# Keywords Software Cost; Effort Estimation, Constructive Cost Model-II (COCOMO-II), Hybrid Model, Functional Link Artificial Neural Network (FLANN).

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2 terminates. Hence it is, therefore, needed to estimate accurate software development time, cost and manpower, which replace older methodologies. Recently, researchers have worked on the applicability of using computation and machine learning techniques to build estimation models. Many models were explored to afford improved effort estimation [6] [7]. Adriano L.I. Oliveira [8] developed COCOMO-II with multi-layer neural networks for software development effort estimation; the accuracy of these models depends on the parameters of this method. K Vinay Kumar [9] developed a wavelet neural network with COCOMO-II for improvements in efforts and cost. In [1], researchers provided a planned solution for estimating software effort using neural network. The Fuzzy logic and neural networks are also used in software projects in cost estimation model [11]. The generic model for cost estimation uses Neural network to find mean value and it is used to calculated cost and effort [12]. Also some of the researchers, applied training algorithms and fuzzy set theory for cost and effort estimation [13],[14],[15]. The number of models was explored to provide best effort estimation. In other search [17] the project characteristics are considered with team members characteristics to find the cost and effort of the software s. TABLE I. Comparison Of MNN And FLANN Multilayer Neural Network 1. It contains the hidden layer. It is flat network. 2. It requires a long time to run the network. 3. Expands input in a Non-linear fashion. 4. It is slow network due to its hidden layers. Functional Link Artificial Neural Network 1. It does not contain hidden layer. 2. FLANN requires less time to run as it does not contain hidden layers. 3. Expands input in a linear fashion. 4. It is a fast network. III. ALGORITHMIC MODEL Boehm developed and calibrated the popular algorithmic model used for software cost estimation called COCOMO- II. It is a best-known algorithmic cost model developed by Barry Boehm in 1981 [2]. It is constructed from an analysis of sixty- three software projects. It includes level wise software cost estimation models such as Basic, Intermediate and Detailed Sub models. The COCOMO-II model as shown in fig.1 is developed from COCOMO model and used everywhere in the software industry. It provides the set of tools and techniques for evaluating the improvements in software technology so that it will maintain the planning and budgeting in the software development life cycle. It includes three sub models: Application Composition : This type of model is appropriate for fast developed applications using the interface based on Graphical User Interface and object points estimation. Early Design : This model is used in the starting phase of software development. It uses an Unadjusted Function Point as a measure of size. It is used in integration of the system, generating application. Post-Architecture : This is the most complete model of the three. It uses the function points or Source line of code (SLOC ) as size estimates with this model. It includes overall development and maintenance of software projects. The fig.1 shows COCOMO-II model that describes Seventeencost drivers that are used in the Post-Architecture model [2]. The COCOMO-II cost drivers are have a priority on a scale from Very Low to Extra High. Values of cost driver are given in Table II , IJAFRC and NCRTIT 215 All Rights Reserved

3 Figure 1. COCOMO-II Cost and Effort Estimation Where A is constant, size is the probable size of the software project expressed in thousands of lines of code (KSLOC), Effort multipliers i=1, and Scale factor i=1, The scale factor is the main parameter of the software development projects. This scale factor affects on increasing or decreasing the amount of development effort and they are: Five-scale factors are: 1. Precedentedness 2. Developing Flexibility 3. Risk Resolution 4. Team Cohesion 5. Process maturity. Seventeen-cost factor based on 1. Product attributes 2. Computer attributes 3. Personal attributes 4. Project attributes. TABLE II. COCOMO-II Cost Drivers Values [2] Cost Very Low Nominal High Very high Driver Low PREC FLEX RESL TEAM PMAT RELY DATA CPLX RUSE DOCU , IJAFRC and NCRTIT 215 All Rights Reserved Extra High

4 TIME STOR PVOL ACAP PCAP PCON AEXP PEXP LTEX TOOL SITE SCED IV. ARCHITECTURE OF FLANN Figure 2. Architecture of FLANN The fig.2 shows the architecture of Functional link artificial neural network. In a FLANN network user provides the input values. These values functionally expanded by network and store the values in some matrix form. These intermediate inputs used by any algorithm to find the desired results. Following are the silent features of FLANN. FLANN is higher order neural network, utilizes a higher combination of its inputs. FLANN is a single-layer network compared to multilayer neural network. It is a flat network with no hidden layer that makes it easier. FLANN combines linearly the input signal and expands the input signal space. The hidden layers existing in the MLP are completely replaced by the nonlinear mappings, that simplify the structure of MLP and makes FLANN perform faster convergence rate and less computational complexity. V. HYBRID MODEL FOR SOFTWARE COST ESTIMATION , IJAFRC and NCRTIT 215 All Rights Reserved

