Kernel methods for software effort estimation: Effects of different kernel functions and bandwidths on estimation accuracy

Empirical Software Engineering

Volumn 18, Issue 1, 2013, Pages 1-24

  Kocaguneli, Ekrem ^a   Menzies, Tim ^a   Keung, Jacky W ^b  

^a West Virginia University   (United States)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Bandwidth; Data mining; Effort estimation; Kernel function

Indexed keywords

ANALOGY BASED ESTIMATION; BANDWIDTH PARAMETERS; DATA SETS; EFFORT ESTIMATION; ESTIMATION PERFORMANCE; EVALUATION CRITERIA; KERNEL DENSITY ESTIMATION; KERNEL FUNCTION; KERNEL METHODS; SOFTWARE EFFORT ESTIMATION; SOFTWARE PROJECT;

DATA MINING; ESTIMATION;

BANDWIDTH;

EID: 84872279818     PISSN: 13823256     EISSN: 15737616     Source Type: Journal    
DOI: 10.1007/s10664-011-9189-1     Document Type: Article

References (59)

1. Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge, MA
2. Angelis L, Stamelos I (2000) A simulation tool for efficient analogy based cost estimation. Empirical Softw Eng 5:35-68
3. Auer M, Trendowicz A, Graser B, Haunschmid E, Biffl S (2006) Optimal project feature weights in analogy-based cost estimation: improvement and limitations. IEEE Trans Softw Eng 32:83-92
4. Baker D (2007) A hybrid approach to expert and model-based effort estimation. Master's thesis, Lane Department of Computer Science and Electrical Engineering, West Virginia University
5. Boehm B, Abts C, Chulani S (2000) Software development cost estimation approaches: a survey. Annals Softw Eng 10:177-205
6. Boehm BW (1981) Software engineering economics. Prentice Hall PTR, Upper Saddle River, NJ, USA
7. Briand L, El Emam K, Bomarius F (1998) Cobra: a hybrid method for software cost estimation, benchmarking, and risk assessment.In: Proceedings of the international conference on software engineering, pp 390-399
8. Briand LC, El Emam K, Surmann D, Wieczorek I, Maxwell KD (1999) An assessment and comparison of common software cost estimation modeling techniques. In: ICSE '99: proceedings of the 21st international conference on software engineering. ACM, New York, NY, USA, pp 313-322
9. Browman HI (1999) Negative results. Mar Ecol Prog Ser 191:301-309
10. Chen Z, Menzies T, Port D (2005) Feature subset selection can improve software cost estimation. In: PROMISE'05: proceedings of the international conference on predictor models in software engineering
11. Cressie NAC (1993) Statistics for spatial data (Wiley series in probability and statistics). Wiley-Interscience
12. Desharnais J (1989) Analyse statistique de la productivitie des projets informatique a partie de la technique des point des fonction. Master's thesis, Univ. of Montreal
13. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley-Interscience, 2 edition.
14. Foss T, Stensrud E, Kitchenham B, Myrtveit I (2003) A simulation study of the model evaluation criterion mmre. IEEE Trans Softw Eng 29(11):985-995
15. Frank E, Hall M, Pfahringer B (2003) Locally weighted naive bayes. In: Proceedings of the conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 249-256
16. Hardle W, Simar L (2003) Applied multivariate statistical analysis. Springer, New York
17. Jeffery R, Ruhe M, Wieczorek I (2001) Using public domain metrics to estimate software development effort. In: METRICS '01: proceedings of the 7th international symposium on software metrics. IEEE Computer Society, Washington, DC, USA, p 16
18. John G, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 338-345
19. Jorgensen M (2004) A review of studies on expert estimation of software development effort. J Syst Softw 70:37-60
20. Kadoda G, Cartwright M, Shepperd M (2000) On configuring a case-based reasoning software project prediction system. UK CBR workshop, Cambridge, UK, pp 1-10
21. Kemerer C (1987) An empirical validation of software cost estimation models. Commun ACM 30(5):416-429
22. Keung J (2008a) Empirical evaluation of analogy-x for software cost estimation. In: ESEM '08: proceedings of the second ACM-IEEE international symposium on empirical software engineering and measurement. ACM, New York, NY, USA, pp 294-296
23. Keung JW (2008b) Theoretical maximum prediction accuracy for analogy-based software cost estimation. In: 2008 15th Asia-Pacific software engineering conference, pp 495-502
24. Keung J, Kitchenham B (2008) Experiments with analogy-x for software cost estimation. In: ASWEC '08: proceedings of the 19th Australian conference on software engineering. IEEE Computer Society, Washington, DC, USA, pp 229-238
25. Keung J, Kocaguneli E, Menzies T (2011) A ranking stability indicator for selecting the best estimator in software cost estimation. Autom Softw Eng (under second round review). http://menzies.us/pdf/11draftranking.pdf
26. Keung JW, Kitchenham BA, Jeffery DR (2008) Analogy-x: providing statistical inference to analogy-based software cost estimation. IEEE Trans Softw Eng 34(4):471-484
27. Kirsopp C, Shepperd M (2003) Case and feature subset selection in case-based software project effort prediction. In: Research and development in intelligent systems XIX: proceedings of ES2002, the twenty-second SGAI international conference on knowledge based systems and applied artificial intelligence, p 61
