Software Effort Estimation Using Grey Relational Analysis

Padmaja, M.; Haritha, D.

doi:10.5815/ijitcs.2017.05.07

Cited by 12 publications

(12 citation statements)

References 12 publications

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“…Here we selected two methods, Grey Relational Analysis (GRA) and one of the Meta heuristic algorithm i.e., BAT Algorithm (BA) were applied on original data to estimate the effort. Description of GRA as explained in [8] and working of BA is explained in [16]. Then applied k-means clustering method on original data to divided into k number of clusters.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…It was applied on different data types and evaluated the results. In addition to this, M.Padmaja et al, [8] have used Grey Relational Analysis on Kemerer data set to estimate the effort. This model shows the minimum error rate on this data than other models.…”

Section: Padmaja D Harithamentioning

confidence: 99%

“…Grey Relational Analysis is included in the Grey System Theory and used to find out projects that are similar to a new project and constitute the similar parameters required to effort estimation or cost of a project. In GRA methodology, With Grey Relational Grades (GRG), know the relationship of current project with historical projects [5,8]. In order to estimate the effort of a project, first we find the required parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 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: Proposed Methodologymentioning

confidence: 99%

Section: Padmaja D Harithamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Heuristic Effort Estimation Method using BAT Algorithm through Clustering

Padmaja¹,

Haritha²

2019

IJITEE

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“…Genetic algorithms (GA) are one of the significant and potent optimization algorithms. It is heuristic natural algorithms which are applied for ruling correct and fairly accurate solutions [16], [17]. GA is focused on a number of feasible alternatives to the issue, which are improving iteratively.…”

Section: Introductionmentioning

confidence: 99%

Time of Development and Fitness Analysis of Modified COCOMOII Model for Software Projects

Sivakumar¹,

Sureshkumar²

2019

IJRTE

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In the management of software projects, software cost estimate is very important. The absence of precise and premature estimates is a significant source of the inability of many software initiatives. Nevertheless, it remains a challenge for the software industry to correctly estimate the time and the cost of development. It is used to predict how much effort and time the project needs. The precision of the technology proposal usually depends on the estimated time and the strength standard of the template. In this research analysis the time of development and model fitness are analyzed as a major contribution. The optimized coefficient values for A, B, C & D is used to estimate the time of development and model fitness. This experimental analysis proved that the time of development and model fitness analysis shows the great significance in the estimation and their influence in software cost estimation

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“…Geeta Nagpal proposed GRA method is combined with regression techniques to estimate the effort on different datasets [9]. 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].…”

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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Software Effort Estimation Using Grey Relational Analysis

Cited by 12 publications

References 12 publications

A Heuristic Effort Estimation Method using BAT Algorithm through Clustering

A Heuristic Effort Estimation Method using BAT Algorithm through Clustering

Time of Development and Fitness Analysis of Modified COCOMOII Model for Software Projects

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

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