warsaw, Poland

    Beata Czarnacka-Chrobot

  3. Polish Software Measurement Association, Warsaw, Poland

    Jaroslaw Świerczek

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Pospieszny, P., Czarnacka-Chrobot, B., Kobyliński, A. (2015). Application of Function Points and Data Mining Techniques for Software Estimation - A Combined Approach. In: Kobyliński, A., Czarnacka-Chrobot, B., Świerczek, J. (eds) Software Measurement. Mensura IWSM 2015 2015. Lecture Notes in Business Information Processing, vol 230. Springer, Cham. https://doi.org/10.1007/978-3-319-24285-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-24285-9_7

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