Enhancing Analogy-Based Software Effort Estimation with Firefly Algorithm Optimization

TL;DR

This work tackles software effort estimation by enhancing Analogy-Based Estimation (ABE) with Firefly Algorithm (FA) optimization, forming FAABE. The method optimizes similarity-weight parameters within ABE to improve predictive accuracy, aided by feature selection. Evaluations on six public datasets (including COCOMO81, Desharnais, Maxwell, among others) show FAABE consistently reduces MMRE, MAE, MSE, and RMSE compared with standard ABE. The study demonstrates a practical, scalable approach to improve estimation reliability and suggests future directions like missing data imputation to push performance further.

Abstract

Analogy-Based Estimation (ABE) is a popular method for non-algorithmic estimation due to its simplicity and effectiveness. The Analogy-Based Estimation (ABE) model was proposed by researchers, however, no optimal approach for reliable estimation was developed. Achieving high accuracy in the ABE might be challenging for new software projects that differ from previous initiatives. This study (conducted in June 2024) proposes a Firefly Algorithm-guided Analogy-Based Estimation (FAABE) model that combines FA with ABE to improve estimation accuracy. The FAABE model was tested on five publicly accessible datasets: Cocomo81, Desharnais, China, Albrecht, Kemerer and Maxwell. To improve prediction efficiency, feature selection was used. The results were measured using a variety of evaluation metrics; various error measures include MMRE, MAE, MSE, and RMSE. Compared to conventional models, the experimental results show notable increases in prediction precision, demonstrating the efficacy of the Firefly-Analogy ensemble.

Figures (3)

• Figure 1: The workflow of firefly algorithm.
• Figure 2: Proposed Methodology for FAABE Algorithm
• Figure 3: Comparison of performance metrics before and after optimization across six datasets.