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Journal ArticleDOI

On using soft computing techniques in software reliability engineering

TL;DR: This paper considers soft computing techniques in order to be used for software fault diagnosis, reliability optimization and for time series prediction during the software reliability analysis.
Abstract: Previous investigations have shown the importance of evaluating computer performances and predicting the system reliability. This paper considers soft computing techniques in order to be used for software fault diagnosis, reliability optimization and for time series prediction during the software reliability analysis. It is shown that the study of the data collections during a software project development can be done within a soft computing framework.

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Citations
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Journal ArticleDOI
TL;DR: In this article, ensemble models are developed to accurately forecast software reliability, including statistical (multiple linear regression and multivariate adaptive regression splines) and intelligent techniques (backpropagation trained neural network, dynamic evolving neuro-fuzzy inference system and TreeNet).

120 citations

Journal Article
TL;DR: Analyze and explore about the actuality of data mining and the trend of knowledge discovery system, and presents new conception on the con non of general framework and specific Problem.
Abstract: Analyze and explore about the actuality of data mining and the trend of knowledge discovery system, and presents new conception on the con non of general framework and specific Problem.

94 citations

Proceedings ArticleDOI
26 Sep 2008
TL;DR: This paper proposes a framework named ASD-DM based on Adaptive Software Development (ASD) that can easily adapt with predictive data mining applications and a case study in automotive manufacturing domain was explained and experimented to evaluate ASD- DM methodology.
Abstract: As the world becomes increasingly dynamic, the traditional static modeling may not be able to deal with it. One solution is to use agile modeling that is characterized with flexibility and adaptability. On the other hand, data mining applications require greater diversity of technology, business skills, and knowledge than the typical applications, which means it may benefit a lot from features of agile software development. In this paper, we will propose a framework named ASD-DM based on Adaptive Software Development (ASD) that can easily adapt with predictive data mining applications. A case study in automotive manufacturing domain was explained and experimented to evaluate ASD-DM methodology.

27 citations

Book ChapterDOI
TL;DR: It was observed that ANFIS yields better results and it predicts the reliability more accurately and precisely as compared to all the above-mentioned techniques and comparative analysis between cumulative failure data and inter failure time data found that cumulative failureData give better and more promising results asCompared to inter failureTime data.
Abstract: Software reliability is an indispensable part of software quality. Software industry endures various challenges in developing highly reliable software. Application of machine learning (ML) techniques for software reliability prediction has shown meticulous and remarkable results. In this paper, we propose the use of machine learning techniques for software reliability prediction and evaluate them based on selected performance criteria. We have applied ML techniques including adaptive neuro fuzzy inference system (ANFIS), feed forward backpropagation neural network (FFBPNN), general regression neural network (GRNN), support vector machines (SVM), multilayer perceptron (MLP), bagging, cascading forward backpropagation neural network (CFBPNN), instance-based learning (IBK), linear regression (Lin Reg), M5P, reduced error pruning tree (reptree), and M5Rules to predict the software reliability on various datasets being chosen from industrial software. Based on the experiments conducted, it was observed that ANFIS yields better results and it predicts the reliability more accurately and precisely as compared to all the above-mentioned techniques. In this study, we also made comparative analysis between cumulative failure data and inter failure time data and found that cumulative failure data give better and more promising results as compared to inter failure time data.

26 citations

Journal ArticleDOI
TL;DR: Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models.
Abstract: We propose ANN based logistic growth curve model (LGCM) of software reliability.We propose neuro-genetic approach for ANN based LGCM by optimizing ANN using GA.Proposed model is compared with NHPP and ANN based software reliability models.ANN based LGCM has better fitting and predictive capability than other models.If GA is applied to train ANN based LGCM, it will give upmost prediction accuracy. In this paper, we propose a multi-layer feedforward artificial neural network (ANN) based logistic growth curve model (LGCM) for software reliability estimation and prediction. We develop the ANN by designing different activation functions for the hidden layer neurons of the network. We explain the ANN from the mathematical viewpoint of logistic growth curve modeling for software reliability. We also propose a neuro-genetic approach for the ANN based LGCM by optimizing the weights of the network using proposed genetic algorithm (GA). We first train the ANN using back-propagation algorithm (BPA) to predict software reliability. After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network. The proposed ANN based LGCM is compared with the traditional Non-homogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) and ANN based software reliability models. We present the comparison between the two training algorithms when they are applied to train the proposed ANN to predict software reliability. The applicability of the different approaches is explained through three real software failure data sets. Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models. It is also noted that when the proposed GA is employed as the learning algorithm to the ANN, the proposed ANN based LGCM gives more fitting and prediction accuracy i.e. the proposed neuro-genetic approach to the LGCM provides utmost predictive validity. Proposed model can be applied during software testing time to get better software reliability estimation and prediction than the other traditional NHPP and ANN based software reliability models.

25 citations

References
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Book
01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

52,705 citations

Journal ArticleDOI
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.

18,794 citations

Journal ArticleDOI
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.

14,937 citations

Book
01 Jan 1976
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Abstract: Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combination, this book constructs a new theory of epistemic probability. The theory draws on the work of A. P. Dempster but diverges from Depster's viewpoint by identifying his "lower probabilities" as epistemic probabilities and taking his rule for combining "upper and lower probabilities" as fundamental. The book opens with a critique of the well-known Bayesian theory of epistemic probability. It then proceeds to develop an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions. This rule, together with the idea of "weights of evidence," leads to both an extensive new theory and a better understanding of the Bayesian theory. The book concludes with a brief treatment of statistical inference and a discussion of the limitations of epistemic probability. Appendices contain mathematical proofs, which are relatively elementary and seldom depend on mathematics more advanced that the binomial theorem.

14,565 citations

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Can a software developer become data analyst?

It is shown that the study of the data collections during a software project development can be done within a soft computing framework.