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Empirical Evidence in Early Stage Software Effort Estimation Using Data Flow Diagram

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Software Engineering and Algorithms (CSOC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 230))

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Abstract

Software effort estimation in a very early stage of the application development lifecycle is always a challenge for project managers. Many researchers have proposed different methods. They have certain advantages and limitations. This study proposed an approach called Early Effort Estimation from the data flow diagram with a complexity tag added that could estimate the effort estimation in a very early stage of the software development cycle. This method can be obtained by evaluating the customized data flow diagram, a data flow diagram with specific tag values. We also proposed a tool that implemented this method.

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References

  1. IFPUG: International Function Point Users Group. http://www.ifpug.org/. Accessed Nov 2020

  2. Albrecht, A.J.: Measuring application development productivity. In: Proceedings of the IBM Applications Development Symposium, p. 83 (1979)

    Google Scholar 

  3. Meli, R.: Early and extended function point: a new method for function points estimation. In: IFPUG Fall Conference, 15–19, Arizona 1997

    Google Scholar 

  4. Meli, R., Santillo, L.: Function point estimation methods: a comparative overview. In: FESMA 1999 Conference Proceedings, Amsterdam (1999)

    Google Scholar 

  5. Rask, R.: Algorithm for counting unadjusted function points from dataflow diagram. Technical report, University of Joensuu (1991)

    Google Scholar 

  6. Rask, R.: Counting function points from SA descriptions. The Papers of the Third Annual Oregon Workshop on Software Metrics (Ed. W. Harrison), Oregon, 17–19 March 1991

    Google Scholar 

  7. Obrien, S.J., Jones, D.A.: Function points in SSADM. Software Qual. J. 2(1), 1–11 (1993)

    Article  Google Scholar 

  8. Shoval, P., Feldman, O.: Combining function points estimation model with ADISSA methodology for system analysis and design. In: Proceeding of ICCSSE, pp. 3–8 (1996)

    Google Scholar 

  9. Lamma, E., Mello, P., Riguzzi, F.: A system for measuring function points from an ER-DFD specification. Comput. J. 47(3), 358–372 (2004)

    Article  Google Scholar 

  10. Gramantieri, F., Lamma, E., Mello, P., Riguzzi, F.: A system for measuring function points from specifications. DEIS – Universita di Bologna, Bologna. and Dipartimento di Ingegneria, Ferrara, Tech. Rep DEIS-LIA-97–006 (1997)

    Google Scholar 

  11. Boehm, B.W.: Software Estimation with COCOMO II, Upper Saddle River, NJ, Prentice-Hall (2002)

    Google Scholar 

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

    Google Scholar 

  13. The OMG XMI document. http://schema.omg.org/spec/XMI/2.1. accessed Nov 2020

  14. Gencel, C., Demirors, O.: Functional size measurement revisited. ACM Trans. Software Eng. Methodol. 17(3), 15.1–15.36 (2008)

    Google Scholar 

  15. Meli, R., Santillo, L.: Function point estimation methods: a comparative overview. In: FESMA 1999 Conference Proceedings, Amsterdam, 4–8, October 1999

    Google Scholar 

  16. Darcy, D.P., Kemerer, C.F., Slaughter, S.A., Tomayko, J.E.: The structural complexity of software an experimental test. IEEE Trans. Software Eng. 31(11), 982–995 (2005). https://doi.org/10.1109/TSE.2005.130

    Article  Google Scholar 

  17. Xia, W., Ho, D., Captrez, L.F.: A neuro-fuzzy model for function point calibration. WSEAS Trans. Inf. Sci. Appl. 5(1), 22–30 (2008)

    Google Scholar 

  18. Ahmed, F., Bouktif, S., Serhani, A., Khalil, I.: Integrating function point project information for improving the accuracy of effort estimation. In: Proceedings of the 2nd International Conference on Advanced Engineering Computing Application Science, pp. 193–198 (2008)

    Google Scholar 

  19. Mange, J.: Effect of training data order for machine learning. In: 2019 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 406–407 (2019). https://doi.org/10.1109/CSCI49370.2019.00078

  20. Hoffmann,A.G.: General limitations on machine learning. In: ECAI 1990: Proceedings of the 9th European Conference on Artificial Intelligence, pp. 345–347, January 1990

    Google Scholar 

  21. Pitt, L., Valiant, L.G.: Computational limitations on learning from examples. J. ACM, (1988). https://doi.org/10.1145/48014.63140

  22. Van Hai, V., Nhung, H.L.T.K., Hoc, H.T.: Productivity Optimizing Model for Improving Software Effort Estimation. CoMeSySo2020, October 2020

    Google Scholar 

  23. Prokopová, Z., Šilhavý, P., Šilhavý, R.: Influence analysis of selected factors in the function point work effort estimation. In: Advances in Intelligent Systems and Computing [online]. Szczecin: Springer Verlag, 2019, s. 112–124. [cit. 2020–12–02]. ISSN 2194–5357

    Google Scholar 

  24. Rule, G.: ‘Small project’, ‘medium-size project’ and ‘large project’: what do these terms mean? PowerPoint presentation. Software Measurement Services Ltd. (SMS), copyright 2004–2005, 124 High Street, Edenbridge, Kent, United Kingdom (2005)

    Google Scholar 

  25. Pratiwi, D.: Implementation of function point analysis in measuring the volume estimation of software system in object-oriented and structural model of academic system. Int. J. Comput. Appl. (0975–8887) 70(10) (2013). https://doi.org/10.5120/11995-7879

  26. Fetcke, T.: A Warehouse Software Portfolio – A Case Study in Functional Size Measurement," Report No. 1999–20

    Google Scholar 

  27. Kitchenham, Mendes, E.: Software productivity measurement using multiple size measures. IEEE Trans. Software Eng. 30(12), 1023–1035 (2004)

    Google Scholar 

  28. Briand, L.C., Emam, K.E., Surmann, D., Wieczorek, I., Maxwell, K.D.: An assessment and comparison of common software cost estimation modeling techniques. In: International Conference on Software Engineering, pp. 313–322 (1999)

    Google Scholar 

  29. . Albrecht, A.J, Gaffney, J.E.: Software function, source lines of code, and development effort prediction: a software science validation. IEEE Trans. Software Eng. SE-9(6), 639–648 (1983). https://doi.org/10.1109/TSE.1983.235271

  30. Foss, T.S.: A simulation study of the model evaluation criterion MMRE. IEEE Trans. Software Eng. 29(11), 985–995 (2003)

    Article  Google Scholar 

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Acknowledgment

This work was supported by the Faculty of Applied Informatics, Tomas Bata University in Zlín, under project IGA/CebiaTech/2021/001.

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Correspondence to Vo Van Hai .

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Van Hai, V., Le Thi Kim Nhung, H., Hoc, H.T. (2021). Empirical Evidence in Early Stage Software Effort Estimation Using Data Flow Diagram. In: Silhavy, R. (eds) Software Engineering and Algorithms. CSOC 2021. Lecture Notes in Networks and Systems, vol 230. Springer, Cham. https://doi.org/10.1007/978-3-030-77442-4_53

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