Web software fault prediction under fuzzy environment using MODULO-M multivariate overlapping fuzzy clustering algorithm and newly proposed revised prediction algorithm

Chatterjee, Subhashish; Roy, Arunava

doi:10.1016/j.asoc.2014.03.030

Cited by 8 publications

(10 citation statements)

References 36 publications

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“…In this comparison, we consider one of the time series model, namely, single exponential smoothing, but it was not performing well compared to inverse 46 and noninverse fuzzy models. [59][60][61][62] Therefore, the proposed model outperforms the existing models for forecasting the UTM's enrollment from 1990 to 2008.…”

Section: Forecasting Enrollment Of University Technology Malaysia (Utm)mentioning

confidence: 89%

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Efendi

Deris

2018

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and noninverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules.

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Section: Forecasting Enrollment Of University Technology Malaysia (Utm)mentioning

confidence: 89%

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Efendi

Deris

2018

Int. J. Unc. Fuzz. Knowl. Based Syst.

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“…In this article, they have discussed the limitations of probabilistic software reliability growth models and identified the factors which can be considered as fuzzy number during the development of SRGMs. Later on, different researchers (Bastani 1985;Cai et al 2003;Chatterjee et al 2011aChatterjee et al , c, 2015Chatterjee and Roy 2014;Zeephongsekul and Xia 1996;Tyagi and Sharma 2012;Pizzi 2008) have proposed various SRGMs based on fuzzy set theory. Based on the discussion given in the previous paragraph and idea of Cai et al (1991) a fuzzy 'if-then' rule based new algorithm has been proposed in this article for software fault prediction in its early phase of life cycle, specifically in requirement phase.…”

Section: Introductionmentioning

confidence: 98%

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Chatterjee

Maji

2015

Soft Comput

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Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.

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“…A serious step for changing the testing procedure is based on the capacity of assessing the fault prediction process, evaluating the degree of a product module and then testing [4], [5]. Fault prediction before testing helps the developer to eliminate costs and fault detection after testing provides feedbacks for procedures of maintenance [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

et al. 2020

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By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies. INDEX TERMS Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization.

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Web software fault prediction under fuzzy environment using MODULO-M multivariate overlapping fuzzy clustering algorithm and newly proposed revised prediction algorithm

Cited by 8 publications

References 36 publications

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

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