scholarly journals Effort Estimation Approach through Extracting Use Cases via Informal Requirement Specifications

2020 ◽  
Vol 10 (9) ◽  
pp. 3044 ◽  
Author(s):  
Bo Kyung Park ◽  
R. Young Chul Kim
Keyword(s):  
Semantic Analysis ◽  
Requirement Engineering ◽  
Use Cases ◽  
Software Systems ◽  
Software Project ◽  
Use Case ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Textual Approach ◽  
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.

scholarly journals Using Actors and Use Cases for Software Size Estimation

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 592
Author(s):  
Radek Silhavy ◽  
Petr Silhavy ◽  
Zdenka Prokopova
Keyword(s):  
Project Planning ◽  
Use Cases ◽  
New Method ◽  
Software Systems ◽  
Software Project ◽  
Use Case ◽  
Size Estimation ◽  
Software Size Estimation ◽  
Use Case Points

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method.

Use case repository framework based on machine learning algorithm to analyze the software development estimation with intelligent information systems

2019 ◽  
Vol 18 (01) ◽  
pp. 1941007
Author(s):  
R. Lalitha ◽  
B. Latha ◽  
G. Sumathi
Keyword(s):  
Machine Learning ◽  
Software Development ◽  
Reference Model ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Use Cases ◽  
Software Project ◽  
Use Case ◽  
Effort Estimation ◽  
Software Estimation

The success of information system process depends on accuracy of software estimation. Estimation is done at initial phase of software development. It requires a collection of all relevant required information for estimating the software effort. In this paper, a methodology is proposed to maintain a knowledgeable use case repository to store the use cases of various projects in several software project-related domains. This acts as a reference model to compare similar use cases of similar types of projects. The use case points are calculated and using this, schedule estimation and effort estimation of a project are calculated using the formulas of software engineering. These values are compared with the estimated effort and scheduled effort of a new project under development. Apart from these, the effective machine learning technique called neural network is used to measure how accurately the information is processed by use of case repository framework. The proposed machine learning-based use case repository system helps to estimate and analyze the effort using the machine learning algorithms.

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

scholarly journals Influence of end user development on software project estimation

2018 ◽  
Vol 7 (3) ◽  
pp. 1812
Author(s):  
Archana Srivastava ◽  
Dr. K. Singh ◽  
Dr Syed Qamar Abbas
Keyword(s):  
User Satisfaction ◽  
Point Method ◽  
Development Effort ◽  
Software Project ◽  
Use Case ◽  
Effort Estimation ◽  
End User ◽  
Software Development Effort ◽  
Project Estimation

Use Case Point Method (UCP) is used to estimate software development effort. UCP uses a project’s use cases to produce a reasonable estimate of a project’s complexity and required man hours. Advance Use Case Point Method (AUCP) is an extension of UCP. AUCP extends UCP by adding the additional effort required in incorporating end user development (EUD) features in the software for overall project effort estimation. Today user needs are diverse, complex, and frequently changing hence need of EUD is also increasing. EUD features if incorporated in the software increases end user satisfaction exponentially but incorporating EUD features increases design time complexity and increases the effort significantly based on the end users requirements. This paper provides a case study to demonstrate the comparative analysis of UCP and AUCP using paired t-test. It also observes that there can be on an average 20% increase in overall effort of development on adding EUD features.  

Regression Analysis Based Software Effort Estimation Method

2016 ◽  
Vol 26 (05) ◽  
pp. 807-826 ◽  
Author(s):  
Fatih Yücalar ◽  
Deniz Kilinc ◽  
Emin Borandag ◽  
Akin Ozcift
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation

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.

A Framework for Understanding and Addressing the Semiotic Quality of Use Case Models

2009 ◽  
pp. 327-351 ◽  
Author(s):  
Pankaj Kamthan
Keyword(s):  
Use Cases ◽  
Software Systems ◽  
Use Case ◽  
Use Case Models

As software systems become ever more interactive, there is a need to model the services they provide to users, and use cases are one abstract way of doing that. As use cases models become pervasive, the question of their communicability to stakeholders arises. In this chapter, we propose a semiotic framework for understanding and systematically addressing the quality of use case models. The quality concerns at each semiotic level are discussed and process- and product-oriented means to address them in a feasible manner are presented. The scope and limitations of the framework, including that of the means, are given. The need for more emphasis on prevention over cure in improving the quality of use case models is emphasized. The ideas explored are illustrated by examples.

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