Avinash Gaur 1, Anil Kumar Patidar 2

Transcription

1 A New Technique for Function Point Analysis VOLUME 1 ISSUE 7 Avinash Gaur 1, Anil Kumar Patidar 2 ABSTRACT: Software size estimation is most vital input for software cost and software effort estimation. As the size and complexity of software is increases the software expansion has turn into a more rowdy procedure and for this reason it desires to take concern of even the effortless movement in the expansion procedure. Therefore improving the correctness of software size estimation finally results in improving the accurateness of the software effort and software cost estimates. These estimates are utilized in budgeting, scheduling, staffing, planning etc. Although when we compute these estimates, only high level software project needs are available to us. Utilizing this high level knowledge/information to create exact software size estimates is a really challenging job. There are several methods are proposed in past for software size estimation, but from these methods function point analysis is very popular. In this paper we suggest a new method for function point analysis with two new general system properties and we also modernize the function point analysis for reengineering projects. 1. INTRODUCTION: T he software development came in to existence around 60 years ago. Right from the beginning to till date the software engineering is continuously evolving the new techniques for developing quick, cheap and high quality software. Everything revolves around cost, schedule and quality. One such evolving field of software development is estimation models for software size and effort [1]. Project is ever behind over budget, schedule, and of poor quality. So essential steps are compulsory to estimate size, effort, and cost exactly. Quality is a real challenge for most of the difficult software projects. The key attributes of the software project quality are performance, accuracy, security, reliably, availability etc. Thus there is a need of novel matrices for potentially and efficiently estimating size. At the starting of the software project, there is a need of knowledge/information. Because of this lack of knowledge/information most of the estimation models like FPA, COCOMO uses one size_fits_all approach to calculate size and effort. It does not give accurate results in most of the modern application development. The expert user programming also affects the size of software. Software cost estimating has been a vital but complex task since the beginning of the computer era in the 1940s. As software projects have grown up in size and importance, the requirement for correctness in software project cost estimating has grown up. In the early days of software, computer programs were usually fewer than 1000 machine instructions in size (or fewer than 30 function points), needed only one programmer to write down, and sometimes took more than a month to finish. The complete development costs were often fewer than $5000. Although cost estimating was complex, the economic consequences of costestimating inaccuracy were not very serious [5]. Today few large software systems exceed 25 million source code statements (or more than 3 lakh function points), may need technical staffs of 1000 personnel or more, and may have more than 5 calendar years to finish. The evolution costs for such large software systems can exceed $500 million; thus, mistakes in cost estimation can be too much serious indeed [5]. The rest of the paper is designed as follows. Section 2 talk about the different software size estimation metrics/techniques, Section 3 talk about related work on different techniques of size, and effort estimation techniques, Section 4 talk about problem & proposed solution, Section 5 shows the result analysis and lastly, the conclusion is discussed in section SOFTWARE SIZE ESTIMATION: The size of software is one of the key components that affect the performance & cost of software and it describes the amount of complexity in problem in terms of time and effort needed to develop the software project [3]. ALL RIGHT RESERVED 65