5 Figure 3. Hybrid for Software Cost Estimation The fig. 3 shows the Hybrid model consists of input as a size in form of KSLOC, five-scale factors and seventeen-cost factors; they are functionally expanded by one polynomial given as fallows. The functional expansion of the input pattern in the FLANN is given by. A. Chebyshev polynomials T (x) = 1, T 1(x) = x, T 2(x) = 2x 2 1, T 3(x) = 4x 3 3x, T 4(x) = 8x 4 8x Next order Chebyshev polynomials may be generated by using the recursion formula given by: T n(x) = 2xT n-1(x) T n-2(x), n 2, ( 1 x 1). B. Functional Expansion Each element of the input values before expansion is represented as (i) where each element P (i) is functionally expanded. The functionally expansion consist Let N = 5 and I = total number of values in terms of cost factor in the dataset has been taken. The Functional Expansion of each input pattern is done as follows: x(z(i)) = 1, x1(z(i)) = z(i), x2(z(i)) = 2z(i) 2 1, x3(z(i)) = 4z(i) 3 3z(i), x4(z(i)) = 8z(i) 4 8z(i) Where, P(i), 1 < i < D, D is the set of features in the dataset. C. Train data Training data involve Back propagation algorithm, which involves following steps 1. Initialization of weight values 2.Feed forward 3. Back Propagation 4. Updating of the weights D. Back-Propagation algorithm The algorithmic description of training the above network and for calculating new set of weights is depicted in the following steps: 1. Initialize the weights as x i=1 for i=1 to 17(for a cost drivers), yj=1 for j=1 to 5 (for a scale factor) and k=1. The Learning rate is in between to 1 we denote by using N. 2. Test network until stopping a condition for false, repeat the steps 3 to For each training pair, repeat the steps 4 to Compute the output from the input units as Output (i) = Input (i) where Input (i) = si, i = 1 to Compute the output from the output unit from COCOMO-II formulas. Store it in output matrix Opm. 6. Compute weight updates as dpi = N * de/dwi where de/dwi = Error pm * (EMi) for i=1 to 17. dk = N * de/dwi where de/dwi = Error pm * 1.1 * (size) dqi = N * de/dwi where de/dwi = Error pm *.1 * (size) *SFi for i=1 to , IJAFRC and NCRTIT 215 All Rights Reserved

6 7. Compute bias updates as dbias= N * de/dwi where de/dwi = Error pm. 8. Update all weights and bias as pi= pi + dpi for i = 1 to 17, qi = qi + dqi for i=1 to 5, k= k + dkbias = bias + dbias. 9. If the error is smaller than a specific tolerance or the number of iteration exceeds a specific value, stop: else continue. 1. Using this approach, the learning rate can be decided by the expert by their opinion. The network should train with correct inputs. VI. EXPERIMETAL SETUP A. Dataset The Datasets are publicly available for the research purpose, the NASA-93 [18] dataset is used. The NASA-93 dataset consists of 93 NASA projects. It consists of twenty four attributes: Seventeen standard cost drivers and five-scale factors of COCOMO-II with priority Very Low to Extra High, Thousand source lines of code measure (KLOC), the actual effort in person months. B. Implementation Details The COCOMO-II model is as shown in the fig. 4. The user will enter in system by giving login and password. The fig. 4 shows the COCOMO-II Module. The input cost factors and scale factors are taken from NASA-93 [18] dataset which available publicly and calculate its estimation in terms of cost, effort and estimation. Figure 4. COCOMO-II The same input is taken from NASA-93 [18] Dataset and tested with a hybrid model. The values of cost drivers are passed to FLANN and trained with back propagation algorithm. Figure 5. Hybrid , IJAFRC and NCRTIT 215 All Rights Reserved

