Address for Correspondence

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1 Research Paper AN AUTOMATED SIZE BASED ESTIMATION TOOL FOR SOFTWARE EFFORT ESTIMATION WITH RUN TIME QUALITY ATTRIBUTES 1 Dr.M. Senthil Kumar, 2 Dr.B. Chidambara Rajan Address for Correspondence 1 Associate Professor, Department of CSE, Valliammai Engineering College, Chennai. 2 Professor & Principal, Valliammai Engineering College, Chennai ABSTRACT: Today, Computer rules the world. Each and every sector needs software products to do the work effectively and quickly. So the software companies are now interested in providing automated software tool with respect to accuracy and security of their products. Therefore, there is a need to develop an automated tool which provides all expected properties. This paper attempts to propose an automated tool which includes quality metrics and enhanced privacy factors with the existing method. Initially, this method utilized a trapezoidal fuzzy membership set to control the ambiguity in the requirement inputs. Secondly, the function point method is enhanced by the privacy and reliability factors in the calculation. Finally, based on the questionnaire survey, the quality metrics are identified and added with the effort estimation for accuracy. The experimentation is done with past project data sets on the automated tool and the results are compared with the existing methods. The performance evaluations are also done by using global metrics like MRE, MdMRE, PRED. It shows that the proposed tool is high in accuracy and privacy of the product compared to existing tools. KEYWORDS: software effort estimation; quality metrics; automated tool; trapezoidal fuzzy set. 1. INTRODUCTION Software Effort estimation is a process of predicting the effort needed to develop a software project, while the software effort estimation may be simple in concept, but it is difficult and complex in reality (Astha Dhiman and Chander Diwaker, 2013).The most effort estimation methods focus on humaneffort and give estimates in terms of personmonth(frank Vijay and Manokaran.C,2009). It determines the amount of effort necessary to complete a software project in terms of its scheduling, acquiring of resources and meeting of budget requirements. The effective and efficient development of the project requires accurate estimates. The Software product privacy is another important challenge in today s competitive world (Harsh Kumar Verma and Vishal Sharma, 2010).The software developers are not having clear idea about the quality metrics which influences the accuracy of the product. Based on the literature survey results, the reasons for software product failures are 1. Uncertainty in input size. 2. Non-standardization of data classification. 3. Clumsy Calculated output. 4. Inaccurate estimation. 5. Vulnerable. 6. Unreliable software process. 7. Absence of accurate automated estimation tool. 8. Lack of risk assessment techniques at an early stage. 9. Improper scheduling & planning. Therefore, there is a need to develop a hybrid tool which provides all the necessary features. The main objective of this research work is to develop and describe an effective effort estimation method and hybrid tool, which can be used to improve the estimation accuracy and to address the other research problems. The goal of this work is to create and evaluate a size based estimation tool to assess estimation performance by using available set of previously completed software projects. The tool should be user friendly, and not rely on any other estimation tools. By creating an easily accessible and usable tool, it will be easier to conduct research in the field of software development. The tool should be easily accessible through any platform, provide good usability, allow integration with other software packages and should be developed in a way that makes future improvements and extensions, easy. This research work is also targeted to evaluate the performance of various existing software effort estimation models which are compared with the proposed tool. 2. RELATED WORKS There are many models available for effort estimation, only a few methods are reviewed here. The work (Imman Attarzadeh and Siek Hock ow, 2010) suggested ambiguous and linguistic inputs of software cost estimation. The work (Harish Kumar Verma & Vishal Sharma, 2010) noted that homogeneous data set results in better and more accurate effort estimates, while the irrelevant and disordered data set results in lesser accuracy. The paper (Ahmeda et al, 2009) proposed a fuzzy logic based framework for managing the imprecision and uncertainty problems. The work (Wei Lin Du et al, 2010) proposed a methodology combining the neurofuzzy technique and SEER-SEM that can function with various algorithmic models. The work (Moussa and Galal-Edeen, 2009) proposed an Enhancing Software Sizing Adjustment Factors. Their results showed that the enhancement achieved good accuracy. The work (Martin and Stefan, 2004) proposed an improved analogy-based approach based on extensive dimension weighting. Their results empirically evaluated the accuracy and reliability improvements of the project efforts.the work (Al- Hajri, et al, 2005) suggested that the Modification of standard function point complexity weights system can reduce the ambiguity in the effort estimation. The work (M.Senthil Kumar and B.Chidambara Rajan, 2014) demonstrated that the effort estimation done by applying the soft computing technique is powerful in solving the real world application with imprecise and uncertain information. The work (M.Senthil Kumar and B.Chidambara Rajan, 2015) suggested that the effort estimation done by applying the Fuzzy technique is effective in handling the clumsy outputs and Non-standardize inputs. The Similarities between these studies are that, they all focus on the elicitation phase of the estimation but do not focus on the construction phase of the product.

