A New Approach to Software Cost Estimation by Improving Genetic Algorithm with Bat Algorithm

Because of the low accuracy of estimation and uncertainty of the techniques used in the past to Software Cost Estimation (SCE), software producers face a high risk in practice with regards to software projects and they often fail in such projects. Thus, SCE as a complex issue in software engineering requires new solutions, and researchers make an effort to make use of Meta-heuristic algorithms to solve this complicated and sensitive issue. In this paper, we propose a new method by improving Genetic Algorithm (GA) with Bat Algorithm (BA), considering the effect of qualitative factors and false variables in the relations concerning the total estimation of the cost. The proposed method was investigated and assessed on four various datasets based on seven criteria. The experimental results indicate that the proposed method mainly improves accuracy in the SCE and it reduced errors' value in comparison with other models. In the results obtained, Mean Magnitude of Relative Error (MMRE) on NASA60, NASA63, NASA93, and KEMERER is 17.91, 34.80, 41.97, and 95.86, respectively. In addition, the experimental results on datasets show that the proposed method significantly outperforms GA and BA and also many other recent SCE methods.