Estimators Characteristics and Effort Estimation of Software Projects

Hrvoje Karna, Sven Gotovac


Effort estimation is an important part of software project management. Accurate estimates ensure planned project execution and compliance with the set time and budget constraints. Despite attempts to produce accurate estimates by using formal models there is no substantial evidence that these methods guarantee better estimates than those experts make. In order to improve the effort estimation process it is crucial to enhance understanding of the human estimator. When producing estimates each expert exhibits mental effort. In such situation estimator relies on his personal characteristics, some of which are, in context of effort estimation, more important than others. This research tries to identify these characteristics and their relative influences. Data for the research have been collected from projects executed in large company specialized for development of IT solutions in telecom domain. For identification of expert characteristics data mining approach is used (the multilayer perceptron neural network). We considered the use of this method as it is similar to the way human brain operates. Data sets used in modelling contain more than 2000 samples collected from analysed projects. The obtained results are highly intuitive and later could be used in the assessment of reliability of each estimator and estimates he produces.


  1. Albrecht, A. and Gaffney, J. (1983) 'Software function, source lines of code, and development effort prediction: a software science validation', IEEE Transactions on Software Engineering, vol. 9, no. 6, November, pp. 639-648.
  2. Abbas, A. S. et al, (2012) 'Neural Net Back Propagation and Software Effort Estimation', ARPN Journal of Systems and Software, vol. 2, no. 6, June.
  3. Basha, S. and Ponnurangam, D. (2010) 'Analysis of Empirical Software Effort Estimation Models', International Journal of Computer Science and Information Security, vol. 7, no. 3, March.
  4. Boehm, B., (1981) 'Software Engineering Economics', Englewood Cliffs, Prentice Hall, NJ, USA.
  5. Boetticher, G., Lokhandwala, N., and Helm, J. (2006) 'Understanding the Human Estimator', Second International Predictive Models in Software Engineering (PROMISE) Workshop co-located at the 22nd IEEE International Conference on Software Maintenance, Philadelphia, PA.
  6. Boetticher, G. and Lokhandwala, N. (2007) 'Assessing the Reliability of a Human Estimator', Third International Predictive Models in Software Engineering (PROMISE) Workshop as part of the International Conference on Software Engineering, Minneapolis, MN.
  7. Boetticher, G. (2001) 'Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-Starved Domains', Model Based Requirements Workshop, San Diego, pp. 17 - 24.
  8. Cheng, B. and Xuejun, Y. (2012) 'The Selection of Agile Development's Effort Estimation Factors based on Principal Component Analysis', Proceedings of International Conference on Information and Computer Applications, vol. 24, pp. 112.
  9. Coelho, E. and Basu, A. (2012) 'Effort Estimation in Agile Software Development using Story Points', International Journal of Applied Information Systems (IJAIS), August, vol. 3, no. 7.
  10. Conte, S. D., Dunsmore, H. E., and Shen, V. Y. (1986) 'Software Engineering Metrics and Models', Menlo Park, CA, Benjamin-Cummings.
  11. Dave, V. S. and Dutta K. (2012) 'Neural network based models for software effort estimation: a review', Artificial Intelligence Review.
  12. Faria, P. and Miranda E. (2012) 'Expert Judgment in Software Estimation during the Bid Phase of a Project - An Exploratory Survey', Software Measurement and the 2012 Seventh International Conference on Software Process and Product Measurement (IWSMMENSURA), October 2012 Joint Conference of the 22nd International Workshop, pp. 126-131.
  13. Ferrucci, F. et al (2010) 'Genetic Programming for Effort Estimation an Analysis of the Impact of Different Fitness Functions', 2nd International Symposium on Search Based Software Engineering, Benevento, Italy, September, pp. 89-98.
  14. Gonzalez, L. R. (2008) 'Neural Networks for Variational Problems in Engineering', PhD thesis. Technical University Catalonia.
  15. Grimstad, S. and Jørgensen, M. (2007) 'Inconsistency of Expert Judgment-based Estimates of Software Development Effort', Journal of Systems and Software archive, vol. 80, no. 11, November, pp. 1770-1777.
  16. Hill, J., Thomas, L.C., and Allen, D.E. (2000) 'Experts estimates of task durations in software development projects', International Journal of Project Management, vol. 18, no. 1, February, pp. 13-21.
  17. Humphrey, W.S. et al (2007) 'Future Directions in Process Improvement'. CrossTalk The Journal of Defense Software Engineering, vol. 20, no. 2, February, pp.17- 22.
  18. Jørgensen, M. et al (2000) 'Human judgement in effort estimation of software projects', Presented at Beg, Borrow, or Steal Workshop, International Conference on Software Engineering, June, Limerick, Ireland.
