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
IFPUG: International Function Point Users Group. http://www.ifpug.org/. Accessed Nov 2020
Albrecht, A.J.: Measuring application development productivity. In: Proceedings of the IBM Applications Development Symposium, p. 83 (1979)
Meli, R.: Early and extended function point: a new method for function points estimation. In: IFPUG Fall Conference, 15–19, Arizona 1997
Meli, R., Santillo, L.: Function point estimation methods: a comparative overview. In: FESMA 1999 Conference Proceedings, Amsterdam (1999)
Rask, R.: Algorithm for counting unadjusted function points from dataflow diagram. Technical report, University of Joensuu (1991)
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
Obrien, S.J., Jones, D.A.: Function points in SSADM. Software Qual. J. 2(1), 1–11 (1993)
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)
Lamma, E., Mello, P., Riguzzi, F.: A system for measuring function points from an ER-DFD specification. Comput. J. 47(3), 358–372 (2004)
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)
Boehm, B.W.: Software Estimation with COCOMO II, Upper Saddle River, NJ, Prentice-Hall (2002)
Conte, S.D., Dunsmore, H.E., Shen, V.Y.: Software Engineering Metrics and Models. Benjamin/ Cummings, Menlo Park CA (1986)
The OMG XMI document. http://schema.omg.org/spec/XMI/2.1. accessed Nov 2020
Gencel, C., Demirors, O.: Functional size measurement revisited. ACM Trans. Software Eng. Methodol. 17(3), 15.1–15.36 (2008)
Meli, R., Santillo, L.: Function point estimation methods: a comparative overview. In: FESMA 1999 Conference Proceedings, Amsterdam, 4–8, October 1999
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
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)
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)
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
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
Pitt, L., Valiant, L.G.: Computational limitations on learning from examples. J. ACM, (1988). https://doi.org/10.1145/48014.63140
Van Hai, V., Nhung, H.L.T.K., Hoc, H.T.: Productivity Optimizing Model for Improving Software Effort Estimation. CoMeSySo2020, October 2020
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
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)
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
Fetcke, T.: A Warehouse Software Portfolio – A Case Study in Functional Size Measurement," Report No. 1999–20
Kitchenham, Mendes, E.: Software productivity measurement using multiple size measures. IEEE Trans. Software Eng. 30(12), 1023–1035 (2004)
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)
. 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
Foss, T.S.: A simulation study of the model evaluation criterion MMRE. IEEE Trans. Software Eng. 29(11), 985–995 (2003)
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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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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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DOI: https://doi.org/10.1007/978-3-030-77442-4_53
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