Using tabu search to configure support vector regression for effort estimation

Empirical Software Engineering

Volumn 18, Issue 3, 2013, Pages 506-546

  Corazza, A ^a   Di Martino, S ^a   Ferrucci, F ^b   Gravino, C ^b   Sarro, F ^b   Mendes, E ^c  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b UNIVERSITY OF SALERNO   (Italy)

^c ZAYED UNIVERSITY   (United Arab Emirates)

Author keywords

Effort estimation; Search based techniques; Support vector regression; Tabu search

Indexed keywords

EFFORT ESTIMATION; LEAVE-ONE-OUT CROSS VALIDATIONS; PREDICTION ACCURACY; SEARCH-BASED; SETTING OF PARAMETERS; SOFTWARE DEVELOPMENT EFFORT; SUPPORT VECTOR ALGORITHMS; SUPPORT VECTOR REGRESSION (SVR);

REGRESSION ANALYSIS; SOFTWARE DESIGN; TABU SEARCH;

VECTORS;

EID: 84878701385     PISSN: 13823256     EISSN: 15737616     Source Type: Journal    
DOI: 10.1007/s10664-011-9187-3     Document Type: Article

References (70)

1. Albrecht AJ, Gaffney JE (1983) Software function, source lines of code, and development effort prediction: a software science validation. IEEE Trans Softw Eng 9(6):639-648
2. Bailey JW, Basili VR (1981) A meta model for software development resource expenditure. Procs. International Conference on Software Engineering, pp 107-116
3. Braga PL, AL Oliveira, Meira SR (2007) Software effort estimation using machine learning techniques with robust confidence intervals. Procs IEEE International Conference on Hybrid Intelligent Systems, pp 352-357
4. Briand L, Emam KE, Surmann D, Wiekzorek I, Maxwell K (1999) An assessment and comparison of common software cost estimation modeling techniques. Procs. International Conference on Software Engineering
5. Briand L, Langley T, Wiekzorek I (2000) A replicated assessment and comparison of common software cost modeling techniques. Procs. International Conference on Software Engineering, pp 377-386
6. Briand L, Wieczorek I (2002) Software resource estimation. Encyclopedia of Software Engineering, pp 1160-1196
7. Burgess CJ, Lefley M (2001) Can genetic programming improve software effort estimation? A comparative evaluation. Inf Softw Technol 43(14):863-873
8. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
9. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM Regression. Neural Netw 17(1):113-126
10. Chiu N-H, Huang S-J (2007) The adjusted analogy-based software effort estimation based on similarity distances. J Syst Software 80(4):628-640
11. Conte SD, Dunsmore HE, Shen VY (1986) Software engineering metrics and model. Benjamin-Cummins Pub Co, Inc. Redwood City, CA, USA
12. Conover WJ (1998) Practical nonparametric statistics, 3rd edn. Wiley, New York
13. Cook RD (1977) Detection of influential observations in linear regression. Technometrics 19:15-18
14. Corazza A, Di Martino S, Ferrucci F, Gravino C, Mendes E (2009) Applying support vector regression for web effort estimation using a cross-company dataset. Procs. Empirical Software Engineering and Measurement, pp 191-202
15. Corazza A, Di Martino S, Ferrucci F, Gravino C, Mendes E (2011) Investigating the use of Support Vector Regression for Web Effort Estimation. Empir Softw Eng 16(2):211-243
16. Corazza A, Di Martino S, Ferrucci F, Gravino C, Sarro F, Mendes E (2010) How effective is Tabu search to configure support vector regression for effort estimation? Procs. International Conference on Predictive Models in Software Engineering, 4
17. Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning, 20
18. Costagliola G, Di Martino S, Ferrucci F, Gravino C, Tortora G, Vitiello G (2006) Effort estimation modeling techniques: a case study for web applications. Procs. International Conference on Web Engineering, pp 9-16
19. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New York, NY, USA
