  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.
