ADJUSTING ANALOGY SOFTWARE EFFORT ESTIMATION BASED ON FUZZY LOGIC

Mohammad Azzeh, Daniel Neagu, Peter Cowling

Abstract

Analogy estimation is a well known approach for software effort estimation. The underlying assumption of this approach is the more similar the software project description attributes are, the more similar the software project effort is. One of the difficult activities in analogy estimation is how to derive a new estimate from retrieved solutions. Using retrieved solutions without adjustment to considered problem environment is not often sufficient. Thus, they need some adjustment to minimize variation between current case and retrieved cases. The main objective of the present paper is to investigate the applicability of fuzzy logic based software projects similarity measure to adjust analogy estimation and derive a new estimate. We proposed adaptation techniques which take into account the similarity between two software projects in terms of each feature. In earlier work, a similarity measure between software projects based on fuzzy C-means has been proposed and validated theoretically against some well known axioms such as: Normality, Symmetry, transitivity, etc. This similarity measure will be guided towards deriving a new estimate.

References

  1. Auer S. B. M. 2004. Increasing the Accuracy and Reliability of Analogy-Based Effort Estimation with Extensive Project Feature Dimension Weighting, Proceedings of the International Symposium on Empirical Software Engineering (ISESE'04), 147-155.
  2. Azzeh M., Neagu D., Cowling P., 2008, Software Project Similarity measurement based on Fuzzy C-means, International Conference on Software Process. Springer.
  3. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, New York (1981)
  4. Boetticher G., Menzies T. Ostrand T. 2007. PROMISE Repository of empirical software engineering data http://promisedata.org/ repository, West Virginia University, Department of Computer Science.
  5. Briadn L., Langley T., Wieczorek I. 2000, Using the European Space agency data set: a replicated assessment and comparison of common software cost modeling techniques, 22nd IEEE international conference on software engineering.
  6. Chiu N.-H., Huang S.-J. 2007. The adjusted analogy-based software effort estimation based on similarity distances. Journal of Systems and Software 80, 628- 640.
  7. Huang, S.J., Chiu, N.H.: optimization of analogy weights by genetic algorithm for software effort estimation. J. Information and software technology (Elsevier) 48 , 1034-1045 (2006)
  8. Idri, A., Abran, A: A fuzzy logic based set of measures for software project similarity: validation and possible improvements. In: Seventh International Software Metrics Symposium, pp. 85-96, London (2001)
  9. Idri, A., Abran, A., Khoshgoftaar, T. 2001. Fuzzy Analogy: a New Approach for Software Effort Estimation, 11th International Workshop in Software Measurements. 93-101.
  10. ISBSG. 2007. International Software Benchmarking standards Group, Data repository release 10, Site: http://www.isbsg.org.
  11. Jorgensen M., Indahl U., and Sjoberg D., Software effort estimation by analogy and "regression toward the mean". Journal of Systems and Software 68 (2003) 253-262.
  12. Kadoda, G., Michelle C., Chen, L., Shepperd, M. 2000. Experience using Case Based reasoning to predict Software project effort, Proceeding of Fourth international conference on empirical Assessment and evaluation in software engineering. 1-17.
  13. Kirsopp, C., Shepperd, M. 2002. Case and Feature Subset Selection in Case-Based Software Project Effort Prediction, Proc. 22nd SGAI Int'l Conf. KnowledgeBased Systems and Applied Artificial Intelligence.
  14. Kirsopp C. , Shepperd M. J. , Hart J. 2002. Search Heuristics, Case-based Reasoning and Software Project Effort Prediction, Proceedings of the Genetic and Evolutionary Computation Conference, 1367- 1374.
  15. Mendes E., Mosley N. 2002. Further investigation into the use of CBR and stepwise regression to predict development effort for Web hypermedia applications, 79-90.
  16. Mendes, E., Mosley, N., Counsell, S. 2003. Do adaptation rules improve web effort estimation?, In Proceedings of the fourteenth ACM conference on Hypertext and hypermedia (Nottingham, UK). 173-183.
  17. Menzies, T. , Chen Z., Hihn, J. Lum, K. 2006. Selecting Best Practices for Effort Estimation. IEEE Transaction on Software Engineering. 32, 883-895
  18. Mittas N., Athanasiades M., Angelis L., 2007, improving analogy-based software cost estimation by a resampling Method. J. of Information and software technology
  19. Myrtveit I., Stendsrud E., 1999, A controlled experiment to assess the benefits of estimating with analogy and regression models, IEEE transactions on software engineering 25 4, 510-525
  20. Ross, T.J. 2004. Fuzzy Logic with engineering applications, John Wiley& Sons
  21. Sankar K. Pal and Simon C. K. Shiu , 2004, Foundations of Soft Case-Based Reasoning. John Wiley & Sons.
  22. Sentas, P., Angelis, L.: Categorical missing data imputation for software cost estimation by multinomial logistic regression. J. Systems and Software 79, 4040-414 (2006)
  23. Shepperd, M. J., Schofield, C. 1997. Estimating Software Project Effort Using Analogies, IEEE Trans. Software Eng. 23, 736-743.
  24. Stamelos, I., Angelis, L., Morisio, M.: Estimating the development cost of custom software. J. Information and management (Elsevier) 40. 729-741 (2003)
  25. Tron, F., Stensrud, E., Kitchenham, B., Myrtveit, I. 2003. A Simulation Study of the Model Evaluation Criterion MMRE, IEEE Transactions on Software Engineering, 29, 985-995.
  26. Xu, Z., Khoshgoftaar, T., 2004. Identification of fuzzy models of software effort estimation. Fuzzy Sets and Systems 145, 141-163
  27. Zadeh, L. 1997. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Journal of fuzzy sets and systems 90, 111-127.
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Paper Citation


in Harvard Style

Azzeh M., Neagu D. and Cowling P. (2008). ADJUSTING ANALOGY SOFTWARE EFFORT ESTIMATION BASED ON FUZZY LOGIC . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-989-8111-52-4, pages 127-132. DOI: 10.5220/0001876601270132


in Bibtex Style

@conference{icsoft08,
author={Mohammad Azzeh and Daniel Neagu and Peter Cowling},
title={ADJUSTING ANALOGY SOFTWARE EFFORT ESTIMATION BASED ON FUZZY LOGIC},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2008},
pages={127-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001876601270132},
isbn={978-989-8111-52-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - ADJUSTING ANALOGY SOFTWARE EFFORT ESTIMATION BASED ON FUZZY LOGIC
SN - 978-989-8111-52-4
AU - Azzeh M.
AU - Neagu D.
AU - Cowling P.
PY - 2008
SP - 127
EP - 132
DO - 10.5220/0001876601270132