Predictive accuracy comparison of fuzzy models for software development effort of small programs

López-Martín, Cuauhtémoc; Yáñez-Márquez, Cornelio; Gutierrez-Tornes, Agustin

doi:10.1016/j.jss.2007.08.027

Cited by 35 publications

(28 citation statements)

References 33 publications

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“…The estimations are conducted with the use of generalized fuzzy number operations and the effort of a project is estimated as a fuzzy number which is defuzzified with the method of center of gravity. Lopez-Martin et al [13] compare three personal fuzzy logic models to estimate the effort of small software programs, namely triangular, trapezoidal and Gaussian membership functions, with linear regression model. They develop the fuzzy logic and linear regression models using the data gathered from 105 small programs, and then the estimations generated by these models are compared with each other using 20 small programs.…”

Section: Literature Reviewmentioning

confidence: 99%

Software Effort Estimation Using Adaptive Fuzzy-Neural Approach

Mehdi

2017

IJCATR

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Abstract-Software effort estimation is an important step in software development. It predicts the amount of effort and development time required to build a software system. It is one of the most important tasks and an accurate estimate is vital to the successful completion of the project. Building software effort estimation requires developing sound computational models. This paper investigates the use of fuzzy-neural systems in estimating software effort. A comparison is made with a radial basis neural network. Results obtained based on the China dataset indicates that a hybrid model that combine fuzzy inferencing with neural networks ability to learn from examples provided more accurate results than using neural networks alone.

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Section: Literature Reviewmentioning

confidence: 99%

Software Effort Estimation Using Adaptive Fuzzy-Neural Approach

Mehdi

2017

IJCATR

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“…Lopez-Martín (2011a,b) and Lopez-Martín et al (2008) created regression models from short scale programs and from ISBSG repository. The authors also developed fuzzy logic and neural network models such as Feed-Forward and General Regression Neural Networks.…”

Section: Related Workmentioning

confidence: 99%

“…References (Ahmed et al, 2005;Papatheocharous et al, 2010) used fuzzy logic and fuzzy decision tree, respectively for software effort estimation. Other works such as (Heiat, 2002;Lopez-Martín, 2011a,b;Lopez-Martín et al, 2008 developed neural network models and compared their works with regression models. Regression models such as linear, nonlinear, stepwise and ridge have been used to predict software effort as shown in (Jiang et al, 2007;Xia et al, 2008;Tan et al, 2009;Li et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Nassif

Ho²,

Capretz

2013

Journal of Systems and Software

177

160

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tSoftware estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effort based on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects.

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“…Among several current approaches, the Neurofuzzy technique is a promising strategy that solves several problems involving evaluation accuracy [18,19]. This approach is a suitable due to its built-in learning capacity, its robustness in the case of uncertain input, and its ability to utilize a variety of information from multiple sources.…”

Section: Neurofuzzy Modelmentioning

confidence: 99%

Improving Effort Estimation by Voting Software Estimation Models

Capretz

Marza

2009

Advances in Software Engineering

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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.

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Predictive accuracy comparison of fuzzy models for software development effort of small programs

Cited by 35 publications

References 33 publications

Software Effort Estimation Using Adaptive Fuzzy-Neural Approach

Software Effort Estimation Using Adaptive Fuzzy-Neural Approach

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Improving Effort Estimation by Voting Software Estimation Models

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