2 Many methods use fuzzy logic to handle uncertainty in the input data. Some methods concentrate only on accuracy but do not focus on quality metrics.few methods suggested security factor for product privacy. In this paper, the authors decided to concentrate on all these issues and propose a hybrid tool which will give solution to all these above mentioned issues. 3. AUTOMATED TOOL This Proposed work develops an optimized Fuzzybased Function Point Analysis automated tool to handle the inaccuracy and vulnerability present in the software project to estimate the effort more accurately and securely. The Proposed procedures include four major steps: Fuzzy based Function point Analysis, Questionnaire for quality metrics, Enhancement of Privacy factors and Effort Calculation by the proposed tool. The Overall framework of the proposed method is given in Figure 1 and the proposed algorithm is given in Table 1 which clearly shows all the steps in this method. Figure 1 Automated Tool Architecture Table 1.Proposed Algorithm BEGIN Input: Specification of the project S 0= {S 1,S 2, S n} Output: Effort E. Init: Specification While S 0 is not null do Classify the input using Trapezoidal fuzzy membership function; WHILE (Input is not Classified) Do Classify the Input; ENDWHILE; IF input is not Standardization; THEN Crossover the Fuzzy Rules; CASE: Rule OF SIMPLE: LOW Complexity & SMALL Weight; VERY SIMPLE: VERY LOW Complexity & TOO SMALL Weight; AVERAGE: NOMINAL Complexity & MEDIUM Weight; MIN AVERAGE: VERY NOMINAL Complexity & TOO MEDIUM Weight; HIGH: HIGH Complexity & HEAVY Weight; VERY HIGH: VERY HIGH Complexity & TOO HEAVY Weight; ENDCASE; ENDIF; IF Output is not crispy; THEN Apply Defuzzification; ELSE Do Nothing; ENDIF; WHILE VAF is not EXTENDED DO Extend Privacy & Reliability Factor; ENDWHILE; CALCULATE Fuzzy Function Point; SELECT the Non-Functional Characteristics; CALCULATE the Precision Value; COMPUTE Extended FP COUNT as FFPA with Precision Value; ESTIMATE the Enhanced Effort Estimation; FOR all the Past Project Data Set Values; APPLY this Method; UNTIL Results Obtained; END FOR; IF Results is Approximately Equal to Real Values; THEN IMPLEMENT the Model; END: 3.1Fuzzy Based Function Point Analysis In the function point analysis, five factors are used as input named as External Inputs (EI), External Outputs (EO), External inquiries (EQ), External Interfaces File (EIF) and Internal Logical Files (ILF). Rating of these factors can be given by adjective terms such as simple, average and complex. All these factors are fuzzified by using Trapezoidal membership function to handle the imprecision in the data set and need proper handling of the dependencies among these factors to improve the accuracy (Senthil Kumar and Chidambara Rajan, 2015).Unfortunately, this is not an easy task in most cases, so the authors propose new fuzzy if-then rules to handle this situation which is given below in Table 2. Table 2.Proposed IF-THEN RULES Complexity/Weight Small Medium Big Low Simple Average Complex Average Simple Average Complex High Simple Average Complex The outputs of each fuzzy rule are needed to standardize for the required output. This is done by de-fuzzification, which converts the fuzzy output into a crisp solution by using the following equation.

