Kernel methods for software effort estimation

Ekrem Kocagüneli1, Tim Menzies1, Jacky Keung2
1Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, USA
2Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong

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Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge, MA

Angelis L, Stamelos I (2000) A simulation tool for efficient analogy based cost estimation. Empirical Softw Eng 5:35–68

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

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

Boehm B, Abts C, Chulani S (2000) Software development cost estimation approaches: a survey. Annals Softw Eng 10:177–205

Boehm BW (1981) Software engineering economics. Prentice Hall PTR, Upper Saddle River, NJ, USA

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

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

Browman HI (1999) Negative results. Mar Ecol Prog Ser 191:301–309

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

Cressie NAC (1993) Statistics for spatial data (Wiley series in probability and statistics). Wiley-Interscience

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

Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley-Interscience, 2 edition.

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

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

Hardle W, Simar L (2003) Applied multivariate statistical analysis. Springer, New York

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

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

Jorgensen M (2004) A review of studies on expert estimation of software development effort. J Syst Softw 70:37–60

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

Kemerer C (1987) An empirical validation of software cost estimation models. Commun ACM 30(5):416–429

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

Keung JW (2008b) Theoretical maximum prediction accuracy for analogy-based software cost estimation. In: 2008 15th Asia-Pacific software engineering conference, pp 495–502

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

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

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

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

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

Kitchenham B, Pickard L, MacDonell S, Shepperd M (2001) What accuracy statistics really measure. IEE Softw 148(3):81–85

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

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

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

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

Li J, Ruhe G (2008) Analysis of attribute weighting heuristics for analogy-based software effort estimation method aqua+. Empirical Softw Eng 63–96

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

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

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

Mendes E, Mosley N (2008) Bayesian network models for web effort prediction: a comparative study. IEEE Trans Softw Eng 34:723–737

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

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

Menzies T, Chen Z, Hihn J, Lum K (2006) Selecting best practices for effort estimation. IEEE Trans Softw Eng 32:883–895

Menzies T, Elrawas O, Hihn J, Feather M, Madachy R, Boehm B (2007) The business case for automated software engineering. ASE, pp 303–312

Menzies T, Jalali O, Hihn J, Baker D, Lum K (2010) Stable rankings for different effort models. Autom Softw Eng 17:409–437

Milic D, Wohlin C (2004) Distribution patterns of effort estimations. In: Euromicro conference

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

Pal SK, Shiu SCK (2001) Foundations of soft case-based reasoning. Cambridge University Press, Cambridge, UK

Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2003) Distributed deviation detection in sensor networks. SIGMOD Rec 32:2003

Pendharkar PC, Subramanian GH, Rodger JA (2005) A probabilistic model for predicting software development effort. IEEE Trans Softw Eng 31:615–624

Robson C (2002) Real world research: a resource for social scientists and practitioner-researchers. Blackwell Publisher Ltd.

Scheid S (2004) Introduction to kernel smoothing. Talk

Scott DW (1992) Multivariate density estimation: theory, practice, and visualization (Wiley series in probability and statistics). Wiley-Interscience

Shepperd M (2007) Software project economics: a roadmap. In: FOSE ’07: future of software engineering, pp 304–315

Shepperd M, Kadoda G (2001) Comparing software prediction models using simulation. IEEE Trans Softw Eng, pp 1014–1022

Shepperd M, Schofield C (1997) Estimating software project effort using analogies. IEEE Trans Softw Eng 23(11):736–743

Shepperd M, Schofield C, Kitchenham B (1996) Effort estimation using analogy. In: International conference on software engineering, pp 170–178

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

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

Walkerden F, Jeffery R (1999) An empirical study of analogy-based software effort estimation. Empirival Softw Eng 4(2):135–158

Wand MP, Jones MC (1994) Kernel smoothing (monographs on statistics and applied probability). Chapman & Hall/CRC, London, UK