Measuring the Function Points for Migration Project: A Case Study

<i>Introduction</i>: The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group&#x2019;s Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. <i>Aim</i>: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. <i>Method</i>: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. <i>Results</i>: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. <i>Conclusion</i>: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.

Some developer very capable of developing good quality working applications, but they don’t have the right value and price for their products, and it is because they did not use the right method to calculate the size of the product and valuate them correctly, most of them use analogy and expert judgment to perform their product sizing and valuation. This situation will create inconsistency in doing the pricing and it will create disadvantages for both developers and users. Function point analysis provides a methodology to perform software size calculation using algorithmic approaches that gives comprehensive and consistent results. In this document, I will explain step by step the processes of calculating the Function point using FPA procedures by implementing calculation formula of EI, EO, EQ, ILF, EIF, and VAP. I also will implement them to calculate the size of the “XIbar” application system, especially module customer care and customer info. By doing all these activities I hope will provide simple templates and procedures for calculating the size of application software that can be used easily by developers, students, and others who need it.

To maintain customer trust, Islamic banking continues to improve its services which rely on the use of information technology (software and its supporting peripherals). In the meantime, research on software in Islamic banking are still finite. Thus, the authors are interested in calculating the size and effort of working on the core application of an Islamic bank, which is called the Islamic Banking Application (IBA). Two methods are used in the study to see the size of the IBA. The size can be seen from the point of view of functional-based processes and non-functional aspects. The functional one, we propose the Use Case Points (UCP) method. In the UCP method, there are at least4 steps that must be passed for measuring software size. On the other hand, for non-functional measurement, we used the original Function Points Analysis (FPA) concepts. As a result, this research produces size measurement software using the FPA technique of 2,109.40 function points, while the UCP method results in 3,024.54 use case points. To convert size measurement into effort estimation, the author uses previous research with a productivity rate of 8.2 person/hour. Thus, the predicted effort consumed to develop the IBA is 17,297.08 person/hour (based on FPA technique) and 24, S01.23 person/hour (by UCP method). We found the deviation of 7,504 person/hour between both methods. This is due to the existence of different measurement parameters.

Service-Oriented Architecture (SOA) is an architectural concept to build software which emphasizes the use of services and comprises design principles. Research on this paper implements SOA design principles in the development of Animal Video Encyclopedia and Plant Video Encyclopedia. By implementing SOA, this paper would like to see if SOA affects the number of Function Points in the development of a software solution. Function Point numbers which are the result of Function Point Analysis (FPA) calculation have been widely used in software industry to measure the size of an application based on functionalities from user’s perspective. This paper uses FPA method to count the Function Points between both Animal and Plant Video Encyclodias solution implementing SOA and Animal and Plant Video Encyclopedias solution that does not implement SOA. Function Points from both solutions are then compared and analyzed based on the implementation of SOA design principles. Results show that numbers of Function Points of the solution implementing SOA appear to be smaller than the number of Function Points of the solution which is not implementing SOA.

Function Point Analysis (FPA) is a largely used technique to estimate the size of development project, enhancement project or applications already installed. During the point counting process that represents the dimension of a project or an application, each function is classified according to its relative functional complexity. Several studies resulted in FPA extensions, and most of them are mainly aimed at achieving greater precision in the point assessment of systems of greater algorithmic complexity. This work proposes the use of concepts and properties from fuzzy set theory to extend FPA into FFPA (Fuzzy Function Point Analysis). Fuzzy theory seeks to build a formal quantitative structure capable of emulating the imprecision of human knowledge. With the function points generated by FFPA, the functionality of the project is better represented than it was through FPA. Thus, derived values such as costs and terms of development can be more precisely determined.

This paper reports on the findings of an empirically based case study of the use of function points analysis (FPA) by the information systems division of a large financial services company. The software engineering productivity figures measured by FPA in this company varied widely across the departments of the division and projects. Investigation of the reasons for the variations showed that, in addition to factors such as the technology platform and application characteristics, organizational and human factors affect the accuracy and reliability of productivity figures. Elucidating the lessons from this case, this paper suggests that three factors – knowledge of the FPA, calibration of the function point productivity indicator and rigour of the measurement process – are critical to the successful implementation of an FPA programme. These findings and the issues identified in the paper will be of interest to academics in the area of software productivity measurement and companies considering FPA as a productivity metric.

This paper investigates correlating the basic elements of Unified Modeling Language and Cyclomatic Complexity with Function Point Analysis (FPA) principles to develop an automated software functional sizing tool. This concept has been difficult to achieve due to the logical nature of the FPA sizing methodology versus the physical nature of source lines of code (SLOC). In this approach, we examine software complexity from design and maintainability perspectives in order to understand relationships in physical code. Our hypothesis is that this method will “simulate” FPA principles and produce an objective sizing method. This would provide the foundation for an automated tool that scans physical software code to derive “Objective Function Points”(OFPs) functional size measure.

Function Point Analysis (FPA) is the most widely used method for measuring the size of software requirements, usually for the purpose of cost estimation. Unfortunately, FPA is affected by several drawbacks: it must be performed by specifically skilled personnel, it is expensive, and the resulting measures are subject to high variability. In order to solve - at least partially - these problems, researchers have proposed to base FP counting on UML models. However, models built without having FPA in mind hardly provide the required information at the proper detail level, so that the measures of the models tend to vary accordingly. On the contrary, building models that are suitable for FPA generally requires additional notations, skills and effort, thus partly spoiling the advantages of the approach. This paper illustrates a technique for building FPA-oriented UML models that do not need to include more information than usually required by the development process, and are easy to measure. As a result, FPA can be performed in a seamless way, while yielding reliable results. The proposed technique was validated by means of a controlled experiment and a set of pilot applications, which are also briefly described in the paper.

