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Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+

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Abstract

Estimation by analogy (EBA) predicts effort for a new project by aggregating effort information of similar projects from a given historical data set. Existing research results have shown that a careful selection and weighting of attributes may improve the performance of the estimation methods. This paper continues along that research line and considers weighting of attributes in order to improve the estimation accuracy. More specifically, the impact of weighting (and selection) of attributes is studied as extensions to our former EBA method AQUA, which has shown promising results and also allows estimation in the case of data sets that have non-quantitative attributes and missing values. The new resulting method is called AQUA+. For attribute weighting, a qualitative analysis pre-step using rough set analysis (RSA) is performed. RSA is a proven machine learning technique for classification of objects. We exploit the RSA results in different ways and define four heuristics for attribute weighting. AQUA+ was evaluated in two ways: (1) comparison between AQUA+ and AQUA, along with the comparative analysis between the proposed four heuristics for AQUA+, (2) comparison of AQUA+ with other EBA methods. The main evaluation results are: (1) better estimation accuracy was obtained by AQUA+ compared to AQUA over all six data sets; and (2) AQUA+ obtained better results than, or very close to that of other EBA methods for the three data sets applied to all the EBA methods. In conclusion, the proposed attribute weighing method using RSA can improve the estimation accuracy of EBA method AQUA+ according to the empirical studies over six data sets. Testing more data sets is necessary to get results that are more statistical significant.

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Acknowledgements

The authors would like to thank the Alberta Informatics Circle of Research Excellence (iCORE) for its financial support of this research. Thanks are also given to Jim McElroy for his contribution to the improvement of readability of this paper. Special thanks are given to the anonymous reviewers for their valuable and in-depth comments.

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Authors and Affiliations

  1. Software Engineering Decision Support Laboratory, University of Calgary, Calgary, AB, T2N1N4, Canada

    Jingzhou Li & Guenther Ruhe

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  1. Jingzhou Li
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  2. Guenther Ruhe
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Correspondence to Jingzhou Li.

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Editor: José Carlo Maldonado

Appendices

Appendix A

1.1 Definition of Attributes in USP05-FT and USP05-RQ

Table 21 Definition of attributes
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Appendix B

2.1 Detailed Results of the Comparative Study

Table 22 Results of USP05-FT
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Table 23 Results of ISBSG04-2
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Table 24 Results of Mends03
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Table 25 Results of Kem87
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Table 26 Results of Desh89
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Li, J., Ruhe, G. Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+ . Empir Software Eng 13, 63–96 (2008). https://doi.org/10.1007/s10664-007-9054-4

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