3 W i *V i Output = (1) W i Where W i =Weighted Average, V i =Peak Value 3.2 Questionnaire for quality metrics The performance of any software can be evaluated in measurable, technical terms, using one or more of the quality metrics (Azath & Wahindabanu 2012). There are many Quality characteristics which are available in ISO 9126 system. It is not easy to judge which of these factors are needed for increasing the accuracy. By performing Questionnaire methods in different organizations, the required quality factors for the software product from the ISO 9126 characteristics are obtained. Based on the survey which has been collected by means of questionnaire issued to managers, project executives and developers working in various software companies, eight quality metrics are identified from the various quality parameters to improve the Performance of the effort calculation. They ranked the eight quality factors with the fact how they influence in the quality of the software products. Correctness and Reliability are predominantly ranked first and second, respectively. Each of these categories has a number of related factors, which have been allocated different values. The sums of the factor scores provide an indication of the degree of influence of the factors in the total project. The result of average calculation of the values obtained from the questionnaire is given below in Table 3. Table 3 Rating of Quality Factors Quality factor Weighted-average rank (out of 8) Median rank (out of 8) Correctness Reliability Usability Maintainability Testability Efficiency Flexibility Reusability The formula to determine the Precision Value is given below, Precision Value (PV) = 0.01 *( 8 i=1 F i * C i ) (2) where, F i = factor of each performance metric & C i = Complexity factor The questionnaire aims to check the requirements of the engineers within the organization pointing out the different viewpoints of the projects accuracy. 3.3Enhancement of Privacy factors The engineering security has substantially raised the software project cost and there has been wide variation in the amount of added cost estimated by different models (Faheem et al 2004). Software Security is a vital factor which directly affects the quality factors such as software functionality and capability. Software failures are due to unreliable specification, and insecure program coding. Software reliability is another important quality attribute that must be assured throughout a software development life cycle. Based on the survey, 33% of interruptions happened due to unreliability of software products. Therefore, it is needed to provide reliable and secure estimation. So the 14 traditional value adjustment factors are extended by 15 th and 16 th factors named product security and product reliability to it. So, the formula for calculation of value adjustment factor (VAF) is modified. VAF = (TDI * 0.01) (3) 16 i 1 Mode (out of 8) TDI DI (4) All the general system characteristic factors including the 15 th & 16 th factors are rated by the six point scale (0-5) according to the relevant degree of influence (DI) on the application. 3.4 Effort Calculation The Extended function point count is calculated by multiplying the extended value adjustment factor with the fuzzified unadjusted function point. EFPA=UFP*VAF (5) The Enhanced function point count is done by adding the precision value with the extended function point count. Effort=EFPA+PV (6) 4. EXPERIMENTAL STEP-UP The proposed estimator tool has been developed in Java script under windows environment and the validation is done by applying different real project data sets. The effort estimation data of ten software projects implemented in different domains are used for testing. At the same time, actual effort, traditional function point method, Use case point method, Tree Boost method, Regression method and COCOMO model are also used to compare, with the proposed effort.table 4 is the result of the comparison. The Comparison chart is clearly shown in Figure 2. Table 4 Effort comparisons Project ID Traditional FP UCP TREE Proposed Real REGRESSION COCOMO BOOST Method Effort A1:12: B1:13: A2:13: I2:13: C2:13: H3:13: G3:13: F4:13: D2:13: E2:13:

4 Figure 4 MdMRE Comparsion of Different Models Figure 2 Effort Charts of Different Methods Figure 2 shows that the proposed method values are very near to the real values. It explicitly means that the accuracy of the proposed tool is high, but practically it must be evaluated by using the globally available performance evaluation Parameters such as MRE, MdMRE and PRED are applied to assess, as well as to compare the accuracy of the estimated tools. Mean Relative Error = (Actual Effort-Expected Effort) Actual Effort (7) The MMRE or the Mean Magnitude of Relative Error is the percentage of average of the MREs over an entire data set. It is used for calculating the accuracy of an estimation technique using T number of tests. MMRE = 100/T * I MRE (8) The Prediction Accuracy (PRED) can be calculated as: PRED = k / N (9) Where k is the number of projects and N is the number of all estimates. The Research work is conducted in order to evaluate prediction accuracy of the effort estimation models. These models are evaluated according to their fidelity not only on single point based estimation, but with the help of Past Real Project data sets. Based on the average result analysis of the ten project data sets, the proposed tool shows better estimation accuracy than the other models in all aspects