20. Desharnais JM (1989) Analyse statistique de la productivitie des projets in formatique a partie de la technique des point des fonction, Ph.D. thesis, Unpublished Masters Thesis, University of Montreal
21. Di Martino S, Ferrucci F, Gravino C, Mendes E (2007) comparing size measures for predicting web application development effort: a case study. Procs. Empirical Software Engineering and Measurement, pp 324-333
22. Ferrucci F, Gravino C, Oliveto R, Sarro F (2009) Using Tabu search to estimate software development effort. Procs. International Conferences on Software Process and Product Measurement. LNCS 5891. Springer-Verlag, Berlin-Heidelberg, pp 307-320
23. Ferrucci F, Gravino C, Mendes E, Oliveto R, Sarro F (2010) Investigating Tabu search for web effort estimation. Procs. EUROMICRO Conference on Software Engineering and Advanced Applications, pp 350-357
24. Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers, Boston
25. Hsu C-W, Chang C-C, Lin C-J (2010) A practical guide to support vector classification, available at http://www.csie.ntu.edu.tw/\~{}cjlin/papers/guide/ guide.pdf
26. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1), ACM New York, NY, USA, pp 10-18
27. Hofmann T, Schölkopf B, Smola AJ (2008) Kernel methods in machine learning. Ann Stat 36:1171-1220
28. Jeffery R, Ruhe M, Wieczorek I (2000) A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data. Inf Softw Technol 42:1009-1016
29. Kemerer CF (1987) An empirical validation of software cost estimation models. Commun ACM 30(5):416-429
30. Keerthi S (2002) Efficient tuning of SVM hyper-parameters using radius/margin bound and iterative algorithms. IEEE Trans Neural Netw 13(5):1225-1229
31. Keerthi S, Lin C-J (2003) Asymptotic behaviors of support vector machines with Gaussian Kernel. Neural Comput 15:1667-1689
32. Kitchenham BA, Mendes E, Travassos GH (2007) Cross versus within-company cost estimation studies: a systematic review. IEEE Trans Softw Eng 33(5):316-329
33. Kitchenham B, Pickard LM, MacDonell SG, Shepperd MJ (2001) What accuracy statistics really measure. IEE Proceedings Software 148(3):81-85
34. Kitchenham BA, Mendes E (2004) A comparison of cross-company and single-company effort estimation models for web applications. Procs. Evaluation & Assessment in Software Engineering, pp 47-55
35. Kitchenham BA, Mendes E (2009) Why comparative effort prediction studies may be invalid. Procs. International Conference on Predictor Models in Software Engineering
36. Kitchenham BA (1998) A procedure for analyzing unbalanced datasets. IEEE Trans Softw Eng 24(4):278-301
37. Kitchenham BA, Pickard L, Peeger S (1995) Case studies for method and tool evaluation. IEEE Softw 12(4):52-62
38. Kocaguneli E, Gay G, Menzies T, Yang Y, Keung JW (2010) When to use data from other projects for effort estimation. Procs. IEEE/ACM international conference on Automated Software Engineering, pp 321-324
39. Kwok JT, Tsang IW (2003) Linear dependency between ε and the input noise in ε-support vector regression. IEEE Trans Neural Netw 14(3):544-553
40. Lefley M, Shepperd MJ (2003) Using genetic programming to improve software effort estimation based on general datasets. Procs. GECCO, LNCS 2724, Springer-Verlag, Berlin, Heidelberg, pp 2477-2487
41. Li YF, Xie M, Goh TN (2009) A study of project selection and feature weighting for analogy based software cost estimation. J Syst Software 82(2):241-252
42. Mair C, Shepperd M (2005) The consistency of empirical comparisons of regression and analogy-based software project cost estimation. Procs ISESE, pp 509-518
43. Mattera D, Haykin S (1999) Support vector machines for dynamic reconstruction of a chaotic system. In: Scholkopf B, Burges J, Smola A (eds) Advances in kernel methods: support vector machine. MIT, Cambridge