28. Kitchenham B, Mendes E (2009) Why comparative effort prediction studies may be invalid. In: PROMISE '09: proceedings of the 5th international conference on predictor models in software engineering. ACM, New York, NY, USA, pp 1-5
29. Kitchenham B, Pickard L, MacDonell S, Shepperd M (2001) What accuracy statistics really measure. IEE Softw 148(3):81-85
30. Kläs M, Trendowicz A, Wickenkamp A, Münch J, Kikuchi N, Ishigai Y (2008) The use of simulation techniques for hybrid software cost estimation and risk analysis. Adv Comput 74:115-174
31. Kocaguneli E, Menzies T, Bener A, Keung JW (2011) Exploiting the essential assumptions of analogy-based effort estimation. IEEE Trans Softw Eng. doi: 10.1109/TSE.2011.27
32. Kultur Y, Kocaguneli E, Bener A (2009) Domain specific phase by phase effort estimation in software projects. In: ISCIS 2009: 24th international symposium on computer and information sciences, pp 498-503
33. Li J, Ruhe G (2006) A comparative study of attribute weighting heuristics for effort estimation by analogy. In: Proceedings of the 2006 ACM/IEEE international symposium on empirical software engineering, vol 13, pp 63-96
34. Li J, Ruhe G (2008) Analysis of attribute weighting heuristics for analogy-based software effort estimation method aqua+. Empirical Softw Eng 63-96
35. Li J, Ruhe G, Al-emran A, Richter MM (2007) A flexible method for software effort estimation by analogy. Empirical Softw Eng 12:65-106
36. Li Y, Xie M, Goh T (2009) A study of project selection and feature weighting for analogy based software cost estimation. J Syst Softw 82:241-252
37. Mendes E, Mosley N (2002) Further investigation into the use of cbr and stepwise regression to predict development effort for web hypermedia applications. In: International symposium on empirical software engineering, pp 79-90
38. Mendes E, Mosley N (2008) Bayesian network models for web effort prediction: a comparative study. IEEE Trans Softw Eng 34:723-737
39. Mendes E, Mosley N, Watson I (2002) A comparison of case-based reasoning approaches. In: WWW '02: proceedings of the 11th international conference on world wide web. ACM, New York, NY, USA, pp 272-280
40. Mendes E, Watson ID, Triggs C, Mosley N, Counsell S (2003) A comparative study of cost estimation models for web hypermedia applications. Empirical Softw Eng 8(2):163-196
41. Menzies T, Chen Z, Hihn J, Lum K (2006) Selecting best practices for effort estimation. IEEE Trans Softw Eng 32:883-895
42. Menzies T, Elrawas O, Hihn J, Feather M, Madachy R, Boehm B (2007) The business case for automated software engineering. ASE, pp 303-312
43. Menzies T, Jalali O, Hihn J, Baker D, Lum K (2010) Stable rankings for different effort models. Autom Softw Eng 17:409-437
44. Milic D, Wohlin C (2004) Distribution patterns of effort estimations. In: Euromicro conference
45. Moløkken-Østvold K, Jørgensen M, Tanilkan SS, Gallis H, Lien AC, Hove SE (2004) A survey on software estimation in the Norwegian industry. In: IEEE international symposium on software metrics, pp 208-219
46. Pal SK, Shiu SCK (2001) Foundations of soft case-based reasoning. Cambridge University Press, Cambridge, UK
47. Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2003) Distributed deviation detection in sensor networks. SIGMOD Rec 32:2003
48. Pendharkar PC, Subramanian GH, Rodger JA (2005) A probabilistic model for predicting software development effort. IEEE Trans Softw Eng 31:615-624
49. Robson C (2002) Real world research: a resource for social scientists and practitioner-researchers. Blackwell Publisher Ltd.
50. Scheid S (2004) Introduction to kernel smoothing. Talk
51. Scott DW (1992) Multivariate density estimation: theory, practice, and visualization (Wiley series in probability and statistics). Wiley-Interscience
52. Shepperd M (2007) Software project economics: a roadmap. In: FOSE '07: future of software engineering, pp 304-315
53. Shepperd M, Kadoda G (2001) Comparing software prediction models using simulation. IEEE Trans Softw Eng, pp 1014-1022
54. Shepperd M, Schofield C (1997) Estimating software project effort using analogies. IEEE Trans Softw Eng 23(11):736-743
55. Shepperd M, Schofield C, Kitchenham B (1996) Effort estimation using analogy. In: International conference on software engineering, pp 170-178
56. Stensrud E, Foss T, Kitchenham B, Myrtveit I (2002) An empirical validation of the relationship between the magnitude of relative error and project size. In: METRICS '02: proceedings of the 8th international symposium on software metrics. IEEE Computer Society, Washington, DC, USA, p 3
57. Trendowicz A, Heidrich J, Münch J, Ishigai Y, Yokoyama K, Kikuchi N (2006) Development of a hybrid cost estimation model in an iterative manner. In: Proceedings of the 28th international conference on software engineering, ICSE '06. ACM, New York, NY, USA, pp 331-340
58. Walkerden F, Jeffery R (1999) An empirical study of analogy-based software effort estimation. Empirival Softw Eng 4(2):135-158
59. Wand MP, Jones MC (1994) Kernel smoothing (monographs on statistics and applied probability). Chapman & Hall/CRC, London, UK