Case Points Method Research Articles

Regression Analysis Based Software Effort Estimation Method

Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

Using improved analytical programming algorithm for effort estimation in software engineering

This paper evaluates the usage of improved analytical programming algorithm for software effort estimation. The new model was generated by improved analytical programming and it was tested and compared with Karner’s model to assess its properties. Least Absolute Deviation and random sub-sampling cross validation were used to assess the reliability to this experiment. The experimental results shows that the new model generated by analytical programming outperforms the Karner’s equation about 40 %. Moreover, this work shows that improved analytical programming algorithm is feasible method for calibrating Use Case Points method. All results were evaluated by standard approach: visual inspection and statistical significance testing.

Prediction accuracy measurements as a fitness function for software effort estimation.

This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The problem is in proper selection of the fitness function. Analytical programming and different fitness functions were tested to assess insight to this problem. Mean magnitude of relative error, prediction 25 %, mean squared error (MSE) and other metrics were as possible candidates for proper fitness function. The experimental results shows that means squared error performs best and therefore is recommended as a fitness function. Moreover, this work shows that analytical programming method is viable method for calibrating use case points method. All results were evaluated by standard approach: visual inspection and statistical significance.

Analysis of task effort estimation accuracy based on use case point size

The use case point (UCP) method is one of the most commonly used size estimation methods in software development. Applicability of UCP size for the project effort estimation is thoroughly investigated; however, little attention is devoted to the effort estimation of particular task types. The authors have created and cross-compared prediction models for estimating task-type efforts by means of UCP size using an Online analytical processing model and R packages on a set of 32 real-world projects, with the goal of facilitating analysis of the correlation between project sizes and effort required to complete task types. Requirements, scoping, functional specification, and functional testing task types have up to two times better estimation accuracies than project effort. Implementation has slightly better accuracy than the project effort, while the other task types are not correlated to the UCP size. Using estimates of the most correlated task types and other techniques, such as expert judgment for others, we improved the overall project effort prediction accuracy and decreased the error from 26 to 16%.

Algorithmic Optimisation Method for Improving Use Case Points Estimation.

This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance.

유스케이스 점수 측정의 신뢰도 향상을 위한 단위기능 중심의 유스케이스 정제 방법

Use The Use Case Points method is a software estimation method that is based on user requirements. When requirement analysts elicit user requirements, they obtain different use cases because different levels of detail are possible for the Use Case, and this affects the Use Case Points. In this paper, we suggest a method to refine the level of detail of the Use Case by using the concept of an elementary function. This refinement method achieves the desired reliability for the Use Case Points because it produces less of a deviation in the Use Case Points for different requirement analysts than other methods that are based on the step, transaction, and narrative of the Use Case.

Use Case Point Method of Software Effort Estimation: A Review

Software project management is one of the critical activities in software development process. Many software development projects fail due to poor management of projects. The main aim of software project management team is to count what is countable, measure what is measurable and prepare measurable parameters for continuously monitor and manage a software development project. One of the very important factors in proper management of a software project is software estimation. At present most of software projects developed are using object oriented technology for the development of software projects. Researchers across the globe have used object oriented technology based method known as use case point method for estimating efforts for a software project development. This research work is an effort to review different invariants of use case point method of effort estimation. The research work also highlights the importance of continuously refining the UCP method in order to reduce the gap between the estimated and actual efforts.

A quantitative and knowledge-based approach to choosing security architectural tactics

This paper presents a quantitative approach to choosing security architectural tactics using architectural tactic knowledge base. An architectural tactic is an architectural design building block pertaining to a software quality. The tactic knowledge base is a tactic repository composing of architectural tactic specifications defined in role based metamodelling language (RBML) and their relationships expressed in a feature model. In this paper, a cost of an architectural tactic is estimated by using the use case points method, and a level of tactic contribution for non–functional requirements (NFRs) is predicted by the analytic hierarchy process (AHP) and sensitivity analysis. Then, the proposed approach suggests the best possible fit which is likely to satisfy NFRs. We applied the approach to choosing security architectural tactics for building software architecture of an online trading system.

