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Appropriate number of analogues in analogy based software effort estimation using quality datasets

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Abstract

Analogy-based software effort estimation (ASEE) plays an important role in software development. It attracts the attention of researchers nowadays due to the simplicity of the ASEE reasoning method. ASEE reasoning is considered simple because it is similar to human reasoning. The estimation approach repeatedly uses the effort values of preceding similar projects. In this approach, the appropriate number of similar previous projects to be reused is still a topic of debate in ASEE research studies. The reliability and accuracy of ASEE methods are considerably affected by the quality of software repositories (datasets). Therefore, if a software dataset does not follow the ASEE principle, then it is not considered useful for the ASEE method. This article presents a novel approach for ASEE to find the appropriate number of analogues from quality datasets. In this approach, the data pre-processing stage is based on Spearman’s rank-order correlation and Kruskal–Wallis test. In the proposed approach, it can deal with categorical (both nominal and ordinal) attributes individually. Spearman’s rank-order correlation is used to find reliable numerical and ordinal attributes. Kruskal–Wallis test identifies reliable nominal attributes. Reliable attributes refer to those attributes which significantly influence the effort. The experimental results show that the proposed approach enhances the quality of the dataset, attribute selection from the metadata, and reduces the abnormal observation and overall project development cost.

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https://github.com/Nisha-pal27/ASEE.

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Authors and Affiliations

  1. CSED, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, 211004, India

    Nisha Pal & Dharmendra Kumar Yadav

  2. CSED, Indian Institute of Information Technology, Pune, Maharashtra, 411041, India

    Mahendra Pratap Yadav

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  1. Nisha Pal
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  2. Mahendra Pratap Yadav
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  3. Dharmendra Kumar Yadav
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Pal, N., Yadav, M.P. & Yadav, D.K. Appropriate number of analogues in analogy based software effort estimation using quality datasets. Cluster Comput 27, 531–546 (2024). https://doi.org/10.1007/s10586-023-03967-2

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