2015/07/31 by Danny Ho, Ho, Danny, Luiz Fernando Capretz +5
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fault Detection and Control Systems #Software Engineering (cs.SE) #Software Engineering Research #Software Reliability and Analysis Research #cs.AI #cs.SE
paper · pdf · doi:10.48550/arxiv.1508.00037
20th International Forum on COCOMO and Software Cost Modeling, Los Angeles, USA, 5 pages, 2005
arxiv created 2015/07/31 · openalex publication_date 2015/07/31 · arxiv updated 2015/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Accurate estimation such as cost estimation, quality estimation and risk analysis is a major issue in management. We propose a patent pending soft computing framework to tackle this challenging problem. Our generic framework is independent of the nature and type of estimation. It consists of neural network, fuzzy logic, and an algorithmic estimation model. We made use of the Constructive Cost Model (COCOMO), Analysis of Variance (ANOVA), and Function Point Analysis as the algorithmic models and validated the accuracy of the Neuro-Fuzzy Algorithmic (NFA) Model in software cost estimation using industrial project data. Our model produces more accurate estimation than using an algorithmic model alone. We also discuss the prototypes of our tools that implement the NFA Model. We conclude with our roadmap and direction to enrich the model in tackling different estimation challenges.