International Journal of Advances in Intelligent Informatics ISSN 2442-6571 Vol. 7, No. 2, July 2021, pp. 177-187 177 https://doi.org/10.26555/ijain.v7i2.583 http://ijain.org ijain@uad.ac.id Optimization of COCOMO Model using Particle Swarm Optimization Noor Azura Zakaria a,1 , Amelia Ritahani Ismail a,2,* , Nadzurah Zainal Abidin a,3 , Nur Hidayah Mohd Khalid a,4 , Afrujaan Yakath Ali a,5 a Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur, Malaysia 1 azurazakaria@iium.edu.my; 2 amelia@iium.edu.my; 3 nadzurah.zabidin@gmail.com, 4 hidayahkhalid7@gmail.com, 5 afrujaan@gmail.com * corresponding author 1. Introduction Estimating software effort is an essential and crucial activity for the software development life cycle as it requires estimating the effort and cost at the initial stage of the project. An accurate effort estimation leads to the effective and efficient development of software and decreased risks [1][2]. Estimations aim to accurately and control the cost and time boundaries of the project planning [3][4]. The key parameters of effort estimation are time and cost, which are based on two reasons: to present software that is adaptable in a limited time frame and fill the gap between software and hardware progressions; and to generate software under the budget and time as responding to changeable customer demands [5][6]. An overflow of time and cost usually occurs in software project development activities, which often forces to cut the development costs at the cost of software quality. This overflow can impose budget deficit, lack of human force, delayed planning, low-quality software, and eventually project failure [1][7]. Project estimation is an essential part of completing a project. Projects are planned in terms of cost, effort, and budget at the beginning phase of development [8]. Precise effort estimation of software ARTICLE INFO ABSTRACT Article history Received October 30, 2020 Revised December 27, 2020 Accepted April 24, 2021 Available online April 24, 2021 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 well- established 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. This is an open access article under the CC–BY-SA license. Keywords Particle Swarm Optimization COCOMO model Software effort Estimation model NASA