2016 International Seminar on Intelligent Technology and Its Application 978-1-5090-1709-6/16/$31.00 ©2016 IEEE 219 Modeling Software Effort Estimation Using Hybrid PSO-ANFIS Suharjito Magister of Information Technology Bina Nusantara University Jakarta, Indonesia suharjito@binus.edu Saka Nanda Magister of Information Technology Bina Nusantara University Jakarta, Indonesia sakatecstar@yahoo.co.id Benfano Soewito Magister of Information Technology Bina Nusantara University Jakarta, Indonesia bsoewito@binus.edu Abstract— Accurate estimating software development effort is essential in effective project management processes such as budgeting, project planning and control. To achieve an accurate estimate some algorithmic estimation techniques proposed to eliminate or reduce inaccuracies estimation. COCOMO is a parametric model used to estimate software effort. However, so far no model has proven successful to effectively and consistently predict software effort. Parametric models are considered vulnerable when faced with the problem of non-linearity of the complex in the parameters. In recent years, some estimation technique appears using intelligent systems to predict software effort. This study uses a model Neuro-fuzzy optimized with PSO to get the right model to improve the estimation effort at NASA dataset software project. Parameter cost driver, consisting of 17 feature COCOMO will then be optimized using PSO techniques to get a better prediction accuracy. Furthermore, the results of the optimization will be trained in using the algorithm to get a prediction Neuro-fuzzy effort. The performance of the proposed estimation model will be evaluated with some other intelligent system model parameters to evaluate several criteria such as Mean Standard Error (MSE), Mean Magnitude of Relative Error (MMER), and Level Prediction (Pred). The model that best shows the error rate MSE and MMER lowest to highest Pred. Keywords—Effort Estimation; ANFIS; COCOMO; Particle Swarm Optimization I. INTRODUCTION The development of the software industry is very rapid in recent causes the cost of the software to be one of the topics that interest [1]. Estimates of the cost to be one measure of success in a software project. An accurate estimate for the software effort, cost and scheduling is very important to manage financial issues and monitor the activities of development and on-time delivery. Based on data from the Standish Group's CHAOS Report 2012 [2] EXTREME, 30,000 software projects, 39% of project failures, 43% experienced problems, only18% of successful projects. In addition to financial losses, the company's IT project failure caused a decline in the company's reputation. To achieve an accurate estimate, many contributions proposed and validated estimation techniques to reduce and eliminate inaccuracies estimation. Highlights of this research is to design software evaluation effort using Adaptive Neuro- Fuzzy Inference System (ANFIS) the historical dataset COCOMO 81 and shows the significance of this technique compared to other machine learning techniques. This study will use a blend of techniques PSO with ANFIS tested for applicability to predict COCOMO feature. Two methods will be compared in terms of the accuracy of their predictions. The remainder of the paper is divided in different sections as follows: Section II includes a brief literature review about the concepts and techniques used in current model. Section III presents the proposed model based on ANFIS Optimization with PSO for software cost estimation. Experiments and results are described in section IV and conclusion of the paper is described in section V and in the last; Appendix shows the comparison between those models. II. LITERATURE REVIEW In four or five decades, there are many proposed estimation technique, referred to as the estimation model. To solve this problem, there are various methods of machine learning [3, 4] [5]. One of the methodological variations among them, Artificial Neural Networks [6, 7, 8], Fuzzy Logic [9, 10], Evolutionary Computing such as Genetic Algorithm [11] and Particle Swarm Optimization [12, 13, 14]. This method is suitable to solve real-world ambiguity. Artificial neuro-fuzzy inference systems have been applied in software effort estimation fields. Many studies on software effort prediction using artificial neuro-fuzzy inference system [15, 13, 16, 8] have been realized recently. Most of them [6] use a gradient descendent technique for optimizing the antecedent parameters and a least means square method for the consequent parameters of the ANFIS. More recently [17, 11] an optimization technique based on genetic algorithm was proposed for training the parameters in the antecedent part of a fuzzy system. A. COCOMO Various parametric models have sprung up, such as COCOMO [18] COCOMO II [19], SLIM [20], ESTIMACS [21], all based Functional size Measurement (FSM) [22] in the estimation of effort. The quality of the data for this model bias depend son subjective assessments for each of the functional size. Model on COCOMO and SLIM depends on the number of SLOC (Source Lines of Code) to be estimated before starting the effort estimation process. COCOMO (Constructive Cost Model) is a regression model developed by Prof. Barry W. Boehm [19]. This method introduces a non-linear approach in 1981. COCOMO categorizes projects into three levels, namely Basic, Intermediate, and Detail. When the data set is still widely used in the prediction of effort and budget planning software [15]. COCOMO II model can be described by the following equation: