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Multi-objective Optimisation, Software Effort Estimation and Linear Models

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

This paper examines the use of a linear model in combination with a multi-objective optimisation. A simple linear model is constructed and trained using data that has been automatically transformed based on skewness. These transformations, and their inverse, can then be used on the test data without having to make any assumptions of the underlying distribution of this data. Using nsga2, the coefficients of the linear model are optimised across a pareto front using 3 objective functions, representing 3 different error measurements. Although nsga2 produces a variety of non-dominated models across the pareto front, we show that the use of these models for creating an ensemble is inappropriate. Our main conclusion is that the use of pareto modelling for creating ensemble methods does not appear to be valuable, although there is some information that can be gained from examining the change in coefficient values of a linear model across the pareto front.

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References

  1. Albrecht, A.J., John, J., Gaffney, E.: Software function, source lines of code, and development effort prediction: A software science validation. IEEE Transactions on Software Engineering 9(6), 639–648 (1983)

    Article  Google Scholar 

  2. Boehm, B.W.: Software Engineering Economics. Prentice-Hall, Inc. (1981)

    Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. on Evol. Comp. 6(8), 182–197 (2002)

    Article  Google Scholar 

  4. Dejaeger, K., Verbeke, W., Martens, D., Baesens, B.: Data mining techniques for software effort estimation: A comparative study. IEEE Transactions on Software Engineering 38(2), 375–398 (2012)

    Article  Google Scholar 

  5. Finnie, G.R., Wittig, G.E., Desharnais, J.-M.: A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models. The Journal of Systems and Software 39(3), 281–290 (1997)

    Article  Google Scholar 

  6. Foss, T., Stensrud, E., Kitchenham, B., Myrtveit, I.: IEEE Trans. Softw. Eng. 29(11), 985–995 (2003)

    Article  Google Scholar 

  7. Joanes, D., Gill, C.: Comparing measures of sample skewness and kurtosis. The Statistician 47(1), 183–189 (1998)

    Google Scholar 

  8. Jorgensen, M., Shepperd, M.: A systematic review of software development cost estimation studies. IEEE Transactions on Software Engineering 33(1), 33–53 (2007)

    Article  Google Scholar 

  9. Kitchenham, B., Mendes, E.: Why comparative effort prediction studies be invalid. In: Proceedings of the 5th International Conference on Predictor Models in Software Engineering, pp. 1–5 (2009)

    Google Scholar 

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

    Article  Google Scholar 

  11. Kutner, M., Nachtsheim, C., Neter, J., Li, W.: Applied Linear Statistical Models, 5th edn. McGraw-Hill/Irwin (2005)

    Google Scholar 

  12. Menzies, T., Caglayan, B., He, Z., Kocaguneli, E., Krall, J., Peters, F., Turhan, B.: The promise repository of empirical software engineering data (2012)

    Google Scholar 

  13. Minku, L., Yao, X.: Ensembles and locality: Insight on improving software effort estimation. Information and Software Technology 55, 1512–1528 (2013)

    Article  Google Scholar 

  14. Minku, L., Yao, X.: Software effort estimation as a multiobjective learning problem. ACM Transactions on Software Engineering and Methodology 22(4), 35–67 (2013)

    Article  Google Scholar 

  15. Nelson, E.: Management Handbook for the Estimation of Computer Programming Costs. Systems Development Corporation (1966)

    Google Scholar 

  16. Park, H., Baek, S.: An empirical validation of a neural network model for software effort estimation. Expert Systems with Applications 35(3), 929–937 (2008)

    Article  Google Scholar 

  17. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2014)

    Google Scholar 

  18. Tronto, I.B., Silva, J., Sant’Anna, N.: Comparison of artificial neural network and regression models in software effort estimation. In: IEEE International Joint Conference on Neural Networks, pp. 771–776. IEEE (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Peter A. Whigham & Caitlin Owen

Authors
  1. Peter A. Whigham
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  2. Caitlin Owen
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Whigham, P.A., Owen, C. (2014). Multi-objective Optimisation, Software Effort Estimation and Linear Models. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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