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

Journal of Computer & Robotics

مقالات آماده انتشار، پذیرفته شده ، انتشار آنلاین از تاریخ 27 شهریور 1397  

نویسندگان

Sakineh Asghari Agcheh Dizaj¹؛ Farhad Soleimanian Gharehchopogh ²

¹Department of Computer Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran

²Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

چکیده

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 have proposed 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. In the end, the proposed model was investigated and assessed on four various datasets based on seven criteria. The results indicate that mainly the proposed model has improved accuracy in the SCE and it reduced errors' value in comparison with any models. Experimental results show that 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, experimental results on datasets show that the proposed model significantly outperforms from GA and BA and also many other recent SCE models.

کلیدواژه‌ها

Software Cost Estimation؛ Bat Algorithm؛ genetic algorithm؛ COCOMO Model؛ Optimization