- Dr. N.K. Goyal
Assistant Professor
Reliability Engineering Centre
IIT Kharagpur
Kharagpur, West Bengal
PIN - 721302 INDIA - 03222283990
Dr. Neeraj Goyal
IIT Kharagpur, Reliability Engineering Centre, Faculty Member
- Reliability Engineering, Software Reliability, Accelerated Life Testing, System Reliability, Probabilistic Risk Assessment, Fault Prone Software Module, and 13 moreSoftware Reliability Prediction, Network optimization, Software Metrics, Artificial Neural Networks, Software Fault Prediction, Early Software Reliability, Network Reliability, Software System Safety, Software FMEA, Network Layout Design, Capacitated Network, Communication network layout optimization, and Reliabilityedit
The recent paper published in IJPE by Tripathy et al. [1] presented a new method based on self-generating, non-redundant and disjoint cutsets to evaluate the three important reliability measures, viz., two-terminal, all-terminal and... more
The recent paper published in IJPE by Tripathy et al. [1] presented a new method based on self-generating, non-redundant and disjoint cutsets to evaluate the three important reliability measures, viz., two-terminal, all-terminal and k-terminal. Authors claim that their algorithm is much more efficient as it saves the overhead of disjointing process and redundant terms removal than the existing Sum-of-Disjoint-Product (SDP) form based algorithms available in the literature. However, we observe several discrepancies in the results generated by their proposed algorithm on the considered benchmark networks and even on the illustrative example taken to describe the algorithm.
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A neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is... more
A neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is encoded using Exponential and Logarithmic function in order to provide the encoded value as the input to the neural network. The effect of encoding and the effect of different encoding parameter on prediction accuracy have been studied. The effect of architecture of the neural network in terms of hidden nodes has also been studied. The performance of the proposed approach has been tested using eighteen software failure data sets. Numerical results show that the proposed approach is giving acceptable results across different software projects. The performance of the approach has been compared with some statistical models and statistical models with change point considering three datasets. The comparison results show that the proposed model has a good prediction capability.
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Software operational profile (SOP) has found its extensive use in applications such as estimation of software reliability, allocation of testing resources, evaluation of software quality attribute and testing of software etc. However, due... more
Software operational profile (SOP) has found its extensive use in applications such as estimation of software reliability, allocation of testing resources, evaluation of software quality attribute and testing of software etc. However, due to limited data resources and large efforts required to collect the data in to point estimates, reluctance is observed towards the development of SOP in spite of its numerous benefits. This paper proposes a model for the development of fuzzy software operational profile (FSOP), which takes input data in the form of linguistic variables from experts and assigns occurrence possibilities to the input parameters. The proposed method has been applied to various software and results obtained are compared with existing practice. Besides simplicity, FSOP can be easily altered for changes in operational frequencies simply by changing the location and value of the affected linguistic variable.
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This paper discusses a fault prediction model with specific focus on process level software metrics during software development. The model helps software development team to optimally allocate resources and achieve more reliable software... more
This paper discusses a fault prediction model with specific focus on process level software metrics during software development. The model helps software development team to optimally allocate resources and achieve more reliable software within the time and cost constraints. The ...