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.

K-Means Clustering Algorithms to Compute Software Effort Estimation

2016 ◽  
Vol 13 (10) ◽  
pp. 7093-7098 ◽  
Author(s):  
Shivakumar Nagarajan ◽  
Balaji Narayanan
Keyword(s):  
Neural Network ◽  
Software Development ◽  
Clustering Algorithms ◽  
Uncertain Data ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Development Effort

Software development effort estimation is the way of predicting the effort to improve software economics. Accurate estimation of effort is the most tedious tasks in software projects. However, several methods are used to estimate the software development effort accurately. Imprecise estimation can leads to project failure due to uncertain data. In this paper, a hybrid model based on combination of Particle Swarm Optimization (PSO), K-means clustering algorithms, neural network and ABE method is proposed. The proposed method can be useful to predict better clustering and more accurate estimation and hence, there are difficulties in clustering and outliers in the software projects. The obtained results showed the better clustering result which provides the estimation result accurately. Then, neural network and Analogy methods are used which enhance the accuracy significantly.

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.

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.

Software Effort Estimation

2011 ◽  
Vol 7 (3) ◽  
pp. 41-53 ◽  
Author(s):  
Jeremiah D. Deng ◽  
Martin Purvis ◽  
Maryam Purvis
Keyword(s):  
Machine Learning ◽  
Software Development ◽  
Domain Knowledge ◽  
Modeling Processes ◽  
Machine Learning Algorithms ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often cannot achieve satisfactory results. This paper presents an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the necessity and effectiveness of the proposed approach.

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