Optimizing Software Effort Estimation Models Using Firefly Algorithm

TL;DR

This paper introduces the Firefly Algorithm as an effective metaheuristic for optimizing parameters in COCOMO-based software effort estimation models, achieving higher accuracy than other algorithms.

Contribution

It is the first to apply the Firefly Algorithm for tuning parameters in COCOMO models, improving estimation accuracy over existing metaheuristics.

Findings

Firefly Algorithm outperforms Genetic Algorithms and Particle Swarm Optimization.

Optimized models show significant error reduction.

High accuracy achieved in effort estimation.

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.