An Early Software Effort Estimation Method Based on Use Cases and Conceptual Classes

It is an important issue in the software industry to predict how much effort will be required for a software project as early as possible. Software size is one of the commonly used attributes in effort estimation. In this paper, we propose an early software size and effort estimation method based on conceptual model of the problem domain. Our method utilizes the noteworthy domain concepts identified mainly from the use cases written in the requirements phase of the software development lifecycle. In order to develop the model and evaluate its prediction quality, the use cases written and the effort data collected for 14 industrial software development projects of a CMMI level 3 certified defense industry company have been used. Evaluation results reveal a high correlation between the number of conceptual classes identified (i.e., domain objects) during the requirements analysis, the number of classes constituting the resulting software and the actual effort spent. Moreover, we have used the use case point (UCP) method to estimate the effort needed for each project and compared the results of UCP analysis with the results obtained with our method. The comparisons have shown that, for the projects considered, our method gives a better effort estimation compared to the effort estimated by using the UCP method.

Simplifying effort estimation based on Use Case Points

Context The Use Case Points (UCP) method can be used to estimate software development effort based on a use-case model and two sets of adjustment factors relating to the environmental and technical complexity of a project. The question arises whether all of these components are important from the effort estimation point of view. Objective This paper investigates the construction of UCP in order to find possible ways of simplifying it. Method The cross-validation procedure was used to compare the accuracy of the different variants of UCP (with and without the investigated simplifications). The analysis was based on data derived from a set of 14 projects for which effort ranged from 277 to 3593 man-hours. In addition, the factor analysis was performed to investigate the possibility of reducing the number of adjustment factors. Results The two variants of UCP – with and without unadjusted actor weights (UAW) provided similar prediction accuracy. In addition, a minor influence of the adjustment factors on the accuracy of UCP was observed. The results of the factor analysis indicated that the number of adjustment factors could be reduced from 21 to 6 (2 environmental factors and 4 technical complexity factors). Another observation was made that the variants of UCP calculated based on steps were slightly more accurate than the variants calculated based on transactions. Finally, a recently proposed use-case-based size metric TTPoints provided better accuracy than any of the investigated variants of UCP. Conclusion The observation in this study was that the UCP method could be simplified by rejecting UAW; calculating UCP based on steps instead of transactions; or just counting the total number of steps in use cases. Moreover, two recently proposed use-case-based size metrics Transactions and TTPoints could be used as an alternative to UCP to estimate effort at the early stages of software development.

Initial Hybrid Method for Analyzing Software Estimation, Benchmarking and Risk Assessment Using Design of Software

Problem statement: Estimation models in software engineering are used to predict some important attributes of future entities such as dev elopment effort, software reliability and programme rs productivity. Among these models, those estimating software effort have motivated considerable research in recent years. Approach: In this study we discussed an available work on th e effort estimation methods and also proposed a hybrid method for effort estimation process. As an initial approach to hybrid technology, we developed a simple approach to SEE based on case models: The use case point's method. This method is not n ew, but has not become popular although it is easy to understand and implement. We therefore investiga ted this promising method, which was inspired by function points analysis. Results: Reliable estimates can be calculated by using our method in a short time with the aid of a spreadsheet. Conclusion: We are planning to extend its applicability to est imate risk and benchmarking measures.

Use cases modeling and software estimation

This article discusses software size/effort estimation by the Use Case Points method (UCP). It is based on the experience accumulated during deployment of the UCP in a software development department of a major financial institution. Typical for such an environment is that software is developed on top of existing applications, and that there are significant differences between projects in business domains, scope, size, complexity, project management details, etc. In a previous article [3] an extension of the Use Case Points method, called UCPm, was described that addresses these issues.Due to their level, use cases make high-level estimation easier. However, this article, presenting a number of real world situations, demonstrates that to obtain reasonably accurate estimates we need to reflect in use case models some aspects of the existing application and of the current project. It also suggests some clarifications of the concept of use case transaction and outlines some ways to support use case models consistency within and across projects.

Software estimation in the maintenance context

This article describes an extension of the Use Case Points method of software estimation. The main goal of this extension, called UCPm, is to reflect the specifics of the maintenance phase of software life cycle. UCPm takes into consideration the complexity of the base system. Then, UCPm does not consider the environmental factor as size-contributing entity and defines product size only via unadjusted use case points and technical factor. UCPm also applies four technical factors at the use case level, rather than at the level of the overall product.The method has been applied to more than 30 projects in the course of work on achieving CMM Level 4. It was found that even when requirements are not produced in the use case style, it is relatively easy to build a use case model for the purpose of estimation. It is also believed that the relatively high level of UCP/UCPm reduces the amount of work on estimation. In our preliminary estimates, one use case point maps to approximately four function points.