7 The result of COCOMO-II and a Hybrid model is compared with the efforts, schedule and cost of the project. The fig. 6 shows the results. Figure 6. Result Comparison of COCOMO-II and Hybrid From NASA-93 [18] dataset we used project id no 4 and 2 Its values and KLOC are given as: For Project id 4: Scale Factors: high, low, high, nominal, nominal. Effort Multipliers: low, low, nominal, nominal, nominal,nominal, high, high, nominal, nominal, low, nominal, nominal, nominal, nominal, KSLOC: 8.2, Efforts: 36, Consider cost: 2\$ person per month For Project id 72: Scale Factors: high, low, low. High, low Effort Multipliers: low, high, nominal, nominal, nominal,nominal, high, low, nominal, nominal, high, nominal, nominal, nominal, nominal, KSLOC: 17.2, Effort: 45.35, Consider cost: 25\$ person per month. By using this data the cost, effort and schedule are compared. Table III. Shows the efforts, cost and schedule of the project id 4 and 2. TABLE III. Data Obtained Using COCOMO-II And Hybrid Project id 4 2 COCOMO- II Hybrid COCOMO- II Hybrid Efforts Cost Schedule C. Effort Comparison The effort comparison is shown in the fig. 7. It shows that the efforts in hour s by person per month. For the project, id 4 and 2 pass all input to COCOMO-II model from NASA-93 [18] dataset and got 36 hrs and , IJAFRC and NCRTIT 215 All Rights Reserved

8 45.35 hrs person per month respectively. Using the hybrid model with same project id 4 and 2, we have effort of 35.3 hrs and 42.5 people per month. H R S Effort Comparison 4 2 Project ID COCOMO-II Hybrid Figure 7. Effort Comparison of COCOMO-II vs Hybrid D. Cost Comparison The cost comparison is shown in fig.8. It shows that the cost required to develop the software. For project id 4 Let us consider the cost of 2\$ per person month. The cost obtained using COCOMO-II and the hybrid model are 74\$ and 711\$ and for project id 2 lets consider cost 25\$ per person month. The cost obtained using COCOMO-II and the hybrid model is 855\$ and 835\$ respectively. C O S T Cost Comparison 4 2 Project ID COCOMO- II Hybrid E. Schedule Comparison Figure 8. Cost Comparison of COCOMO-II vs Hybrid The schedule comparison is shown in fig. 9. It shows that the schedule required to develop the software in month. Hence, using the hybrid model schedule is reduced as compared to COCOMO-II model. M O N T H Schedule Comparison 4 2 Project ID COCOMO-II Hybrid Figure 9. Schedule Comparison Of COCOMO-II Vs Hybrid , IJAFRC and NCRTIT 215 All Rights Reserved

9 F. Evaluation Details International Journal of Advance Foundation and Research in Computer (IJAFRC) The hybrid model proposed in the previous section is implemented in a Core Java and Java server pages using the eclipse tool in windows 8. operating system. Training and testing was done using the COCOMO-II dataset and the effort obtained was compared with that of the actual effort. The evaluation criterion is used to compare the performance of the COCOMO-II model. Criteria to satisfy the most cost estimation models are the Magnitude of Relative Error (MRE). MRE is defined as = 1 (6) The Table IV shows a comparison of evaluation part of the system. The effort factor of Dataset NASA-93 [18] and the hybrid model compared. The different project Ids used and find its effort using COCOMO-II and the hybrid model. For example, project id 4 from NASA-93 [18] dataset taken input in COCOMO-II and the hybrid model, to calculate the effort MRE (%). TABLE IV. Mre Comparison On Dataset Nasa-93[2] For COCOMO-II Vs Hybrid Sr. No. Project ID Effort MRE(%) using COCOMO-II Effort MRE(%) using Hybrid M R E ( % ) MRE Comparison MRE with COCOMO-II MRE with Hybrid Project IDs Nasa-93 Dataset Figure 1. MRE Comparison of COCOMO-II vs Hybrid Thus, the bar chart in fig.1 showing the MRE comparison of COCOMO-II model efforts and Hybrid model effort. MRE is calculated using above formula. Thus, from this comparison the hybrid model with FLANN gives an efficient method to find the efforts of cost and effort estimation method. The X-axis represents , IJAFRC and NCRTIT 215 All Rights Reserved

