Details

Autor(en) / Beteiligte

• Zakaria, Noor Azura
• Ismail, Amelia Ritahani
• Abidin, Nadzurah Zainal
• Khalid, Nur Hidayah Mohd
• Ali, Afrujaan Yakath

Titel

Optimization of COCOMO Model using Particle Swarm Optimization

Ist Teil von

• International journal of advances in intelligent informatics, 2021-07, Vol.7 (2), p.177-187

Ort / Verlag

Yogyakarta: Universitas Ahmad Dahlan

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a wellestablished software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. 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. 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. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. The proposed approach has been compared with the three traditional algorithms; however, the obtained results confirm low accuracy before hybridizing with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.