  19. Jørgensen, M. (2004) 'Top-down and Bottom-Up Expert Estimation on Software Development Effort', Journal of Information and Software Technology, vol.46, no. 1, January, pp. 3-16.
  20. Jørgensen, M., Boehm, B. and Rifkin, S. (2009) 'Software Development Effort Estimation: Formal Models or Expert Judgment?78, IEEE Software, vol. 26, no. 2, March-April, pp. 14-19.
  21. Jørgensen, M. (2007) 'Estimation of Software Development Work Effort: Evidence on Expert Judgment and Formal Models', International Journal of Forecasting, vol. 23, no. 3, pp. 449-462.
  22. Jørgensen, M. (2005) 'Practical guidelines for expertjudgment-based software effort estimation', IEEE Software, vol. 22, no. 3, May-June, pp. 57-63.
  23. Jørgensen, M. (2014) 'What We Do and Don't Know about Software Development Effort Estimation', IEEE Software, vol. 31 no. 2, pp. 37-40.
  24. Keung, J. (2009) 'Software Development Cost Estimation using Analogy: A Review', In proceeding of 20th Australian Software Engineering Conference, Gold Cost, Australia, April, pp.327-336.
  25. Layman, L. et al (2008) 'Mining Software Effort Data: Preliminary Analysis of Visual Studio Team System Data', Proceedings of the 2008 International Working Conference on Mining Software Repositories, May, pp.43-46.
  26. Lin S. W and Bier V. M. (2008) 'A study of expert overconfidence', Reliability Engineering & System Safety, vol. 93, no. 5, pp. 711-721.
  27. Moløkken, M. and Jørgensen, M. (2004) 'A review of surveys on Software Effort Estimation', International Symposium on Empirical Software Engineering, September-October, pp. 223-230.
  28. Nisbet, R., Elder, J. and Miner, G. (2009) 'Handbook of Statistical Analysis and Data Mining Applications', Elsevier Inc.
  29. Rojas, R. (1996) 'Neural Networks - A Systematic Introduction', Springer-Verlag, Berlin, New-York.
  30. Satyananda, C. R. and Raju, K. (2009) 'A Concise Neural Network Model for Estimating Software Effort', International Journal of Recent Trends in Engineering, vol. 1, no. 1, May.
  31. Singh, J. and Sahoo, B. (2011) 'Software Effort Estimation with Different Artificial Neural Network', International Journal of Computer Applications (IJCA) - Special Issue on 2nd National Conference - Computing, Communication and Sensor Network (CCSN), vol. 4, pp. 13-17.
  32. Shepperd, M., Schofield, C., and Kitchenham, B. (1996) 'Effort estimation using analogy', In International Conference on Software Engineerin, March, pp. 170- 178.
  33. Shepperd, M. and Schofield, C. (1997) 'Estimating software project effort using analogies', IEEE Transactions on Software Engineering, vol. 23, no. 11, November, pp. 736-743.
  34. Shepperd, M. (2007) 'Software project economics - a roadmap', Future of Software Engineering - 29th International Conference on Software Engineering, Minneapolis, MN, USA, May, pp. 304-315.
  35. Stensrud, E. et al (2003) 'A Further Empirical Investigation of the Relationship Between MRE and Project Size', Empirical Software Engineering, vol. 8, no. 2, pp. 139-161.
  36. Tadayon, N. (2005) 'Neural Network Approach for Software Cost Estimation', Proceedings of the International Conference on Information Technology: Coding and Computing, vol. 2, April, pp. 815-818.
  37. Wang, Y. (2007) 'On Laws of Work Organization in Human Cooperation', International Journal of Cognitive Informatics and Natural Intelligence, vol. 1, no. 2, pp. 1-15.
  38. Xie, T. (2013) 'Synergy of Human and Artificial Intelligence in Software Engineering', In Proceedings of the 2nd International NSF sponsored Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, San Francisco, CA.
  39. Xie, T. et al (2009) 'Data Mining for Software Engineering', IEEE Computer, vol. 42, no. 8, August, pp. 35-42.
  40. Rojas, R., (1996) 'Neural Networks - A Systematic Introduction', Springer-Verlag, Berlin, NY, USA.
  41. Ziauddin et al. (2012) 'An Effort Estimation Model for Agile Software Development', Advances in Computer Science and its Applications (ACSA), vol. 2, no. 1.
  42. Zulkefli M. et al. (2011) 'Review on Traditional and Agile Cost Estimation Success Factor in Software Development Project', International Journal on New Computer Architectures and Their Applications (IJNCAA), vol. 1, no. 3, pp. 942-952.

Paper Citation

in Harvard Style

Karna H. and Gotovac S. (2014). Estimators Characteristics and Effort Estimation of Software Projects . In Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014) ISBN 978-989-758-036-9, pages 26-35. DOI: 10.5220/0005002600260035

in Bibtex Style

author={Hrvoje Karna and Sven Gotovac},
title={Estimators Characteristics and Effort Estimation of Software Projects},
booktitle={Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014)},

in EndNote Style

JO - Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014)
TI - Estimators Characteristics and Effort Estimation of Software Projects
SN - 978-989-758-036-9
AU - Karna H.
AU - Gotovac S.
PY - 2014
SP - 26
EP - 35
DO - 10.5220/0005002600260035