ANN-based software cost estimation with input from COCOMO: CANN model

Abstract

Different project management processes have been used in software engineering to support managers in keeping project costs manageable. One of the essential processes in software engineering is to accurately and reliably estimate the required effort and cost to complete the projects. The domain of software cost estimation has witnessed a prominent surge in research activities in recent years and being an evolving process, it keeps opening new avenues, each with advantages and disadvantages, making it important to work out better options. This research aims to identify the factors that influence the software effort estimation using the constructive cost model (COCOMO), and artificial neural networks (ANN) model by introducing a novel cost estimation approach, COCOMO-ANN (CANN), utilizing a partially connected neural network (PCNN) with inputs derived from calibrated values of the COCOMO model. A publicly available dataset (COCOMONASA 2), various combinations of activation functions, and layer densities have been systematically explored, employing multiple evaluation metrics such as MAE, MRE, and MMRE. In the PCNN model, the ReLU activation function and a 1000-dense layer have demonstrated better performance. While layer density generally correlates with better outcomes, this correlation is not universally applicable for all activation functions and outcomes vary across different combinations. The use of the relationships between 26 key parameters of COCOMO in PCNN produced better results than FCNN by 0.59%, achieving an MRE of 6.55 and an MMRE of 7.04. The results indicated that the CANN model (COCOMO & ANN) presented better results than existing models.

Keywords

• Software cost estimation
• Predictive analytics
• AI in project management
• Machine learning for cost prediction
• Project cost forecasting
• Predictive models for software costs