The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in ABE model in strongly associated with the quality of the dataset since it depends on previous completed projects for estimation. Missing Data (MD) is one of major challenges in software engineering datasets. Several missing data imputation techniques have been investigated by researchers in ABE model. Identification of the most similar donor values from the completed software projects dataset for imputation is a challenging issue in existing missing data techniques adopted for ABE model. In this study, Fuzzy C-Mean Imputation (FCMI), Mean Imputation (MI) and K-Nearest Neighbor Imputation (KNNI) are investigated to impute missing values in Desharnais dataset under different missing data percentages (Desh-Miss1, Desh-Miss2) for ABE model. FCMI-ABE technique is proposed in this study. Evaluation comparison among MI, KNNI, and (ABE-FCMI) is conducted for ABE model to identify the suitable MD imputation method. The results suggest that the use of (ABE-FCMI), rather than MI and KNNI, imputes more reliable values to incomplete software projects in the missing datasets. It was also found that the proposed imputation method significantly improves software development effort prediction of ABE model.
Proper cost estimation is one of the vital tasks that must be achieved for software project development. Owing to the complexity and uncertainties of the software development process, this task is ambiguous and difficult. Recently, analogybased estimation (ABE) has become one of the popular approaches in this field due to its effectiveness and practicability in comparing completed projects and new projects in estimating the development effort. However, in spite of its many achievements, this method is not capable to guarantee accurate estimation confronting the complex relation between independent features and software effort. In such a case, the performance of the ABE can be improved by efficient feature weighting. This study introduces an enhanced software estimation method by integrating the firefly algorithm (FA) with the ABE method for improving software development effort estimation (SDEE). The proposed model can provide accurate identification of similar projects by optimising the performances of the similarity function in the estimation process in which the most relevant weights are assigned to project features for obtaining the more accurate estimates. A series of experiments were carried out using six real-world datasets. The results based on the statistical analysis showed that the integration of the FA and ABE significantly outperformed the existing analogy-based approaches especially for the ISBSG dataset.
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