Optimizing COCOMO II parameters using particle swarm method

Langsari, Kholed; Sarno, Riyanarto

doi:10.1109/icsitech.2017.8257081

Cited by 10 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Individuals migrate one after the other. The P RT parameter influences the direction of migration, and according to (8), a new P RT vector is constructed for each t jump of the individual, and new positions are then given by (9). After the jump positions of the individual are known, the domains are checked.…”

Section: ) Soma Optimization Algorithmmentioning

confidence: 99%

“…The performance of the eSOMCOCOMO against traditional models and other metaheuristic algorithms was assessed by MMRE, PRED(0.25), MAE, and RMSE. The proposed experiments (Table 10) were compared with the original CO-COMO or COCOMO II model and metaheuristic algorithms (Particle Swarm Optimization (PSO) [9] and Genetic Algorithm (GA) [38]). Moreover, the experiment (EXP C1) was compared with Local Calibration [39], and the experiments (EXP A1 -EXP A5) were compared with the baseline models (the Walston-Felix model [40], the Bailey-Basili model [27], and the Halstead model [41]).…”

Section: Benchmark Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Software Effort Estimation With Self-Organizing Migration Algorithm: A Comparative Analysis of COCOMO Models

Bajusova,

Silhavy,

Silhavy

2024

IEEE Access

View full text Add to dashboard Cite

This study presents a comprehensive analysis of enhancing software effort estimation accuracy using a Self-Organizing Migration Algorithm (SOMA)-optimized Constructive Cost Model (COCOMO). By conducting a comparative analysis of traditional COCOMO models and SOMA-optimized variants across preprocessed datasets (NASA93, NASA63, NASA18, Kemerer, Miyazaki94, and Turkish), our research focuses on crucial evaluation metrics including Mean Magnitude of Relative Error (MMRE), Prediction at 0.25 (PRED(0.25)), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The analysis encompasses various configurations of COCOMO models-basic, intermediate, and postarchitecture COCOMO II, supplemented with additional statistical testing and residual analysis for in-depth insights. The results demonstrate that the SOMA-optimized COCOMO models generally surpass traditional models in predictive accuracy, especially notable in metrics such as MMRE where an improvement of up to 12%, PRED(0.25) with an enhancement of 15%, MAE reduction by 18%, and a decrease in RMSE by 20% were observed. However, performance variances were identified in specific scenarios, highlighting areas for further refinement, particularly in large-scale estimations where residual plots suggested the potential for underestimation or overestimation. The study concludes that integrating the SOMA optimization algorithm into COCOMO models significantly enhances the accuracy of software effort estimations, providing valuable insights for future research to optimise estimations for larger projects and advance prediction models. This advancement addresses the technical challenge of parameter accuracy and offers a methodological improvement in model selection and application, underscoring the potential of metaheuristic optimization in software effort estimation.INDEX TERMS software effort estimation, COCOMO models, SOMA, metaheuristic optimization.

show abstract

Section: ) Soma Optimization Algorithmmentioning

confidence: 99%