Software Effort Estimation Using Grey Relational Analysis with K-Means Clustering

Padmaja, M.; Haritha, D.

doi:10.1007/978-981-10-7512-4_92

Cited by 7 publications

(12 citation statements)

References 5 publications

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“…It can be observed that, for these criteria, BAT algorithm (BA) works with minimum error as in MMRE, MdMRE 0.213183, 0.171727 respectively and maximum PRED as 0.7854 on reduced data with clustering than GRA are as prescribed in Figure 3. The results with GRA method was shown in [9], compare the results in all considerations with BAT algorithm (BA) in this paper. Finally, the BA was proved to show better results than GRA.…”

Section: Fig 2 Efficiency Of Mmre With Anticipated Modelmentioning

confidence: 92%

“…In the data set every project has more number of parameters so, only influenced parameters should be selected. To achieve this, ANOVA and PCC methods were selected and the procedure of these methods is described in [9]. By using these methods essential parameters were taken which are eight in addition to the actual effort: rely, data, turn, acap, aexp, pcap, lexp and modp.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…To do this, they used ANOVA and PCC methods to select suitable parameters. M.Padmaja et al, [9] applied GRA method on COCOMO81 dataset with k-means clustering. In this, suitable parameters with ANOVA and PCC methods were selected, and this sufficient data is known as reduced data.…”

Section: Padmaja D Harithamentioning

confidence: 99%

“…One-way ANOVA method and Pearson correlation coefficient (PCC) methods are also used to find influential features. From these methods, most influenced parameters such as rely, data, turn, acap, aexp, pcap, lexp, modp and effort were considered to estimate the effort [9]. Those required parameters on same number of projects are known as reduced data.…”

Section: Experimental Analysismentioning

confidence: 99%

See 3 more Smart Citations

A Heuristic Effort Estimation Method using BAT Algorithm through Clustering

Padmaja¹,

Haritha²

2019

IJITEE

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The most challenging task within software industries or organizations is project handling. Effort estimation has to be made accurately, before handling the project in order to win the bidding among several competitions. To estimate this effort an efficient method has to be selected. In this paper, we propose two novel methods: Grey Relational Analysis (GRA) method and Meta heuristic approach as BAT algorithm (BA) for predicting the effort of a specific project. These two methods are applied on suitable parameters or features that are obtained through one-way ANOVA and Pearson Correlation Coefficient (PCC) methods to forming a reduced data. The experimentation of this paper proves the efficiency of the anticipated method. The proposed method is applied over original data set and dimensionally reduced data set which are compared and tabulated. From the experimental results it is evident that the proposed method produced less error and more prediction.

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Section: Fig 2 Efficiency Of Mmre With Anticipated Modelmentioning

confidence: 92%

Section: Proposed Methodologymentioning

confidence: 99%

Section: Padmaja D Harithamentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Heuristic Effort Estimation Method using BAT Algorithm through Clustering

Padmaja¹,

Haritha²

2019

IJITEE

Self Cite

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“…M.Padmaja was used GRA method to estimate the effort and compare the results with existing methods [10]. This work extended using k-means clustering method to divide into clusters and applied GRA to find the effort estimation [20]. Padmaja was used BAT algorithm to estimate the effort and compare and evaluated with other models [19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Process Parameters Using GreyTaguchi Method for Software Effort Estimation of Software Project

Padmaja¹,

Haritha²

2018

IJIGSP

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Optimization is one of the techniques used in the estimation of projects to obtain the optimal parameter sequence at different levels for the best project conditions, such as size, duration and function points. In this paper, to select the significant process parameter sequence at different levels, a combination of Grey Relational Analysis (GRA) and Taguchi method applied during the estimation. This parameter sequence is essential for the industries in producing quality product at a lower cost. Taguchi method is used to improve the product quality and reduce the cost. Among the various methods of Taguchi as a standard Orthogonal Array (OA) produces better parameters to be considered at different levels. This paper uses L16 Orthogonal Array (OA) whose efficiency is proven in the experimental results. Here, a variant of GRA, GRG has been used to assign grades for projects in the dataset. Finally, the optimized process parameter sequence at different levels is obtained through the application of GRG over L16 Orthogonal Array (OA). In this paper, Grey-Taguchi method is implemented to find out the levels of software process parameters such as Duration, KSLOC, Adjustment Function Points and Raw Function Points necessary for minimizing software effort. Experimental results show that parameter levels suggested by Grey-Taguchi method result in improved GRG, which results in better software effort estimation.

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A soft computing approach for software defect density prediction

Azzeh

Alqasrawi

Elsheikh

2023

J Software Evolu Process

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Defect density is an essential software testing and maintenance aspect that determines the quality of software products. It is used as a management factor to distribute limited human resources successfully. The availability of public defect datasets facilitates building defect density prediction models using established static code metrics. Since the data gathered for software modules are often subject to uncertainty, it becomes difficult to deliver accurate and reliable predictions. To alleviate this issue, we propose a new prediction model that integrates gray system theory and fuzzy logic to handle the imprecision in software measurement. We propose a new similarity measure that combines the benefits of fuzzy logic and gray relational analysis. The proposed model was validated against defect density prediction models using public defect datasets. The defect density variable is frequently sparse because of the vast number of none‐defected modules in the datasets. Therefore, we also check our proposed model's performance against the sparsity level. The findings reveal that the developed model surpasses other defect density prediction models over the datasets with high and very high sparsity ratios. The ensemble learning techniques are competitive choices to the proposed model when the sparsity ratio is relatively small. On the other hand, the statistical regression models were the most inadequate methods for such problems and datasets. Finally, the proposed model was evaluated against different degrees of uncertainty using a sensitivity analysis procedure. The results showed that our model behaves stably under different degrees of uncertainty.

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Software Effort Estimation Using Grey Relational Analysis with K-Means Clustering

Cited by 7 publications

References 5 publications

A Heuristic Effort Estimation Method using BAT Algorithm through Clustering

A Heuristic Effort Estimation Method using BAT Algorithm through Clustering

Optimization of Process Parameters Using GreyTaguchi Method for Software Effort Estimation of Software Project

A soft computing approach for software defect density prediction

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