Use Case Points Research Articles

Use Case Point Methods For Analysis of Software Development Cost In Information Communication Technology Units of Khairun University

The Information and Communication Technology Unit (ICTU) is a technical implementation unit in the field of information and communication technology development and management. Currently, Khairun University's ICTU is undertaking a software development project, one of which involves creating an application called "Permata". In the process of developing this application, one of the common challenges faced is estimating the project costs for software development, as the developers do not yet have a standard method to determine the costs of software development. The objective of this research is to produce a Use Case Point application for estimating the costs of software development at ICTU, Khairun University. The Use Case Point (UCP) method is derived from the Function Point Analysis (FPA) method and aims to provide a simple estimation method oriented toward software project objects. The software objects involved in this method are Actor Types (UAW), Use Case Weight (UUCW), Technical System Development Complexity (TF), and Development Environment Complexity (EF). The estimated time value for developing the Permata application is 1,389.844855 man-hours, equivalent to approximately 1.904 months. The estimated cost for the Permata application is Rp129,099,127.

A Methodology to Analyze and Estimate the Software Development Process Using Machine Learning Techniques

Analyzing the software development process and estimating the effort required for its completion is an essential task. In the case of Agile methodology, the values of the parameters used for estimation vary frequently as the scope of the project changes with changes in the requirements of the clients. Hence, the estimation done at the initial phase will not be appropriate until the completion of the project. Therefore, to overcome this issue, a methodology is proposed to estimate the duration of a project by applying machine learning techniques. The use-case point method is used for estimating the duration. Information about the number of use cases and values for environmental and technical factors is stored in a repository. Few values may be uncertain, and to estimate the effort for a new project with few unknown or uncertain values, the machine learning algorithm Gaussian Process Regression (GPR) is used. The repository information is taken as the training dataset, and the new project data is taken as the test dataset. The estimated value shows the accurate duration for the new project. The result is validated with a popular dataset.

Fuzzy Use Case Points as a Basis for Effort Estimation

Many software development projects encounter problems related to over- or under-estimation of effort. Accurate effort estimation is crucial for successful project management, but it can be challenging when resources are limited, and little is known about the project. The commonly used method for effort estimation is Use Case Points (UCP), which is mainly used for application-based objects and takes use cases as input. However, UCP has weaknesses, particularly in the high variation of weight factor values for Unadjusted Use Case Weight (UUCW). To address this problem, Fuzzy Use Case Points (FUCP), which is a combination of fuzzy logic and use case points, can be used. By applying fuzzy logic to the UUCW category, FUCP derives new weight factor values for UUCW. The implementation of FUCP to calculate effort estimation in ten government-based projects in this research has shown that FUCP yields the closest value to the actual effort required. It has also been demonstrated that FUCP outperforms UCP in terms of accuracy, with an improvement of 6.51%.

Towards non-linear regression-based prediction of use case point (UCP) metric

Towards non-linear regression-based prediction of use case point (UCP) metric

On the value of project productivity for early effort estimation

In general, estimating software effort using a Use Case Point (UCP) size requires the use of productivity as a second prediction factor. However, there are three drawbacks to this approach: (1) there is no clear procedure for predicting productivity in the early stages, (2) the use of fixed or limited productivity ratios does not allow research to reflect the realities of the software industry, and (3) productivity from historical data is often challenging. The new UCP datasets now available allow us to perform further empirical investigations of the productivity variable in order to estimate the UCP effort. Accordingly, four different prediction models based on productivity were used. The results showed that learning productivity from historical data is more efficient than using classical approaches that rely on default or limited productivity values. In addition, predicting productivity from historical environmental factors is not often accurate. From here we conclude that productivity is an effective factor for estimating the software effort based on the UCP in the presence and absence of previous historical data. Moreover, productivity measurement should be flexible and adjustable when historical data is available.

Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method

The quality of a product is one of the major interests of the manufacturing process in all industries. The software industry imposes to construct a project with several phases to ensure producing high-quality software. A software development company estimates time, effort and cost of the project during planning phase. It is important to have accurate estimations to reduce the risks of project failure. Several cost estimation methods are practiced in the software development companies such as Function Point (FP), Use Case Points (UCP), Constructive Cost Model I and II and Story Points (SP). UCP cost estimation method is taken in this research to improve the accuracy of its estimation. UCP estimation depends on the use case diagram of the proposed system. A use case diagram describes the main functional requirements of the proposed system. UCP partially considers non-functional requirements through the technical and environmental factors. There is a lacking in the UCP method to consider the importance of quality attributes in the estimating process. This paper proposes an extended version of the existing UCP method named Quality Extended Use Case Point (QUCP) method in which quality attributes are included to improve the accuracy of cost estimation. A questionnaire is used to validate the proposed QUCP method. It is found after data analysis that seventy five percentages of the participants are agreed that the proposed method will not only help to improve the accuracy of cost estimation but it will also enable a software development company to deliver high-quality products.

Empirical analysis on productivity prediction and locality for use case points method

Use case points (UCP) method has been around for over two decades. Although there was a substantial criticism concerning the algebraic construction and factor assessment of UCP, it remains an efficient early size estimation method. Predicting software effort from UCP is still an ever-present challenge. The earlier version of UCP method suggested using productivity as a cost driver, where fixed or a few pre-defined productivity ratios have been widely agreed. While this approach was successful when not enough historical data is available, it is no longer acceptable because software projects are different in terms of development aspects. Therefore, it is better to understand the relationship between productivity and other UCP variables. This paper examines the impact of data locality approaches on productivity and effort prediction from multiple UCP variables. The environmental factors are used as partitioning factors to produce local homogeneous data either based on their influential levels or using clustering algorithms. Different machine learning methods, including solo and ensemble methods, are used to construct productivity and effort prediction models based on the local data. The results demonstrate that the prediction models that are created based on local data surpass models that use entire data. Also, the results show that conforming to the hypothetical assumption between productivity and environmental factors is not necessarily a requirement for the success of locality.

Predicting software effort from use case points: A systematic review

Context: Predicting software project effort from Use Case Points (UCP) method is increasingly used among researchers and practitioners. However, unlike other effort estimation domains, this area of interest has not been systematically reviewed. Aims: There is a need for a systemic literature review to provide directions and supports for this research area of effort estimation. Specifically, the objective of this study is twofold: 1) to classify UCP effort estimation papers based on four criteria: contribution type, research approach, dataset type and techniques used with UCP; and 2) to analyze these paper from different views: estimation accuracy, favorable estimation context and impact of combined techniques on the accuracy of UCP. Method: We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, selecting quality papers, extracting data and drawing results. Result: The authors of UCP research paper, are generally not aware of previous published results and conclusions in the field of UCP effort estimation. There is a lack of UCP related publications in the top software engineering journals. This makes a conclusion that such papers are not useful for the community. Furthermore, most articles used small numbers of projects which cannot support generalizing the conclusion in most cases. Conclusions: There are multiple research directions for UCP method that have not been examined so far such as validating the algebraic construction of UCP based on industrial data. Also, there is a need for standard automated tools that govern the process of translating use case diagram into its corresponding UCP metrics. Although there is an increase interest among researchers to collect industrial data and build effort prediction models based on machine learning methods, the quality of data is still subject to debate.

Use Case Points (UCP) with 3 Point in Program Evaluation and Review Technique (PERT) to Estimate Effort Software

Software has an important influence in business to accelerate and manage business processes better. Software development projects and projects in general require precise estimates for good business management and running smoothly. The method of Use Case Points is one of the methods used to estimate object-based software utterances, but there are some disadvantages such as subjective assessment of Technical Complexity Factor (TCF) and Environmental Complexity Factor (ECF) for novice developers who are inexperienced. Three points from the Program Evaluation and Review Technique (PERT) are used at UCP to help estimate the two factors better by modifying both assessment factors. By using 3 PERT points in UCP, it is expected to help novice developers in estimating the size of the software as predicted by experienced developers. Results of the calculation of the estimated software information systems sales of goods in the Madrasah Istiqlal foundation obtained an effort value is 1,011 man-hours.

