An optimized case-based software project effort estimation using genetic algorithm

Software development companies have long suffered from inaccurate estimation of their software projects. This in turn led to huge losses, especially in the financial resources available for the project as well as the time required to complete it. As a result of this, the research community has developed different methods for esti-mating effort in software projects in the hope of achieving high levels of accuracy and efficiency in the use of available resources. Among those methods that have proven to be accurate in estimating the effort of software projects is the use of machine learning (ML) techniques, especially the case-based reasoning technique (CBR). This technique is based on adapting previously successful solutions for similar software projects. However, the CBR technique suffers from a problem which is its multiple parameters that are difficult to be tuned. This justifies the importance of the adaptation and adjustment process as an essential part of CBR to produce accurate and efficient results with least absolute estimation error. In this paper, one of the most efficient multi-objective evolutionary techniques, the Genetic Algorithm (GA), are used to help find the best set of classical CBR pa-rameters (feature selection, feature weighting, similarity measures, and k number of nearest neighbors) to produce the most accurate effort estimates for software projects. The proposed CBR-GA model showed the effectiveness of using the GA algorithm to search for the best combination of CBR parameters and thus improve its accuracy. This in turn is beneficial for project managers in the early financial planning phase for effort estimation and thus project cost control. To validate the proposed CBR-GA model, we used a set of public benchmark datasets available on PROMISE data repository, in addition we used a set of reliable evaluation metrics. The obtained results are promising in terms of accuracy and significance tests. This implies the importance of search-based techniques for tuning effort estimation methods.