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Conclusions

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Practitioner's Knowledge Representation

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Abstract

This chapter details our experience building and validating six different expert-based Web effort estimation models for Information and Communication Technology (ICT) companies in New Zealand and Brazil. All models were created using Bayesian networks, via eliciting knowledge from domain experts, and validated using data from past finished projects. Post-mortem interviews with the participating companies showed that they found the entire process extremely beneficial and worthwhile, and that all the models created remained in use by those companies.

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References

  1. Mendes E (2007) Predicting web development effort using a Bayesian network. In: Proceedings of EASE’07, Swinton, UK, pp 83–93

    Google Scholar 

  2. Mendes E (2007) The use of a Bayesian network for web effort estimation. In: Proceedings of international conference on web engineering, LNCS 4607, pp 90–104

    Google Scholar 

  3. Mendes E (2007) A comparison of techniques for web effort estimation. In: Proceedings of the ACM/IEEE international symposium on empirical software engineering, Madrid, pp 334–343

    Google Scholar 

  4. Mendes E (2008) The use of Bayesian networks for web effort estimation: further investigation. In: Proceedings of ICWE’08, Yorktown Heights, NJ, pp 203–216

    Google Scholar 

  5. Mendes E (2011) Building a web effort estimation model through knowledge elicitation. In: Proceedings of the 13th international conference on enterprise information systems, pp 128–135

    Google Scholar 

  6. Mendes E, Mosley N (2008) Bayesian network models for web effort prediction: a comparative study. Trans Softw Eng 34(6):723–737

    Article  Google Scholar 

  7. Mendes E, Pollino C, Mosley N (2009) Building an expert-based web effort estimation model using Bayesian networks. In: Proceedings of the EASE conference, Swinton, UK, pp 1–10

    Google Scholar 

  8. Mendes E (2011) Improving project management of healthcare projects through knowledge elicitation. In: Miranda IM, Cruz-Cunha MM (eds) Handbook of research on ICTs for healthcare and social services: developments and applications. IGI Global, Hershey

    Google Scholar 

  9. Mendes E (2011) Uncertainty-based software effort estimation. The International Function Points Users Group (IFPUG) Guide to IT and Software Measurement, CRC press

    Google Scholar 

  10. Jensen FV (1996) An introduction to Bayesian networks. UCL Press, London

    Google Scholar 

  11. Pearl J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  12. Fenton N, Marsh W, Neil M, Cates P, Forey S, Tailor M (2004) Making resource decisions for software projects. In: Proceedings of ICSE’04, pp 397–406

    Google Scholar 

  13. Mendes E, Mosley N, Counsell S (2005) Investigating web size metrics for early web cost estimation. J Syst Softw 77(2):157–172

    Article  Google Scholar 

  14. Woodberry O, Nicholson A, Korb K, Pollino C (2004) Parameterising Bayesian networks. In: Proceedings of Australian conference on artificial intelligence, Berlin, pp 1101–1107

    Google Scholar 

  15. Mendes E, Mosley N, Counsell S (2005) The need for web engineering: an introduction. In: Mendes E, Mosley N (eds) Web engineering. Springer, Berlin, pp 1–28. ISBN 3-540-28196-7

    Google Scholar 

  16. Druzdzel MJ, van der Gaag LC (2000) Building probabilistic networks: where do the numbers come from? IEEE Trans Knowl Data Eng 12(4):481–486

    Article  Google Scholar 

  17. Mahoney SM, Laskey KB (1996) Network engineering for complex belief networks. In: Proceedings of 12th annual conference on uncertainty in artificial intelligence, San Francisco, CA, pp 389–396

    Google Scholar 

  18. Neil M, Fenton N, Nielsen L (2000) Building large-scale Bayesian networks. Knowl Eng Rev 15(3):257–284

    Article  MATH  Google Scholar 

  19. Studer R, Benjamins VR, Fensel D (1998) Knowledge engineering: principles and methods. Data Knowl Eng 25:161–197

    Article  MATH  Google Scholar 

  20. Das B (2004) Generating conditional probabilities for Bayesian networks: easing the knowledge acquisition problem. http://arxiv.org/pdf/cs/0411034v1.pdf. Accessed on 2008

  21. Tang Z, McCabe B (2007) Developing complete conditional probability tables from fractional data for Bayesian belief networks. J Comput Civ Eng 21(4):265–276

    Article  Google Scholar 

  22. Baker S, Mendes E (2010) Aggregating expert-driven causal maps for web effort estimation. In: Proceedings of the international conference on advanced software engineering & its applications (ASEA), vol 117, pp 264–282

    Google Scholar 

  23. Baker S, Mendes E (2010) Assessing the weighted sum algorithm for automatic generation of probabilities in Bayesian networks. In: Proceedings of the international conference on information and automation 2010 (ICIA 2010), Harbin, pp 867–873. doi: 10.1109/ICINFA.2010.5512447

  24. Baker S, Mendes E (2010) Evaluating the weighted sum algorithm for estimating conditional probabilities in Bayesian networks. In: Proceedings of the software engineering and knowledge engineering conference (SEKE 2010), pp 319–324

    Google Scholar 

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Authors and Affiliations

  1. Blekinge Institute of Technology, Karlskrona, Sweden

    Emilia Mendes

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  1. Emilia Mendes
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Mendes, E. (2014). Conclusions. In: Practitioner's Knowledge Representation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54157-5_14

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  • DOI: https://doi.org/10.1007/978-3-642-54157-5_14

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  • Print ISBN: 978-3-642-54156-8

  • Online ISBN: 978-3-642-54157-5

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