Software cost estimation is crucial to software management, which has received considerable attention from both industry and academia. Software size is an important metric that forms the cornerstone of software cost estimation. The function point has been proven to be a useful software size unit for size estimation and has been successfully implemented in many countries. However, in current practice, the rule of function point size method is complicated and performed manually. Consequently, it is costly in both time and resources spent to apply these methods, especially in the scenario of large-scale software development in the industry. In this paper, a deep learning-based named entity recognition (NER) model was designed in place of manual function point recognition. In particular, a BiLSTM-CRF model was trained on previously labeled requirements in the industry to classify the function point type of new requirements in the same domain. The proposed method was verified on 29 real projects provided by our industry partner. A comparative experiment was designed for the quantitative evaluation of efficiency improvement of the proposed NER model aided function point estimation. The result suggests that, for the NER model, the precision and F1 of the BiLSTM-CRF-based function point analysis on test samples achieved 94.5% and 80.3%, respectively. Moreover, the improvement in the efficiency of the software size estimation process achieved an average of 38.6%, which is a significant enhancement for the function point-based software size estimation.

Overviews of the estimation model function point analysis (FPA) and the operational software development method Jackson system development (JSD) are given. The adaptation to JSD projects of two main versions of the FPA method is described. A number of issues are raised concerning both the applicability of FPA-based techniques to JSD projects and general ways in which FPA estimation might be improved. A summary is presented of the results obtained by applying the two adaptations to an actual commercial JSD project, and various objectives are highlighted for future research.

Function point analysis is a widely cited method for estimating software project size, which is an important activity of project management. At the beginning stage of planning, the top-down approach can be applied. Having obtained more systems specifications at later stages, the bottom-up approach might also be used to improve the accuracy of the estimation. However, the bottom-up approach is not a conventional way of function point analysis. There was no empirical evidence showing the difference between the fully informed top-down approach and the bottom-up approach. Through the implementation of a function point analysis system in an in-house software development department, this paper compares the results of the two approaches. This comparison study shows that the bottom-up approach does not contribute a significant added value to a fully-informed top-down approach. Therefore, the fully-informed top down approach has been chosen as a method for building a software metric database in the organization. More important, the observations and experience gained from this project may help in-house development organizations to establish their own function point analysis systems.

Effective management of the software development process requires that management be able to estimate total development effort and cost. One of the fundamental problems associated with effort and cost estimation is the a priori estimation of software size. Function point analysis has emerged over the last decade as a popular tool for this task. Criticisms of the method that relate to the way in which function counts are calculated and the impact of the processing complexity adjustment on the function point count have arisen. SPQR/20 function points among others are claimed to overcome some of these criticisms. The SPQR/20 function point method is compared to traditional function point analysis as a measure of software size in an empirical study of MIS environments. In a study of 64 projects in one organization it was found that both methods would appear equally satisfactory. However consistent use of one method should occur since the individual counts differ considerably. >

Software cost is estimated through the effort and number of functioning components measured in terms of person month (p-m) and function points (FPs) respectively. In this paper we have considered the software cost based on the FPs because FPs is independent of the technologies. Initially function point analysis (FPA) was designed without any reference to the theoretical foundation which is based on the measurement done by the expert team. Function point data is described for more than hundred software development projects in the literature. It was also discussed about limitations of the resulting model in estimating development effort. This paper attempts to study and quantify the software cost in case of multiple projects or set of softwares. In case of single project or software, we attempt to study and quantify the function point counts (FPCs) for different components of the software or function types (FTs). Maximum Entropy Principle (MEP) is a very popular technique to estimate the maximum information or entropy subject to the given constraints. This paper presents an application of Maximum Entropy Principle (MEP) to distribute the Unadjusted Function Point Counts (UFPCs) subject to a given software cost. Thereafter, this application is applied over set of softwares to allocate the individual software cost when total cost to the software was given. In this paper we have also analyzed the proportionate of Unadjusted Function Point Counts (UFPCs), Number of FPs (# FPs), and weight of the different functional components or FTs for given software cost.

The approach to requirements modelling based on problem frames is gaining increasing attention and popularity. However, the usage of problem frames in industrial software development processes requires that some industrial practices, like cost estimation, are supported. This paper explores the possibility to apply the most popular functional measurement method, namely Function Point Analysis, to user requirements represented using problem frames. In particular, a simple problem is modelled using problem frames "both in their native notation and using UML" and its functional size in Function Points is measured. This case study indicates that it is actually possible to apply Function Point Analysis to problem frames, and provides some insight into the measurement process to be adopted.

Nowadays, software effort estimation plays an important role in software project management due to its extensive use in industry to monitor progress, and performance, determine overall productivity and assist in project planning. After the success of methods such as IFPUG Function Point Analysis, MarkII Function Point Analysis, and COSMIC Full Function Points, several other extension methods have been introduced to be adopted in software projects. Despite the efficiency in measuring the software cost, software effort estimation, unfortunately, is facing several issues; it requires some knowledge, effort, and a significant amount of time to conduct the measurement, thus slightly ruining the advantages of this approach. This paper demonstrates a functional size measurement tool, named UML Point tool, that utilizes the concept of IFPUG Function Point Analysis directly to Unified Modeling Language (UML) model. The tool allows the UML eXchange Format (UXF) file to decode the UML model of mobile game requirement and extract the diagrams into component complexity, object interface complexity, and sequence diagram complexity, according to the defined measurement rules. UML Point tool then automatically compute the functional size, effort, time, human resources, and total development cost of mobile game. Besides, this paper also provides a simple case study to validate the tool. The initial results proved that the tool could be useful to improve estimation accuracy for mobile game application development and found to be reliable to be applied in the mobile game industry.