2 Line of Code Functio n Point Metrics for Software Size Estimation Featur e Point Use Case Point Figure 1: Metrics for Software Size Estimation There are a number of metric is defined in literature that shown in figure 1, for correctly estimate the software size, which can denote the software size [4] [6]. On the basis of above metrics following are the software size estimation techniques: a. Line of Code (LOC): In this technique, the size of software is estimated by calculating the number of source instructions in the software program to be developed, excluding header lines and comments. Only after the completion of project we get the correct LOC. There is no accepted standard definition of LOC and this is major drawback of LOC. Also, in final software product from problem specification, the correct estimation of LOC is very complex. Therefore, the LOC metric is of less use to project managers during the project planning [4] [6]. Object Points b. Function Point (FP): The problem of LOC is overcome by FP [3]. Allan Albrecht (1983) offered Function Point metric to calculate the functionality of the software project [7]. Instead of calculating the line of codes (LOCs) of a developed solution (software) for the problem, it calculates the size of the problem to solve [3]. The 5 user function types for which calculations are made are divided into two categories [8]: Data function Types: Internal Logical File (ILF) & External Interface Files (EIF). Transactional function types: External Input (EI), External Output (EO) & External Inquiry (EI). Every instance of the above 5 function types afterward categorized according the complexity level. Complexity levels find out a set of weights, which after applied to their analogous function counts to find out UFP (unadjusted function points) quantity. On the basis of quantity of data element types included and the quantity of file types suggested each function count is categorized into complexity levels as low, average and high [6]. The multiplication of complexity weights and the counts for each function element, gives the UFP. By adding all the counts of weighted functions we get UFP [9]. In addition to the value of above elements, 14 additional software system characteristics are utilized in the measurement process. The addition of their produced rating scores is known as TCF (Technical Complexity Factors) that states their influence on the whole complexity of the software system [10]. Each element can rated in the range from 0 to 5 where 0 shows that the element has no effect on the software project and 5 denotes the element is necessary and very significant correspondingly [8]. The ultimate Function Point (FP) can be evaluated as [9]: FP=UFC*TCF FP is independent from construction technique [9]. Function points are also not dependent on the methods, tools or language utilized for the implementation [11]. But the computation of FP is difficult and differs for different categories of software systems [9]. c. Feature Point: Feature Point extended form of function points in which algorithms are included as a new class. In this each algorithm utilized, given a weight choice from 1 (for elementary) to 10 (for sophisticated algorithms). The feature point is computed as weighted summation of the algorithms with addition of function points. FPP (Full Function Point) is one more extension of the function points is used for evaluating real time application [12]. d. Use Case Points (UCP): Gustav Karner was developed the Use Case Points technique. UC Points are calculated from the analysis of use case system. During early phases of an object oriented software project UCP are computed that captures its extent with use cases. To construct point count every use case is extent as easy, average or hard. We also regulate the UC points for software project s technical & personnel features, and directly transformed to the hours in order to achieve a jagged idea of a supposed project agenda [9]. e. Object Points: Whereas Feature Point and Full Function Point (FFP) broaden Function Point, the Object Point determines the size from different directions. This measurement is stand on the amount and complexity of pursuing objects. Comparatively object point is novel metric and not been yet well liked, but it is easy to use [12]. 3. LITERATURE SURVEY: ALL RIGHT RESERVED 66

3 There are various techniques are presented in past for estimation of size and effort of a software project. Estimation by expert technique is illustrated in [13], analogy based estimation schemes is presented in [14], algorithmic techniques together with empirical methods presented in [15], rule induction techniques presented in [16], artificial neural network supported approaches described in [17] [18] [19], Decision tree supported techniques demonstrated in [20] and fuzzy logic supported estimation techniques presented in [21]. Among these expanded models, empirical estimation models are discovered to be possibly exact compared to other estimation techniques and COCOMO, SEER SEM, SLIM and FP analysis techniques are well liked in practice in the empirical class [22] [23]. The most admired algorithmic estimation models comprise Boehm s COCOMO (constructive cost model) [2]. Thus, perfect estimation techniques, for example, the Function Point technique, have achieved increasing significance [24]. The size is found out by recognizing the elements of the system as seen [24] by utilizing the end user like the inputs, outputs, interfaces, inquiries [2] to further systems and logical internal files [25]. Some more techniques proposed in literature are as follows: 1. Chander Diwaker and Astha Dhiman [6]: In this paper authors presented, an overview of software size and effort estimation techniques. In ideal size estimation techniques comparatively simple metrics are defined which are directly correlated to the product size and these techniques are independent from selected construction technique and could be applied at early stage in the software project life cycle. For well known software projects, we should utilize the expert judgment technique or analogy approach if the likeness of them can be found, because it is reliable and fast. Algorithmic model similar to COCOMO II are better to utilize for large and lesser well known software projects. COCOMO II model provides a framework for a wide amount of current data assembling and study effort to further improve and regulate the model's estimation abilities. Because of more variables are considered consist of reuse parameter; it gives the correct result. In the web based software application development for estimating the cost, this parameter is one of the necessary variables. Appropriateness of estimation techniques is depends on some key factors similar to Software application domain, availability of historical data, product complexity, team expertise etc. 2. Vahid Khatibi, Dayang N. A. Jawawi [7]: In this paper authors presented, an overview of various existing techniques for software cost estimation. On the basis of the outcomes of various past techniques presented, the main source for software project crashes is incorrect estimation in early steps of the software project. In recent study most of the present estimation techniques have been demonstrated systematically. Software project managers select the best estimation technique based on the situations and position of the project, illustrating and consisting of estimation methods can be helpful for falling of the project crashes. No one single estimation technique is present which provides best estimates in every situation and each technique having a special area of software applications and it can be appropriate only in these special software projects. Performance of every estimation technique is depends on various parameters such as duration of the software project, complexity of the software project, expertise of the personnel, development techniques etc. 3. M. Pauline, P. Aruna and B. Shadaksharappa [27]: In this paper authors discussed, a model for estimating software efforts, which concentrates on reducing the effort by improving the adjustments made to the techniques of functional sizing. To simplify the procedure of adjustment factors and to provide more consistency in the adjustments, the idea of grouping is established. This technique utilizes fuzzy logic for measuring the quality of necessities and this quality factor is used as one of the adjustment factor. The computed function point from the model is supply as input to the well known COCOMO II model for estimation of cost whose cost factors can be adapted to the individual development environment, further which is essential for the correctness of the cost estimates. In future to reduce the risk of surprises and unexpected interruption, the estimation of cost should be made more carefully throughout the software project life cycle. 4. M. Pauline [28]: In this paper authors proposed, an efficient system for estimation of effort and cost, based on quality assurance coverage and this paper also give attention on a problem with the recent methods for ALL RIGHT RESERVED 67

