Abstract
Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.
Highlights
Effort estimation of software development has been a crucial task for software engineering community
The experiments apply Firefly Algorithm (FA), Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for optimizing the coefficients of the basic COCOMO model, COCOMO Model I and COCOMO Model II based on the training part of NASA data set
Mean Absolute Error (MAE) criteria are used as an objective function which is shown in Equation (10)
Summary
Effort estimation of software development has been a crucial task for software engineering community. Several effort estimation models have been developed and improved over time for better prediction accuracy and better development quality [1] [4]-[8]. Such models range from complex calculations and statistical analysis of project parameters, to advanced machine learning approaches. Heuristic optimizers have been used in software effort estimation [10] as the use of genetic programming in [11] for model optimization. Another example is the part that Particle Swarm Optimization took in [12] as a heuristic optimizer.
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