scholarly journals Improving Effort Estimation by Voting Software Estimation Models

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Luiz Fernando Capretz ◽  
Venus Marza
Keyword(s):  
Estimation Accuracy ◽  
Data Repository ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Projects ◽  
Software Estimation ◽  
Software Development Effort ◽  
Commercial Purpose ◽  
Estimation Models

Estimating software development effort is an important task in the management of large software projects. The task is challenging, and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel models to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. Empirical validation of this approach uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement in software estimation accuracy.

SOFTWARE DEVELOPMENT EFFORT ESTIMATION USING CLASSICAL AND FUZZY ANALOGY: A CROSS-VALIDATION COMPARATIVE STUDY

2014 ◽  
Vol 13 (03) ◽  
pp. 1450013 ◽  
Author(s):  
FATIMA AZZAHRA AMAZAL ◽  
ALI IDRI ◽  
ALAIN ABRAN
Keyword(s):  
Software Development ◽  
Case Based Reasoning ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Development Effort ◽  
Research Questions ◽  
Estimation Models

Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models.

The Application of Meta-Heuristic Algorithms to Improve the Performance of Software Development Effort Estimation Models

2015 ◽  
Vol 6 (4) ◽  
pp. 39-68 ◽  
Author(s):  
Maryam Hassani Saadi ◽  
Vahid Khatibi Bardsiri ◽  
Fahimeh Ziaaddini
Keyword(s):  
Software Development ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Development Effort ◽  
Special Role ◽  
Effort Estimation ◽  
Software Projects ◽  
Software Development Effort ◽  
Estimation Models ◽  
Software Development Effort Estimation

One of the major activities in effective and efficient production of software projects is the precise estimation of software development effort. Estimation of the effort in primary steps of software development is one of the most important challenges in managing software projects. Some reasons for these challenges such as: discordant software projects, the complexity of the manufacturing process, special role of human and high level of obscure and unusual features of software projects can be noted. Predicting the necessary efforts to develop software using meta-heuristic optimization algorithms has made significant progressions in this field. These algorithms have the potent to be used in estimation of the effort of the software. The necessity to increase estimation precision urged the authors to survey the efficiency of some meta-heuristic optimization algorithms and their effects on the software projects. To do so, in this paper, they investigated the effect of combining various optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and ant colony algorithm on different models such as COCOMO, estimation based on analogy, machine learning methods and standard estimation models. These models have employed various data sets to evaluate the results such as COCOMO, Desharnais, NASA, Kemerer, CF, DPS, ISBSG and Koten & Gary. The results of this survey can be used by researchers as a primary reference.

scholarly journals Applying COCOMO II for a DO-178C Safety-Critical Software Effort Estimation

2019 ◽  
Author(s):  
Lucas Pereira dos Santos ◽  
Maurício Ferreira
Keyword(s):  
Estimation Accuracy ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Application ◽  
Safety Critical ◽  
Software Development Effort ◽  
Software Practitioners ◽  
Cocomo Ii

This paper provides a real example of applying COCOMO II as an estimation technique for the required software development effort in a safety-critical software application project following the DO-178C processes. The main goal and contribution of the case study is to support the research on software effort estimation and to provide software practitioners with useful data based on a real project. We applied the method as it is, by correlating the effort multiplier factors with the complexity and objectives introduced by the DO-178C level A application, resulting in an estimated effort. The rationales for each scale factor and effort multiplier selection were also described in detail. By comparing the estimated values with the actual required data, we found a magnitude of relative error (MRE) of 40% and provided alternatives for future work in order to increase the effort estimation accuracy in safety-critical software projects.

scholarly journals Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy

2019 ◽  
Vol 29 (1) ◽  
pp. 1468-1479
Author(s):  
V Resmi ◽  
S Vijayalakshmi
Keyword(s):  
Prediction Accuracy ◽  
Project Manager ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Soft Computing Techniques ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Abstract In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy.

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.

Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

2022 ◽  
pp. 1652-1665
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakashima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Software Development ◽  
Embedded Software ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Development Projects ◽  
Effort Estimation ◽  
Software Projects ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann ◽  
Estimation Models

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

Sign in / Sign up

Export Citation Format

Share Document