A Comparison of Techniques for Web Effort Estimation

The objective of this paper is to extend the work by Mendes (2007), and to compare four techniques for Web effort estimation to identify which one provides best prediction accuracy. We employed four effort estimation techniques - Bayesian networks (BN), forward stepwise regression (SWR), case-based reasoning (CBR) and Classification and regression trees (CART) to obtain effort estimates. The dataset employed was of 150 Web projects from the Tukutuku dataset. Results showed that predictions obtained using a BN were significantly superior to those using other techniques. A model that incorporates the uncertainty inherent in effort estimation, can outperform other commonly used techniques, such as those used in this study.

[1]  Emilia Mendes,et al.  Early Web size measures and effort prediction for Web costimation , 2003, Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No.03EX717).

[2]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[3]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[4]  Kathryn B. Laskey,et al.  Network Engineering for Complex Belief Networks , 1996, UAI.

[5]  Emilia Mendes,et al.  Web development effort estimation using analogy , 2000, Proceedings 2000 Australian Software Engineering Conference.

[6]  Emilia Mendes,et al.  Further comparison of cross-company and within-company effort estimation models for Web applications , 2004 .

[7]  Emilia Mendes,et al.  A Comparative Study of Cost Estimation Models for Web Hypermedia Applications , 2003, Empirical Software Engineering.

[8]  Emilia Mendes,et al.  Web Effort Estimation , 2006, Web Engineering.

[9]  Kevin B. Korb,et al.  Parameterising Bayesian Networks , 2004, Australian Conference on Artificial Intelligence.

[10]  D. Ross Jeffery,et al.  Cost estimation for web applications , 2003, 25th International Conference on Software Engineering, 2003. Proceedings..

[11]  Donald J. Reifer.  Ten Deadly Risks in Internet and Intranet Software Development , 2002, IEEE Softw..

[12]  Emilia Mendes,et al.  Comparison of Web size measures for predicting Web design and authoring effort , 2002, IEE Proc. Softw..

[13]  Luciano Baresi,et al.  Estimating the design effort of Web applications , 2003, Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No.03EX717).

[14]  Andrew K. C. Wong,et al.  Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Kevin B. Korb,et al.  Bayesian Artificial Intelligence , 2004, Computer science and data analysis series.

[16]  Roberto Paiano,et al.  MMWA: a software sizing model for Web applications , 2003, Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003..

[17]  Emilia Mendes,et al.  Web Metrics-Estimating Design and Authoring Effort , 2001, IEEE Multim..

[18]  Genny Tortora,et al.  Effort estimation modeling techniques: a case study for web applications , 2006, ICWE '06.

[19]  S. Lauritzen.  The EM algorithm for graphical association models with missing data , 1995 .

[20]  Parag C. Pendharkar,et al.  A probabilistic model for predicting software development effort , 2003, IEEE Transactions on Software Engineering.

[21]  Ioannis Stamelos,et al.  On the use of Bayesian belief networks for the prediction of software productivity , 2003, Inf. Softw. Technol..

[22]  Emilia Mendes,et al.  Investigating Web size metrics for early Web cost estimation , 2005, J. Syst. Softw..

[23]  Donald J. Reifer,et al.  Web Development: Estimating Quick-to-Market Software , 2000, IEEE Softw..

[24]  Marek J. Druzdzel,et al.  Knowledge Engineering for Very Large Decision-analytic Medical Models , 1999, AMIA.

[25]  Martin Shepperd,et al.  Using Simulation to Evaluate Prediction Techniques , 2001 .

[26]  Arno J. Knobbe,et al.  Numbers in Multi-relational Data Mining , 2005, PKDD.

[27]  William Marsh,et al.  Making resource decisions for software projects , 2004, Proceedings. 26th International Conference on Software Engineering.

[28]  Emilia Mendes,et al.  Measurement, prediction and risk analysis for Web applications , 2001, Proceedings Seventh International Software Metrics Symposium.