#Cocomo model implementation software#
Banimustafa, "Predicting Software Effort Estimation Using Machine Learning Techniques," 8th Int. Cho, H.-G., Kim, K.-G., Kim, J.-Y., & Kim, "A Comparison of Construction Cost Estimation Using Multiple Regression Analysis and Neural Network in Elementary School Project.," J. M., & Handayaningsih, “Analogy-based model for software project effort estimation,” Int. Haszlinna Mustaffa, "Adoption of Machine Learning Techniques in Software Effort Estimation: An Overview," IOP Conf. Ponnurangam, "Analysis of Empirical Software Effort Estimation Models," Int. Kaur, "Optimization of COCOMO Parameters using TLBO Algorithm," Int. Khatibi, "A PSO-based model to increase the accuracy of software development effort estimation," Softw. Singh, "Parameter Tuning for Software Effort Estimation Using Particle Swarm Optimization Algorithm," Int. Sakar, "Software Development Effort Estimation Using Ensemble Machine Learning," Int. Singh, "Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm," Int.
Ziaaddini, "Optimization of software cost estimation using harmony search algorithm," 1st Conf. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models. Comparing the proposed approach has been made with the three traditional algorithms however, the obtained results confirm low accuracy before hybrid with PSO.
The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model however, it lacks accuracy in effort and cost estimation, especially for current projects. Various software effort estimation model has been introduced to resolve this problem. A poor estimation will impact the result, which worsens the project management.
The success of a software project development depends mainly on the accuracy of software effort and cost estimation. It determines the budget, time, and resources needed to develop a software project. Software effort and cost estimation are crucial parts of software project development.