Radial basis function network using intuitionistic fuzzy C means for software cost estimation

Kaushik, Anupama; Soni, Ajay; Soni, Rachna

doi:10.1504/ijcat.2013.054305

Cited by 21 publications

(6 citation statements)

References 31 publications

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“…The model is a computing approach that is based on the degrees of truth rather than the unusual true or false normally referred to as Boolean logic which most of the modern computers are based on [31]. Additionally, it can be said to be an Journal of Software Engineering and Applications approach to computing based on many-valued function for instance, instead of a task completed or not, one can say 50% is completed.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…The first system is the one that is exploited in the estimation sector as most of the systems produce crisps data as input and expect the same type of data as output. There are three steps that have to be followed while using FL; the first is the Fuzzification which converts a crisp into a fuzzy set [31]. The second is the Fuzzy Rule-based System, at the step after all the crisp input has been fuzzified into their respective linguistic values the inference engine then derives their linguistic values [32].…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…Also, with the approach, it is advantageous as estimators of software are in a position to estimate and give an anticipation of the different aspects of the project even before the initiation level. The method has also a fast learning ability compared to other methods under the same scope [31]. The major disadvantage that the approach lacks guarantee of accuracy which is a vital part of the software cost estimation as to avoid incurring financial losses.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…There are a number of three applied approaches under this method which include; 2FA-kmodes it is used in clustering where numerical datasets are shown through fuzzy sets however the fuzz k-modes algorithm is used to maintain categorical attributes. The second approach is 2FA-kprototype it is used in clustering also where project data are clustered into homogeneous sets [31]. The final approach is the classical analogy it is used to predict the effort of the target project using information from former similar projects.…”

Section: Fuzzy Logicmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Software Cost Estimation Techniques

Chirra¹,

Reza²

2019

JSEA

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The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.

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Section: Fuzzy Logicmentioning

confidence: 99%

Section: Fuzzy Logicmentioning

confidence: 99%

Section: Fuzzy Logicmentioning

confidence: 99%

Section: Fuzzy Logicmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Software Cost Estimation Techniques

Chirra¹,

Reza²

2019

JSEA

View full text Add to dashboard Cite

show abstract

“…In fact, the prediction is used also for other purposes. As example, the prediction can address the cost (Kaushik et al, 2013), the fault proneness (Singh et al, 2014) or the level of severity faults (Singh et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

QM4MAS: a quality model for multi-agent systems

Marir

Mokhati

Bouchlaghem-Seridi

et al. 2016

IJCAT

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Multi-agent systems (MASs) are being increasingly used in complex and distributed applications development. Such applications should satisfy the requirements of users in terms of quality. Accordingly, it is important to assess the quality of such systems. In fact, several metrics have been proposed to assess different aspects of multi-agent systems. However, the lack of comprehensive quality model for multi-agent applications that combines the software's characteristics with the proposed metrics limits the usefulness of such metrics. In this paper, we propose an overall quality model for multi-agent-based software, called QM4MAS. An overall quality model gives a global view of the quality showing the relationships between its characteristics. The use of QM4MAS has two main objectives: 1) It allows defining and assessing the MAS quality; 2) it facilitates the maintenance of software product (high quality software is easier to maintain). The proposed model has been applied to JADE applications through a set of metrics. The assessment of JADE's proposed metrics can be done automatically using a tool we developed for this purpose. 298 T. Marir et al.

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Integrating firefly algorithm in artificial neural network models for accurate software cost predictions

Kaushik

Tayal

Yadav

et al. 2016

J Software Evolu Process

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Human effort is one of the main resources of software cost estimation. A successful software project development primarily relies on accurate effort prediction at an early stage of development. There are many effort prediction models in the literature. Deciding which model to choose is a challenge for the project managers. This paper investigates whether it is possible to improve the accuracy of software cost estimations by coupling firefly algorithm with the existing artificial neural network (ANN) models used in software cost predictions. The firefly algorithm is one of the recent evolutionary computing models inspired by the behaviour of fireflies in nature. This is compared with particle swarm optimization used already in literature for software cost estimations. The ANN models examined in this work include radial basis function network and functional link artificial neural networks models. The experimental results show that ANN models perform extremely well by incorporating firefly algorithm and intuitionistic fuzzy C-means for data preprocessing. The proposed approach is empirically validated through a statistical framework.All the software effort estimation studies are divided into two general classes, sparse data and many data methods. The sparse data methods require few or no historical data such as expert judgement techniques, where the estimate is produced based on judgmental processes of a software expert. The many data methods are based on available datasets and may be subdivided into parametric, nonparametric and semiparametric [4,5].Parametric models clearly describe the underlying relationship among parameters through an equation. Here, the predictors are linearly associated with the response variables. These models are applied initially for software cost estimation [6][7][8][9][10][11][12][13]. The non-parametric models are more robust. They do not depend on any specific data distribution and nor assume any fixed structure for a model. Thus, these models have many advantages then its parametric counterpart. The semiparametric models contain the merits of both parametric as well as non-parametric models. They are used especially in cases where there is less information on the relationships between the dependent and the independent variables. They can identify complex relations between variables for which there is no predetermined model structure or in cases where non-parametric model fails [5].This work focuses on non-parametric studies especially artificial neural network (ANN) models for software cost estimation. The ANN models used in our approach are functional link artificial neural networks (FLANN) and radial basis function network (RBFN). The reasons for choosing them are RBFN has the advantages of easy design, good generalization, strong tolerance to input noise and online learning ability [14], whereas FLANN also can beautifully handle the data, which are highly nonlinear in nature. It has faster convergence and has less computational complexity as is associated with multilayer neural net...

show abstract

Radial basis function network using intuitionistic fuzzy C means for software cost estimation

Cited by 21 publications

References 31 publications

A Survey on Software Cost Estimation Techniques

A Survey on Software Cost Estimation Techniques

QM4MAS: a quality model for multi-agent systems

Integrating firefly algorithm in artificial neural network models for accurate software cost predictions

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