44. Maxwell (2002) Applied statistics for software managers. Software Quality Institute Series, Prentice Hall, Upper Saddle River, NJ, USA
45. Maxwell K, Wassenhove LS, Dutta S (1999) Performance evaluation of general and company specific models in software development effort estimation. Manag Sci 45(6):787-803
46. Mendes E (2008) The use of bayesian networks for web effort estimation: further investigation. Procs. International Conference on Web Engineering, pp 203-216
47. Mendes E, Pollino C, Mosley N (2009) Building an expert-based web effort estimation model using Bayesian Networks Procs EASE Conference, pp 1-10
48. Mendes E (2009) Web cost estimation and productivity benchmarking. ISSSE, LNCS 5413, Publisher: Springer-Verlag, Berlin Heidelberg, pp 194-222
49. Mendes E, Mosley N, Counsell S (2005a) Investigating web size metrics for early web cost estimation. J Syst Software 77(2):157-172
50. Mendes E, Di Martino S, Ferrucci F, Gravino C (2008) Cross-company vs. single-company web effort models using the Tukutuku database: an extended study. J Syst Software 81(5):673-690
51. Mendes E, Mosley N, Counsell S (2003a) Investigating early web size measures for web cost estimation Procs. Evaluation and Assessment in Software Engineering, pp 1-22
52. Mendes E, Kitchenham BA (2004) Further Comparison of cross-company and within-company effort estimation models for web applications. Procs. IEEE International Software Metrics Symposium, pp 348-357
53. Mendes E, Counsell S, Mosley N, Triggs C, Watson I (2003b) Comparative study of cost estimation models for web hypermedia applications. Empir Softw Eng 8(23):163-196
54. Mendes E, Mosley N, Counsell S (2005b) The need for web engineering: an introduction, web engineering. In: Mendes E, Mosley N (eds). Springer-Verlag, pp 1-28
55. Miyazaki Y, Terakado M, Ozaki K, Nozaki H (1994) Robust regression for developing software estimation models. J Syst Softw 27(1):3-16
56. Moser R, Pedrycz W, Succi G (2007) Incremental effort prediction models in agile development using radial basis functions. Procs. International Conference on Software Engineering and Knowledge Engineering, pp 519-522
57. Oliveira ALI (2006) Estimation of software project effort with support vector regression. Neurocomputing 69(13-15):1749-1753
58. PROMISE (2011) Repository of empirical software engineering data. http://promisedata.org/repository
59. Shepperd MJ, Kadoda G (2001) Using simulation to evaluate prediction techniques. Procs. IEEE International Software Metrics Symposium, pp 349-358
60. Shepperd M, Schofield C (1997) Estimating software project effort using analogies. IEEE Trans Softw Eng 23(11):736-743
61. Shepperd M, Schofield C, Kitchenham BA (1996) Effort estimation using analogy. Procs. International Conference on Software Engineering, pp 170-178
62. Shin M, Goel AL (2000) Empirical data modeling in software engineering using radical basis functions. IEEE Trans Softw Eng 26(6):567-576
63. Scholkopf B, Smola A (2002) Learning with Kernels. MIT Press
64. Schölkopf B, Sung K, Burges C, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with Gaussian Kernels to radial basis function classifiers. IEEE Trans Signal Process 45(11):2758-2765
65. Shukla KK (2000) Neuro-genetic prediction of software development effort. Inf Softw Technol 42(10):701-713
66. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199-222
67. Vapnik V, Chervonenkis A (1964) A note on one class of perceptrons. Automatics and Remote Control 25
68. Vapnik V, Chervonenkis AY (1974) Theory of pattern recognition (in Russian). Nauka, Moscow
69. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag
70. Wieczorek I, Ruhe M (2002) How valuable is company-specific data compared to multi-company data for software cost estimation? Procs. International Software Metrics Symposium, pp 237-246