International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Estimating Software Size Using Metrics from Analysis and Design Phases (ADM) of the Software Development Life Cycle (SDLC)

Abstract

Due to the lack of available information in the early stages of project creation, early estimation of the project size is a difficult task. The information appears more detailed and clear as we progress in the stages of building the software. The aim of the research is to design and implement a model to estimate the size of the program in the two phases of analysis and design separately, and in the phases of analysis and design together using four measures of the class diagram, which are "number of classes", "number of features", "number of operations" and "number of relationships".

Country : Iraq

1 AESHAA L. AL-SALEEM2 ASMA’A Y. HAMMO

  1. Department of Software Engineering, College of Computer Science and Mathematics, University of Mosul, Iraq
  2. Department of Software Engineering, College of Computer Science and Mathematics, University of Mosul, Iraq

IRJIET, Volume 7, Issue 3, March 2023 pp. 29-35

doi.org/10.47001/IRJIET/2023.703005

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References

  1. Badri, M., Badri, L., Flageol, W., &Toure, F. (2017). Source code size prediction using use case metrics: an empirical comparison with use case points. Innovations in Systems and Software Engineering, 13(2), 143-159.
  2. Nassif AB, Ho D, Capretz LF (2013) Towards an early software estimation using log-linear regression and a multilayer perceptron model. J Syst Softw 86(1):144–160.
  3. Ochodek M, Nawrocki J, Kwarciak K (2011) Simplifying effort estimation based on use case points. Inf Softw Techno 53: 200–213.
  4. Lagerstroemia R, von Wurttemberg LM, Holm H, Luczak O (2012) Identifying factors affecting software development cost and productivity. Softw Qual J 20(2): 395–417.
  5. Zhou Y, Yang Y, Xu B, Leung H, Zhou X (2014) Source code size estimation approaches for object oriented systems from UML class diagrams: a comparative study. Inf Softw Technol 56: 220–237.
  6. Silhavy, R., Silhavy, P. and Prokopova, Z. (2021) Using actors and use cases for software size estimation. Electron., 10, 1–20.
  7. Densumite, S. and Muenchaisri, P. (2017) Software size estimation using activity point. IOP Conf. Ser.: Mater. Sci. Eng., 185, 1-8.
  8. Ungan, E. (2013) A functional software measurement approach bridging the gap between problem and solution domains. Ph.D. dissertation. In The Department of Information Systems. Middle East Technical University, Ankara, Turkey.
  9. Daud, M. and Malik, A.A. (2021) Improving the accuracy of early software size estimation using analysis-to-design adjustment factors (ADAFs). IEEE Access, 9, 81986–81999.
  10. Kim, S., Lively, W. and Simmons, D. (2006) An effort estimation by UML points in the early stage of software development. In Proc. SERP 06, Las Vegas, Nevada, June 26–29, pp. 415–421. CSREA Press, Las Vegas, Nevada.
  11. Zhou, Y., Yang, Y., Xu, B., Leung, H. and Zhou, X. (2014) Source code size estimation approaches for object oriented systems from UML class diagrams: a comparative study. Inf. Softw. Technol., 56, 220–237.
  12. Misic, V.B. and Te ˇ sic, D.N. (1998) Estimation of effort and ˇ complexity: an object-oriented case study. J. Syst. Softw., 41, 133–143.
  13. Antoniol, G., Lokan, C., Caldiera, G. and Fiutem, R. (1999) A function point-like measure for object oriented software. Empirical Softw. Eng., 4, 263–287.
  14. Antoniol, G., Fiutem, R. and Lokan, C. (2003) Object-oriented function points: an empirical validation. Empirical Softw. Eng., 8, 225–254.
  15. Chen, Y., Boehm, B.W., Madachy, R. and Valerdi, R. (2004) An empirical study of eServices product UML sizing metrics. In Proc. ISESE 04, Redondo Beach, CA, August 19–20, pp. 199– 206. IEEE, Washington, DC.
  16. Bianco, V.D. and Lavazza, L. (2005) an empirical assessment of function point-like object oriented metrics. In Proc. METRICS 05, Como, Italy, September 19–22, pp. 1–10. IEEE, Washington, DC.
  17. Tan, H.B.K. and Zhao, Y. (2006) Sizing data-intensive systems from ER model. IEICE - Trans. Inf. Syst., E89-D, 1321–1326.
  18. Tan, H.B.K., Zhao, Y. and Zhang, H. (2009) Conceptual data model-based software size estimation for information systems. ACM Trans. Softw. Eng. Methodol., 19, 1–37.
  19. Harizi, M. (2012) the role of class diagram in estimating software size. Int. J. Comput. Appl., 44, 31–33.
  20. Lazic, L., Petrovic, M. and Spalevic, P. (2012) Comparative study on applicability of four software size estimation models based on lines of code. In Proc. ECC 12, Prague, Szeh Republic, September 24–26, pp. 71–80. WSEAS Press, Zografou, Athens, Greece.