Effort Estimation Approach through Extracting Use Cases via Informal Requirement Specifications

Sometimes unclearly describing the requirement specifications of satisfied customer’s needs, means it may be difficult to develop the production of high-quality software systems. A persistent issue of requirement engineering is how to clearly understand the requirements of the large and complex software project, and also how to analyze them exactly. To solve this problem, we propose a linguistic analysis method based on the semantic analysis of the Fillmore’s textual approach. This method extracts use-cases from informal requirement specifications. For applied requirement engineering with this method, we suggest extracting a use-case diagram, as well as calculating the software effort estimation with the original use-case point (UCP). To simply explanations of our use-case extraction method, we use one example of a simple postal information system.

A systematic review of studies on use case points and expert‐based estimation of software development effort

AbstractIn recent years, due to significant evolution in adopting new technologies and development methodologies in the field of software engineering, the developers and researchers are striving to optimize the accuracy of software effort estimation (SEE). The overestimation and underestimation both are the key challenges for software progress; hence, there is a continuous need for an accurate SEE. This paper highlights a systematic review of studies associated with the best practices of use case point (UCP) and expert judgment–based software development effort estimation techniques. The primary aim and contribution of this paper are to support the researchers through an extensive review to ease to other researcher's search for effort estimation studies. We have performed state‐of‐the‐art review from five viewpoints of reference: (a) review of studies concerning UCPs and expert judgment–based effort estimation, (b) research contribution and future recommendation in different novelties, (c) usage of the dataset, (d) availability of accuracy metrics, and (e) findings of the studies. We have performed a systematic review of studies which are published in the period of 2000 to 2019. We have selected a total of 34 primary studies of UCP and expert judgment–based estimation techniques to report the research questions stated in this review.

Effort Estimation For Software Development On Mobile Application Of 'Tangkap Reptil'

An essential aspect of planning and management of software design projects is to estimate work time, costs, and human resources. The calculation solution made in this study aims to assist in calculating the estimated time of developing a reptile capture application using the Use Case Point (UCP) method. The UCP method is a software effort estimation method that shows better performance compared to other methods. The result of this research is the risk of software development on the mobile application of 'Tangkap Reptil' has a small chance, can be done in a relatively short time, and does not require a lot of resources.

An Effective Software Effort Estimation based on Functional Points using Soft Computing Techniques

Still in this 21st century, it is a great challenge for the Project Managers to make the software projects successful. The success of software projects relies on how accurately the estimates of effort, cost and duration can be made. Most of the standard surveys stated that only 30-40% of software projects are successful and the remaining are either challenged, cancelled or failed. One of the key reasons for failure of projects is inaccurate estimations. Effort Estimation should be carried out in the early stage of Software Development Life Cycle (SDLC) and it is an essential activity to establish scope & business case of software project management activities. Over estimation or under estimation leads to failure of the software projects. Many of the stakeholders are expecting the estimation of development effort in early stage for their better bidding. There are many methodologies like KLOC, Use Case Points (UCP), Class Points, Story Points, Test Case Points, Functional Points (FP), etc. to estimate effort in the software development. To estimate the effort in the early stage of software development, UCP, Story Points and FP are more preferable. The methods for estimation may be adopted based on the project complexity, functionality, approaches etc. In order to achieve an efficient and reliable effort estimate and thereby have a proper execution of software development plan, Soft Computing Techniques can be adopted in the various organizations and different research domains. In this paper, Functional Points have been selected for effort estimation and implemented using soft computing techniques like Neural Networks and Neuro Fuzzy techniques. After examination the results are evaluated using different error measures like VAF,MMRE,RAE, RRSE and PRED. Basing on results it is observed that the Neuro Fuzzy techniques provided better effort estimates

A model of owner estimate cost for software development project in Indonesia

AbstractThe purpose of this study is to develop a model of owner estimate cost of software development projects. The model consists of three main components, namely, personnel direct costs, nonpersonnel direct costs, and taxes. Personnel direct costs are obtained by using the use case points (UCP) method that has been advanced. Initially, UCP was utilized to calculate the effort required to complete the software project. In this study, UCP was institutionalized to obtain personnel direct costs by distributing the acquired effort into each activity in a project. Further, the effort per activity is multiplied by the pay rate for each activity. Personnel direct costs are resulted by adding up the cost of all activities. The experiment has been conducted on nine software development projects in which the model is applied to get the estimated cost calculation.

