Integration of the grey relational analysis with genetic algorithm for software effort estimation

Huang, Sun-Jen; Chiu, Nan-Hsing; Chen, Liwei

doi:10.1016/j.ejor.2007.07.002

Cited by 186 publications

(125 citation statements)

References 18 publications

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“…Using five publicly available data sets, we have compared the proposed method with prediction models based on stepwise regression and Analogy, and when available, also with NW GRA [16], LW GRA [16], ANN [41,18], CART [46,18], GRA [18], and GP [10] methods. The results show that the proposed method outperformed 1) stepwise regression and Analogy in terms of MMRE, MdMRE, and PRED(25) for the majority (4 out of 5) of data sets we used; 2) other methods in terms of MMRE and PRED(25) excepting for a 4.2% PRED(25) smaller than GRA with one data set.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the use of the same data set and the same experimental method allow us to compare GRACE + with NW GRA [16], LW GRA [16], ANN [41,18], CART [46,18], GRA [18], and GP [10] methods. with the Albreht data set and the COCOMO81 data set.…”

Section: Investigationmentioning

confidence: 99%

“…Hsu et al [15,16] proposed an improved GRA model to enhance predicted results. Huang et al [18] integrated a genetic algorithm (GA) to the GRA. The GA method is adopted to find the best fit of weights for each software effort driver in the similarity measures.…”

Section: Related Workmentioning

confidence: 99%

“…It is a distinct similarity measurement that is different from traditionally distance measure [31]. Further, some studies [45,15,16,18] have shown that the GRA method performs acceptably in predicting software development effort. However, we found that these studies lay emphases on the different aspects of software project effort prediction and none of them consider noisy data problem.…”

Section: Introductionmentioning

confidence: 99%

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Predicting software project effort: A grey relational analysis based method

Song

Shepperd

2011

Expert Systems with Applications

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The inherent uncertainty of the software development process presents particular challenges for software effort prediction. We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data. However, in this paper, we particularly focus on outlier detection, feature subset selection, and effort prediction at an early stage of a project. We propose a novel approach of using Grey Relational Analysis (GRA) from Grey System Theory (GST), which is a recently developed system engineering theory based on the uncertainty of small samples. In this work we address some of the theoretical challenges in applying GRA to outlier detection, feature subset selection, and effort prediction, and then evaluate our approach on five publicly available industrial data sets using both stepwise regression and Analogy as benchmarks. The results are very encouraging in the sense of being comparable or better than other machine learning techniques and thus indicate that the method has considerable potential.

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Section: Discussionmentioning

confidence: 99%

Section: Investigationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting software project effort: A grey relational analysis based method

Song

Shepperd

2011

Expert Systems with Applications

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“…Sun-Jen Huang et., al [9] combined GRA with Genetic algorithms to estimate the effort on different datasets. In their work, the appropriate weights of the weighted GRG for each effort driver were prefixed to improve the accuracies of the software effort estimates.…”

Section: Related Workmentioning

confidence: 99%

Software Effort Estimation Using Grey Relational Analysis

Padmaja¹,

Haritha²

2017

IJITCS

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Abstract-Software effort estimation is the process of predicting the number of persons required to build a software system. Effort estimation is calculated in terms of person per month for the completion of a project. If any new project is launched into a market or in industry, then cost and effort of a new project will be estimated. In this context, a number of models have been proposed to construct the effort and cost estimation. Accurate software effort estimation is a challenge within the software industry. In this paper we propose a novel method, Grey Relational Analysis (GRA) to estimate the effort of a particular project. To estimate the effort of a project, traditional methods have been used as algorithmic models to evaluate the parameters of the basic model i.e. basic COCOMO model. In this paper, to show the minimum error rate we have used Grey Relational Analysis (GRA) to predict the effort estimation on Kemerer dataset. When compared to the traditional techniques for estimation, the proposed method proved better results. The efficiency of the proposed system is illustrated through experimental results.

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Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques

Idri

Amazal

Abran

2015

Int. J. Intell. Syst.

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Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FAkmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are quite diverse and have different sizes: ISBSG, COCOMO, USP05-FT, and USP05-RQ. The results obtained showed that both 2FA-kprototypes and 2FA-kmodes perform better than classical analogy. C 2015 Wiley Periodicals, Inc.

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Integration of the grey relational analysis with genetic algorithm for software effort estimation

Cited by 186 publications

References 18 publications

Predicting software project effort: A grey relational analysis based method

Predicting software project effort: A grey relational analysis based method

Software Effort Estimation Using Grey Relational Analysis

Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques

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