  21. Alashhb, M.I. and Lazic, L. (2016) A critical review of source code size estimation approaches for object-oriented programming languages: a comparative study. In INFOTEH-JAHORINA 16, Jahorina, Bosnia, March 16–18, pp. 535–540. IEEE, Washington, DC.
  22. Ayyildiz, T.E. and Koyiit, A. (2018) Size and effort estimation based on problem domain measures for object oriented software. Int. J. Softw. Eng. Knowl. Eng., 28, 219–238.
  23. Ayyildiz, T.E. (2015) Size and effort estimation based on correlations between problem and solution domain measures for object oriented software. Ph.D. dissertation. In The Department of Information Systems. Middle East Technical University, Ankara, Turkey.
  24. Kiewkanya, M. and Surak, S. (2016) Constructing C++ software size estimation model from class diagram. In Proc. JCSSE 16, Khon Kaen, Thailand, July 13–15, pp. 1–6. IEEE, Washington, DC.
  25. Badri, M., Badri, L. and Flageol, W. (2016) Source and test code size prediction-A comparison between use case metrics and objective class points. In Proc. ENASE 16, Rome, Italy, April 27– 28, pp. 172–180. Springer, Berlin.
  26. Object Management Group. About the UML specification version 2.5.1. https://www.omg.org/spec/UML/About-UML/ (accessed October 11, 2021).
  27. R. T. Hughes, ‘‘Expert judgment as an estimating method,’’ Inf. Softw. Technol., vol. 38, no. 2 pp. 67–75, Jan. 1996.
  28. R. Valerdi, B. W. Boehm, and D. J. Reifer, ‘‘COSYSMO: A constructive systems engineering cost model coming of age,’’ in Proc. INCOSE Int. Symp., vol. 13, no. 1. Hoboken, NJ, USA: Wiley, 2003, pp. 70–82.
  29. Y. Singh, P. K. Bhatia, and O. Sangwan, ‘‘ANN model for predicting software function point metric,’’ ACM SIGSOFT Softw. Eng. Notes, vol. 34, no. 1, pp. 1–4, Jan. 2009.
  30. P. Pospieszny, B. Czarnacka-Chrobot, and A. Kobylinski, “An effective approach for software project effort and duration estimation with machine learning algorithms,’’ J. Syst. Softw., vol. 137, pp. 184–196, Mar. 2018, doi: 10.1016/j.jss.2017.11.066
  31. M. Salmanoglu, T. Hacaloglu, and O. Demirors, ‘‘Effort estimation for agile software development: Comparative case studies using COSMIC functional size measurement and story points,’’ in Proc. ACM Int. Conf. Proc., 2017, pp. 41–49, doi: 10.1145/3143434.3143450.
  32. Pasuksmit, J., Thongtanunam, P., & Karunasekera, S. (2022). Story points changes in agile iterative development. Empirical Software Engineering, 27(6), 1-55.
  33. Roger S Pressman and Bruce R Maxim, 2020 “software engineering a practitioners approach”.
  34. Del Bianco, V., & Lavazza, L. (2006, October). Object-oriented model size measurement: experiences and a proposal for a process. In Workshop on Model Size Metrics, part of the ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MoDELS 2006), Genova.
  35. Amanullah, K., & Bell, T. (2019, October). Evaluating the use of remixing in scratch projects based on repertoire, lines of code (LOC), and elementary patterns. In 2019 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). IEEE.
  36. Aswini, S., & Yazhini, M. (2017). An assessment framework of routing complexities using LOC metrics. 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), 1-6.Software Size Estimation with Deep Learning Model, 2020.
  37. Morrow, P. (2018). Software sizing for cost/schedule estimation (Doctoral dissertation, Ulster University).
  38. Agresti, A. (2010). Analysis of ordinal categorical data (Vol. 656). John Wiley & Sons.
  39. Stern, S., & Gencel, C. (2010, November). Embedded software memory size estimation using COSMIC: a case study. In Int’l Workshop on Software Measurement (IWSM) (Vol. 39).
  40. Abdullah, Nur Atiqah Sia, Nur Ida Aniza Rusli, and Mohd Faisal Ibrahim. "A case study in COSMIC functional size measurement: angry bird mobile application." In 2013 IEEE Conference on Open Systems (ICOS), pp. 139-144. IEEE, 2013.
  41. Chander Diwaker, Astha Dhiman, “Estimating Size and Effort Estimation Techniques for Software Development,” International Journal of Software and Web Sciences (IJSWS), pp. 2279–0071, May. 2013.
  42. Ahmed, A.T. and Taha, D.B., Webapp Effort Estimation using Cosmic Method. International Journal of Computer Applications, (2018):  975, p.8887.
  43. https://en.wikipedia.org/wiki/Class_diagram
  44. Ungan, E., Trudel, S. and Abran, A. (2018) Analysis of the gap between initial estimated size and final (true) size of implemented software. In IWSM /Mensura 18, Beijing, China, September 19–20, pp. 123–137. CEUR Workshop Proceedings, Aachen, Germany.

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