Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

Computer Aided Civil and Infrastructure Engineering

https://cyberleninka.org/article/n/1407659.pdf

Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

Models in Ecology

Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

/pdf/a-review-on-representative-swarm-intelligence-algorithms-for-4f005x62n2.pdf

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

A mathematical model of a prey-predator type fishery in the presence of toxicity with fuzzy optimal harvesting

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308141

Global stability and analysing the sensitivity of parameters of a multiple-susceptible population model of SARS-CoV-2 emphasising vaccination drive

In this paper, we study the dynamical properties of a discrete time prey predator model under imprecise biological parameters. We consider refuge for prey and cost of fear into prey reproduction. The equilibria of the model are obtained and the dynamic behaviors of the proposed system are examined. Hopf Bifurcation of the proposed prey predator model are analysis. Simulations of the model are performed for different parameters of the model. Numerical simulations show that the proposed discrete model exhibits rich dynamics of a chaotic and complex nature.

Fear effect on a discrete-time prey predator model with imprecise biological parameters

Software reliability models with imperfect debugging can characterize the quality of software fault removal, in addition to the rate of faults discovered. Change points are therefore useful to quantity such changes in the fault discovery rate. To address these issues, this paper develops a hazard rate model based on the widely studied Jelinski-Moranda model, introducing two additional parameters, namely an imperfect debugging parameter and a single change point parameters. We compare the proposed model with simpler models to show these additional parameters are justifiable. Both information theoretic and predictive measures of goodness of fit are applied to demonstrate these additional parameters are appropriate on some data sets.

A Single Change Point Hazard Rate Software Reliability Model with Imperfect Debugging

This paper presents an advanced ant colony optimisation approach to solve sub-tour constant travelling salesman problem (SCTSP) with time limit in fuzzy environment. In SCTSP, there is a set of sub-tours (two cities for each sub-tour). A traveller must complete his/her tour with predetermined sequence of distinct sub-tours. In this proposed model, the total time of travel must not exceed a predetermined time limit, i.e., the salesman should maintain a maximum time limit to complete his/her tour. In this paper, we present a modified form of the basic ACO algorithm, obtained by introducing a new operation known as restoring operation for solving the problem. In restoring operation, the low-quality path/solution given by any ant is replaced by a nearest better solution. The proposed problem is solved by considering fuzzy travel costs and time. Fuzzy credibility measure and graded mean integration method are used to obtain optimal decision. Computational results with different datasets are presented for illustration.

G. S. Mahapatra

Papers

A mathematical model of a prey-predator type fishery in the presence of toxicity with fuzzy optimal harvesting

Global stability and analysing the sensitivity of parameters of a multiple-susceptible population model of SARS-CoV-2 emphasising vaccination drive

Fear effect on a discrete-time prey predator model with imprecise biological parameters

A Single Change Point Hazard Rate Software Reliability Model with Imperfect Debugging

A modified ant colony optimisation based approach to solve sub-tour constant travelling salesman problem