4 determining function points that confines the effective utilization of function points and advise an adaptation to the approach that should improve the accuracy. To simplify the procedure of adjustment factors and to provide more consistency in the adjustments, the idea of grouping is established. This technique utilizes fuzzy logic for measuring the quality of necessities and this quality factor is used as one of the adjustment factor. The calculation for estimation of effort and cost using the author s suggested model taking HR application and hospital desktop application as case studies. The computed function point from the model is supply as input to the static single variable model (well known COCOMO II and Intermediate COCOMO) for estimation of cost whose cost factors can be adapted to the individual development environment, further which is essential for the correctness of the cost estimates. In this ISO 9126 quality factors are utilized for quality assurance and the function point metric is utilized for the weighing factors as an estimation approach. From the results author s shown that author s proposed model generated values are much closer to planned effort as compared to the existing method. 5. Jack E. Matson, Bruce E. Barrett, and Joseph M. Mellichamp [29]: In this paper authors presented, different models for estimation of cost by using Function points in Software Development. 5.1 SEER SEM Estimation Model: SEER SEM model is owned by Galorath Associates Inc. in 1980 (Galorath 2006), it designed specially to monitor, plan and estimate the effort and resources needed for any kind of software maintenance and/or development projects. SEER SEM, referring to one containing the capability to predict the future, relies on parametric algorithms, simulation based probability, knowledge bases, and historical precedents to permit engineers, project managers, and cost analysts to correctly estimate a project's cost schedule, effort and risk before the project is initiated. This estimation model is based on the early work of Dr. Randall Jensen. The mathematical equations utilized in SEER are unavailable to the public, although the writings of Dr. Randall Jensen create the fundamental equations accessible for review. Dr. Randall Jensen called this fundamental equation as "software equation" and it is as follows: Se =Cte*(K*td)*0.5; where, S is the effective lines of code, Cte is the effective developer technology constant, K is the total life cycle cost in man years, and td is the development time in years. 5.2 Albrecht and Gaffney model: This model developed by IBM DP Services Organization utilizes function point for effort estimate. Albrecht and Gaffney provide the counts of function point and the resultant work hours, which are known as effort, for every software project. E = FP 5.3 Kemerer model: Kemerer model is used for estimation of cost and utilizing this model linear regression and function points. The dependent variable, Effort, is determined in man months where one man month is 152 workhours. E = FP 5.4 SLIM Estimation Model: The SLIM (Software Life Cycle Model) was established in the early 1970s by Larry Putnam. SLIM employs the probabilistic opinion called Rayleigh allocation between personnel level and time. SLIM is most widely used model of these types of early developed models. The models which are closely interrelated to SLIM includes COCOMO (Constructive Cost Model), PRICE S (Parametric Review of Information for Costing and Evaluation Software), and SEER SEM (Software Evaluation and Estimation of Resources Software Estimating Model). In realistic use, the effort of a software project is estimated by solving the following software equation: Effort: LOC = c * K 0.3 * T SMPEEM: The Software Maintenance Project Effort Estimation Model (SMPEEM) is proposed in which Software maintenance size is discussed. The SMPEEM utilizes function points to compute the amount of the maintenance function E = FP * COCOMO: Constructive Cost Model (COCOMO) is one of the well known and systematically documented effort estimation models for software and it is based on regression techniques. This model combined three different modeling levels of detail: Basic, Intermediate, and Detailed. Generally in COCOMO the modeling process has three categories of systems embedded, organic and semi detached. ALL RIGHT RESERVED 68