10 the number of project ideas from Dataset NASA-93 tested on COCOMO-II model and the hybrid model and Y-axis shown the MRE (%) of the efforts of these models. VII. CONCLUSION & FUTURE WORK It has been challenging to develop a trustworthy estimate of software development effort for both the software organization. As so many software efforts prediction models are available. One can develop hybrid cost estimation models based on the artificial neural network. The main idea focuses on to use of COCOMO-II model with minimum number layers to increase the performance of the network. The neural network used to predict the software development effort is the FLANN. The learning algorithm used back propagation to train the network. For training and testing purpose NASA-93, the dataset is used and it is found that the single layer neural network model with back-propagation provides better cost estimations than the estimation done using COCOMO-II model. Finally, one can say that this work provides a good prediction for the software development effort. Further research can be done by training the network with genetic algorithm, classification and swarm optimization techniques such as bee colony algorithm and particle swarm optimization. The researchers can use these techniques with neural network and fuzzy logic to increase the accuracy and efficiency of estimating the cost and effort of software application. VIII. REFERENCES [1] Author1_Name, Author2_Name, Paper Title ACASH: An Adaptive Web Caching method based on. [2] L. H. Putnam, "A General Empirical Solution to the Macro Software Sizing and Estimating Problem," IEEE Transactions on Software Engineering, vol. 4, pp , [3] B. W. Boehm, Software Engineering Economics. Prentice-Hall,1981. [4] A. Albrecht, "Measuring application development productivity," in IBM Application Development Symp. 1979, pp [5] G. Karner, "Resource Estimation for Objectory Projects, Objective Systems, [6] R. T. Hughes, "Expert judgement as an estimating method," Information and Software Technology, vol. 38, pp , [7] N. Dalkey and O. Helmer, "An Experimental Application of the Delphi Method to the Use of Experts," Management Science, vol. 9, pp. pp , [8] S. Chulani, B. Boehm, and B. Steece, Calibrating software cost models using bayesian analysis, IEEE Trans. Software Engr., July- August 1999, pp , [9] B. Clark, S. Devnani-Chulani, and B. Boehm, Calibrating the cocomo ii post-architecture model, in ICSE 98: Proceedings of the 2 th international conference on Software engineering, (Washington, DC, USA), pp , IEEE Computer Society, [1] Adriano L.I. Oliveira, Petronio L. Braga, Ricardo M.F. Lima, Márcio L. Cornélio GA-based method for feature selection and parameter optimization for machine learning regression applied to software effort estimation Journal of Information and Software Technology 52 (21) , IJAFRC and NCRTIT 215 All Rights Reserved

11 [11] K. Vinay Kumar, V. Ravi, Mahil Carr, N. Raj Kiran- Software development cost estimation using wavelet neural networks The Journal of Systems and Software 81 (28) [12] S. Kumar, B. A. Krishna, and P. Satsangi, Fuzzy systems and neural networks in software engineering project management, Journal of Applied Intelligence, vol. 4, pp , [13] Patil L.V., Waghmode R.M., Joshi S.D, Khanna V, Generic model of software cost estimation: A hybrid approach Souvenir of the 214 IEEE International Advance Computing Conference, IACC 214, (IEEE),Article number , Pages [14] A. C. Hodgkinson and P. W. Garratt, A neuro-fuzzy cost estimator, in Proceedings of the Third Conference on Software Engineering and Applications, pp , [15] J. Ryder, Fuzzy COCOMO: Software Cost Estimation. PhD thesis, Binghamton University, [16] P. Mus ılek, W. Pedrycz, G. Succi, and M. Reformat, Software cost estimation with fuzzy models, SIGAPP Appl. Comput. Rev., vol. 8, no. 2, pp , 2. [17] Y. H. Pao, Adaptive Pattern Recognition and Neural Networks, Reading, MA: Addison-Wesley, [18] M. Kazemifard, A. Zaeri, N. Ghasem-Aghaee, M.A. Nematbaksh, F.Mardukhi, Fuzzy Emotional COCOMO-II Software cost estimation using multiagent system, Applied soft computing, Vol. 211, pp , 211. [19] J. Sayyad Shirabad and T. Menzies, The PROMISE Repository of Software Engineering Databases... School of Information Technology and Engineering, University of Ottawa, Canada, 25. Available from , IJAFRC and NCRTIT 215 All Rights Reserved

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