of evaluation criteria which is clearly shown in Table 5. The proposed tool shows the lower MRE values less than 50% and higher prediction value higher than 96% which is mandatory for accurate estimation. Table 5 Performance Evaluation Model MMRE MdMRE PRED(0.25) Proposed % Tree-Boost % Regression % UCP % FP % COCOMO % Figures 3, 4&5 clearly show the improved accuracy of the proposed method in terms of MMRE, MdMRE and PRED. This made a reasonable belief, that using proposed automated tool has more accuracy and security than the other existing models. This also confirms the significant improvement brought in the early effort estimation. Figure 3 MMRE Comparsion of Different Models Figure 5 PRED(0.25) Comparsion of Different Models 5. CONCLUSION A Software supplier organization strives to estimate the effort needed in building software as accurately as possible to ensure the project s budget and schedules, and the success of resource allocation. Despite the numerous effort estimation approaches and applications available, the estimates have remained inaccurate. The aim of this work was to examine the impact of the Quality metrics on the accuracy of Effort estimation in the software development life cycle. Some empirical studies in the literature have shown that in many situations, a bad choice of the metrics can easily lead to poor approximation ability. But, the proposed model is able to provide good estimation capabilities. It is concluded that, Improved the Effort Estimation results using Function point analysis by utilizing the fuzzy techniques to overcome inputs in the form of linguistic terms. Improved project planning process by integrating the Non-Functional Point Analysis and Effort Estimation activity in software development project. Improved effort estimation results by providing high accuracy, security and reliability of the project. Overall conclusion from the research stated that proposed Model can be used to complement Function Point Analysis effort estimation by providing the preliminary software project Size based on effort factors, and calculate the Performance factors to improve the accuracy. This research indicates directions for further research work. The proposed tool can be analyzed in terms of feasibility and acceptance in the industry. Trying to improve the performance of existing methods and introducing the new techniques for effort estimation based on today s software project eclitation can be future research works in this area. CONFLICT OF INTERESTS The authors declare that there is no conflict of interests regarding the publication of this paper. ACKNOWLEDGMENTS The authors are greatly thankful to Maven tricks Technologies for providing the Real Project data sets for the experiments, and Valliammai Engineering

5 College, Chennai, for providing excellent lab facilities, making this work possible. REFERENCES 1. M. A. Al-Hajri, A. A. A. Ghani, M. N. Sulaiman and M. H. Selamat.(2005). Modification of standard function point complexity weights system. Journal of Systems and Software, M.A. Ahmeda and Z. Muzaffar.(2009). Handling imprecision and uncertainty in software development effort prediction: a type-2 fuzzy logic based framework. Journal of Information and Software Technology, Astha Dhiman and Chander Diwaker.(2013). Optimization of COCOMO II effort estimation using Genetic Algorithm. American International Journal of Research in Science, Technology, Engineering & Mathematics J.Frank Vijay and C.Manokaran.(2010). Initial Hybrid Method for Analyzing Software Estimation, Benchmarking and Risk Assessment Using Design of Software, Harsh Kumar Verma and Vishal Sharma.(2010). Handling Imprecision in Inputs using Fuzzy Logic to Predict Effort in Software Development. IEEE Transactions, X.Huang, D.Ho and J. Ren.(2008). A soft computing Framework for Software Effort Estimation.Soft Computing Journal, Springer, Imman Attarzadeh and Siew Hockow.(2010). Improving the accuracy of software cost Estimation Model Based on a New Fuzzy Logic Model. World Applied Sciences Journal, Jana Sedlackova.(2011). Security Factors in Effort Estimation of software Projects. Information Sciences and Technologies Bulletin of the ACM Slovakia, Martin Aver and Stefan Biffi.(2004). Increasing the Accuracy and Reliability of Analogy-Based cost Estimation with Extensive Project Feature Dimension weighting. International symposium on empirical software Engineering, V.V.Manoj and R.Swarup Kumar.(2012). A Novel Interval Type-2 Fuzzy software Effort Estimation Using Takogi-sugeno Fuzzy Controller. International Journal of Modern Engineering Research, Mohammad Azzeh, Daniel Neagu and Peter. I.Cowling.(2011). Analogy-Based Software Effort Estimation using Fuzzy Numbers. Journal of Systems and Software, H. Moussa, G. H. Galal-Edeen, and A.Kamel.(2009).Enhancing Software Sizing Adjustment Factors.Fourth International Conference on Intelligent Computing and Information Systems ICICIS. Cairo, Egypt, ACM, M.Senthil Kumar and B. Chidambara Rajan.(2014).Impact of Performance Metrics in software Effort Estimation using Function Point Analysis. Information An International Interdisciplinary Journal, M.Senthil Kumar and B. Chidambara Rajan.(2015).An Accurate FFPA-PSR Estimator Algorithm and Tool for Software Effort Estimation. The Scientific World Journal, 6 pages. 15. Wei Lin Du, Danny Ho, and Luiz Fernando Capretz.(2010). Improving Software Effort Estimation Using Neuro-Fuzzy Model with SEER-SEM. Global Journal of Computer Science and Technology,