5 The COCOMO computes the quantity of effort and based on this calculated effort, compute cost, time and number of staff for software projects. The next version of COCOMO is COCOMO 81 be the primary and stable model on that time. COCOMO II was suggested and grown to solve most of the problems of COCOMO COCOMO 81(Intermediate COCOMO): An empirical estimation system known as COCOMO 81 was proposed in 1981, to estimate the effort, cost, and time schedule for software projects. These formulae tie the size of the software system and Effort Multipliers (EM) to discover the effort to build up a software system. In COCOMO 81, effort is articulated as Person Months (PM) and it can be computed as PM = a * Size b * ΣEMi where, a and b are the domain constants of the model. It consists of 15 effort multipliers. In this estimation technique past project data and experience is utilized, which is really difficult to understand. 5.8 COCOMO II: In 1997, an enhanced scheme for estimating the effort for software development activities, which is called as COCOMO II. In COCOMO II, the effort requirement can be calculated as: Effort = A * [SIZE] B * Π I=1to17 EFFORT Multiplier Where B = * Σ J=1to5 SCALE FACTOR For the completion of software project cost drivers are utilized to confine features of the software growth that affect the effort. COCOMO II utilized to predict effort, time and other parameters that produce accurateness in strong co linearity and greatly variable prediction. COCOMO utilizes LOC (Lines of Code) like an estimation parameter and also FP (Function Point) as one of the estimation parameter. 4. PROBLEM & PROPOSED SOLUTION: PROBLEM: Software size estimation is most vital input for software cost and software effort estimation. As the size and complexity of software is increases the software expansion has turn into a more rowdy procedure and for this reason it desires to take concern of even the effortless movement in the expansion procedure. Therefore improving the correctness of software size estimation finally results in improving the accurateness of the software effort and software cost estimates. These estimates are highly important and utilized in various applications of budgeting, scheduling, staffing, planning etc. Although when we compute these estimates, only high level software project needs are available to us. Utilizing this high level knowledge/information to create exact software size estimates is a really challenging job. There are several methods are proposed in past for software size estimation as we discussed above like LOC, FPA, object point, use case point, feature point but all the methods except FPA are very less in use because of their calculation difficulty and some other limitation as we discussed above. ALL RIGHT RESERVED 69

6 PROPOSED SOLUTION: As we know that the existing system contains fourteen general system characteristics (GSC) on which calculation of value adjustment factor (VAF) is dependent and ultimately it affects the value of function point count. In this paper, we suggest a new function point based method with two new general system characteristic (GSC) for the accurate software size estimation and we also updated function point analysis for reengineering software project. It contains an additional term SDDMFP (source to destination data migration function point) which is the migration from the source data format to destination data format function points. So, SDDMFP is the no. of external inputs required to convert the source data files to target data files. Table 1 shows the general system properties, in this table GSC 1 to GSC 14 are already exist and we have proposed two new GSC 15 and GSC 16 (Facilities for Users and Issue Management & Solving). The general system property Facilities for Users contains 20 elements by which we calculate the degree of impact of Facilities for User GSC. As we know that the value of UFC is depending on data function types and transaction function types as discussed above. So the values of this data and transaction function types along with the value of SDDMFP (if 2 external inputs with average difficulty are needed to migrate) are shown in table Function Point FP = UFC*VAF 4. Find the value of SDDMFP (source to destination data migration function point). 5. Finally, calculate the value of Re engineering Function Point count as 6. Re engineering function point if calculated as follows As we have discussed above existing system uses 14 GSC and proposed system uses 16 GSC. So, on the basis values described in table 1 and table 2 the computation of function point is as follows Using Existing System: Using Proposed System: 5. RESULT ANALYSIS: We performed all the experiment with the help of NetBeans IDE. With the help of results generated above we created a graphical analysis as shown in figure 2. So, on the basis of experimental results, we can say that proposed system performs better as compared to existing system. The analysis of Function Point is done in following steps: 1. Identify and rate to each transactional function types and data function types to calculate the value of Unadjusted Function Point count (UFC) as 2. Rate to each General System Characteristics (GSCs) (according to the given software project) and determine the Value Adjustment Factor (VAF) as CONCLUSION: Recently, software size and effort estimation is very significant area for researchers in software ALL RIGHT RESERVED 70

7 estimation. In reality, size is the most dominant input of these models. The accurateness of effort and cost estimation is depends on exact size estimates more than on any other effort and costrelated parameters. In this research paper we offered a new function point analysis method by proposing two new GSC s and also try it for reengineering projects. The experimental results shows sufficient enhancement in proposed system as compared to existing system. REFERENCES: 1. Ms. Alka Soniya and Mr. Pawan Ratadiya, A New Way to Estimate the Size and Effort of Software for Expert User Programming, International Journal of Technology Research & Management (IJTRM), Volume 1 Issue 2, May Boehm B.W., Software Engineering Economics, Prentice Hall, Englewood Cliffs, NJ, Lionel C. Briand and Isabella Wieczorek, Resource Estimation in Software Engineering, In the second edition of the Encyclopedia of Software Engineering, 2001, pp Rajib Mall, Fundamentals of Software Engineering, pp roduction to software cost estimation. 6. Chander Diwaker and Astha Dhiman, Size and Effort Estimation Techniques for Software Development, International Journal of Software and Web Sciences (IJSWS), Vahid Khatibi and Dayang N. A. Jawawi, Software Cost Estimation Methods: A Review, Vol. 2, no. 1, ISSN , pp K. K. Aggarwal and Yogesh Singh, Software Engineering, 3rd Edition, pp Bogdan Stępień, Software Development Cost Estimation Methods and Research Trends, Computer Science, Vol. 5, 2003, pp Xiangzhu gao and Bruce Lo, An Integrated Software Cost Model Based on COCOMO and Function Point Approaches, Southern Cross University, In 1995 IEEE, pp Liming Wu, The Comparison of the Software Cost Estimating Methods, University of Calgary, pp Hareton Leung, Zhang Fan, Software cost estimation, Department of Computing, The Hong Kong Polytechnic University, pp Saleem Basha and Dhavachelvan P., Analysis of Empirical Software Effort Estimation Models, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 3, Chiu NH and Huang SJ, The Adjusted Analogy Based Software Effort Estimation Based on Similarity Distances, Journal of Systems and Software, Volume 80, Issue 4, pp , Kaczmarek J and Kucharski M, Size and Effort Estimation for Applications Written in Java, Journal of Information and Software Technology, Volume 46, Issue 9, pp , Jeffery R, Ruhe M and Wieczorek I, Using Public Domain Metrics to Estimate Software Development Effort, In Proceedings of the 7th International Symposium on Software Metrics, IEEE Computer Society, Washington, DC, pp 16 27, Heiat A, Comparison of Artificial Neural Network and Regression Models for Estimating Software Development Effort, Journal of Information and Software Technology, Volume 44, Issue 15, pp , K. Srinivasan and D. Fisher, "Machine learning approaches to estimating software development effort", IEEE Transactions on Software Engineering, vol. 21, pp , A. R. Venkatachalam, "Software Cost Estimation Using Artificial Neural Networks", Presented at 1993 International Joint Conference on Neural Networks, Nagoya, Japan, R. W. Selby and A. A. Porter, "Learning from examples: generation and evaluation of decision trees for software resource analysis", IEEE Transactions on Software Engineering, vol. 14, pp , Huang SJ, Lin CY and Chiu NH, Fuzzy Decision Tree Approach for Embedding Risk Assessment Information into Software Cost Estimation Model, Journal of Information Science and Engineering, Volume 22, Number 2, pp , M. Van Genuchten and H. Koolen, "On the Use of Software Cost Models", Information & Management, vol. 21, pp , T. K. Abdel Hamid, "Adapting, Correcting, and Perfecting softwareestimates: A maintenance metaphor", in Computer, vol. 26, pp , Peischl, B., Nica, M., Zanker, M. and Schmid W. Recommending effort estimation methods for software project management, Proc. IEEE/WIC/ACM Int. Conf. on Web Intelligence ALL RIGHT RESERVED 71

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