Knowledge based non-functional software distinguishing quality effort estimation using fuzzy use case point approach

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.

Knowledge based non-functional software distinguishing quality effort estimation using fuzzy use case point approach

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.

Estimated software measurement base on use case for online admission system

This article aims to measure the size of the software Online Admission System using the method of Use Case Point (UCP). UCP method can help developers in the estimation of factor productivity development of software projects that include estimated time work, human resource needs, and the estimated cost of creating software. This method has the ability in providing the effort estimation approach which is needed to do a software development project. The measurement is carried out based on the number and complexity of software projects by analyzing the actor, use case, and a variety of technical factors as well as environmental factors. The result of the measurement is the software development project of online admission system belongs the medium category with the acquisition value of UCP 103.86, estimated cost 277,168,750 IDR and time work 1,454 hours

Project productivity evaluation in early software effort estimation

AbstractThe productivity factor has long been a key driver to estimate effort from Use Case Points (UCP) size measure, especially when historical dataset is absent. But, no one questions: Does productivity still matter when historical data are also available? To facilitate answering this question, the present paper studies the role of productivity from 2 perspectives. First, does learning productivity from historical data lead to better accuracy than using fixed productivity ratios? Second, what is the impact of ignoring productivity when estimating the effort from UCP? Five different models that use productivity factor have been used under different experimental settings and compared with some regression models that use only UCP size metrics. We found that dynamically learning and adjusting productivity from historical data are more efficient than using fixed productivity values. Moreover, using UCP size variables to estimate effort tends to be more accurate than using productivity and UCP variables. We also did not find any significant improvement when using UCP adjustment factors for measuring productivity. Finally, we conclude that the productivity factor is a good driver to generate effort estimate from UCP in the presence and absence of historical datasets. But using UCP size variables alone for predicting effort is more accurate than using productivity.

Implementation of use case point as software effort estimation in Scrum Framework

Estimation is a mechanism to estimate and provide a value for software development projects. Use Case Point (UCP) is a method of software estimation effort in software development based on Use Case model. Scrum is a framework for software development that can solve complex problems and always change. The aims of this study are finding the implementation of UCP as a method of estimating efforts software development with Scrum framework. The methodology of this research is identifying the estimation process with UCP and examine collaboration with Scrum Framework. The results of UCP and Scrum Framework collaborations will be simulated using a web-based project management system. The result of this research is an implementation of UCP and Scrum can be done by associate the User Stories on the Product Backlog with the Use Case on UCP and by modifying User Story attribute to Functionality and adding transaction attribute when defining the User Storie on Product Backlog.

Comparative analysis of soft computing techniques for predicting software effort based use case points

The size of a software project is a key measure of predicting software effort at the requirements and analysis phase. Use case points (UCP) is among software size metrics that achieved good reputation because of the increasing popularity of use case driven development methodologies in software industry. Nevertheless, there is no consistent method that can effectively translate the UCP into its corresponding effort. Previous estimation models were built using a very limited number of projects, and they were not well examined. The soft computing techniques were rarely applied for such problem and their performances have not been well investigated using a systematic procedure. This study looks into the accuracy and stability of some soft computing methods for the problem of effort estimation based on UCP. Four neural network methods, adaptive neuro fuzzy inference system and support vector regression have been used in this comparative study. The results suggest that most used soft computing techniques can work well with good accuracy for such problem. Among them, the general regression neural network is the superior one with stable ranking across different accuracy measures. Also, it has been found that using adjustment variables with basic UCP variables, solely or together, have positive impact on the accuracy and stability.