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Software effort estimation using FAHP and weighted kernel LSSVM machine

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Abstract

In the life cycle of software product development, the software effort estimation (SEE) has always been a critical activity. The researchers have proposed numerous estimation methods since the inception of software engineering as a research area. The diversity of estimation approaches is very high and increasing, but it has been interpreted that no single technique performs consistently for each project and environment. Multi-criteria decision-making (MCDM) approach generates more credible estimates, which is subjected to expert’s experience. In this paper, a hybrid model has been developed to combine MCDM (for handling uncertainty) and machine learning algorithm (for handling imprecision) approach to predict the effort more accurately. Fuzzy analytic hierarchy process (FAHP) has been used effectively for feature ranking. Ranks generated from FAHP have been integrated into weighted kernel least square support vector machine for effort estimation. The model developed has been empirically validated on data repositories available for SEE. The combination of weights generated by FAHP and the radial basis function (RBF) kernel has resulted in more accurate effort estimates in comparison with bee colony optimisation and basic RBF kernel-based model.

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Authors and Affiliations

  1. I.K.G. Punjab Technical University, Jalandhar, Punjab, India

    Sumeet Kaur Sehra & Yadwinder Singh Brar

  2. Guru Nanak Dev Engineering College, Ludhiana, Punjab, India

    Sumeet Kaur Sehra

  3. Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India

    Navdeep Kaur

  4. Elocity Technology Inc., Toronto, Canada

    Sukhjit Singh Sehra

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  1. Sumeet Kaur Sehra
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  2. Yadwinder Singh Brar
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  3. Navdeep Kaur
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  4. Sukhjit Singh Sehra
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Correspondence to Sumeet Kaur Sehra.

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Sehra, S.K., Brar, Y.S., Kaur, N. et al. Software effort estimation using FAHP and weighted kernel LSSVM machine. Soft Comput 23, 10881–10900 (2019). https://doi.org/10.1007/s00500-018-3639-2

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