International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Enhancing the Software Effort Prediction Accuracy Using Reduced Number of Cost Estimation Factors with Modified COCOMO II Model D. Sivakumar1, K. Janaki2 1Associate
Professor, Dept. of CSE, ACS College of Engineering, Bengaluru, Karnataka, India Associate Professor, Dept. of CSE, RajaRajeswari College of Engineering, Bengaluru, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------2
Abstract - The empirical estimation method mainly relies
on cost drivers in estimating effort and cost of software projects. The cost drivers and the selection of ranges for a particular cost driver will not be same for all models and situations. The variety of cost drivers and its properties in the standard COCOMO II model in view of recent scenario is attained more focus on research interest. The main objective of this work is to analyze the COCOMO II model cost drivers and the impact of some specified cost drivers in estimating effort and cost of software projects. In this paper the ranges of cost drivers and its values are adjusted according to the recent industrial situations and needs. The number of cost drivers is reduced to 13 and the efforts are estimated using this newly modified cost drivers. This model proved its improved efficiency in estimation with reduction in percentage of MRE and MMR values. Key Words: Effort Estimation, Software Project, Cost Drivers, Modified COCOMO II, MRE, MMRE.
1. INTRODUCTION Estimations are indispensable in software projects to support the decision building in different phases Boehm [1]. The very first decision on a project is evaluating, in which it is acknowledged that, whether the project is usually and economically feasible or not Boehm et al., BW & Valerdi, R [2][3]. The effort required to make the software is a vital factor in building decision, as software projects seldom comprise major cost items other than salaries and interrelated side expenses. Even before starting a project, there could be deliberate forecast activities to find out the potential relevance domains and projects Charette & Chen et al., [4][5]. Estimating the software project development effort in early development is a tedious job for the software project managers in the current industrial situations De Jong [6]. In this paper it is aimed to analyze and identify the changes required in the already developed COCOMO II post architecture model and the cost drivers are classified and reframed according to the recent industrial scenarios Denver et al., & Elyassami et al., [7][8]. The COCOMO II post architecture model has 17 effort multipliers whereas the early design model uses only six Š 2017, IRJET
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Impact Factor value: 5.181
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effort multipliers which are also called as cost drivers Fischman [9]. The COCOMO II early design model is a simplification of the post architectural model Galorath & Evans [10]. All type of COCOMO model analysis is made based on the impact of each software cost attribute in estimation and in specific development situations Cuadrado et al.,[11]. These types of estimation and analysis will helps us to suggest some useful guidelines to the software project managers for better software cost and effort estimation and to maintain the cost of a software project in specified limit for better decision making at different levels.
2. LITERATURE SURVEY Samson [12] connected neural system model, Cerebellar Model Arithmetic Computer (CMAC) to the expectation of exertion from programming code size. CMAC is an observation and capacity estimate created by Goldberg & Hale [13] [14]. This neural system was prepared for Boehm's COCOMO information set with a specific end goal to foresee exertion from size, in the same way relapse procedures were connected for expectation purposes. Point by point audit of distinctive studies on the product development effort was given by Jorgensen [15] with the principle objective of contributing and supporting the master estimation research. Neural systems have the learning capacity and are great at displaying complex nonlinear relationships gives more adaptability to incorporate master information into the model. The Standish exploration, gathering said in the CHAOS report uncovers the significant crisis joined with the fate of the product ventures. Jorgensen et al [16]. This likewise demonstrates that the expense overwhelm connected with it is 189%. A dominant part of investigation on utilizing the neural systems for programming expense estimation, are centered around demonstrating the COCOMO strategy, for instance, in Attarzadeh et al [17] a neural system has been proposed for estimation of programming expense. Recardo de Aroujo et al [18] exhibited a cross breed savvy model to plan the morphological rank linear perceptrons to take care of the product cost estimation issue by utilizing the modified genetic algorithm with a gradient descent strategy to upgrade the model. Shepperd & Schofield [19] depicted an option way to deal with the exertion estimation in view of the ISO 9001:2008 Certified Journal
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