SUMMARY

This research paper presents a framework for ranking of software engineering metrics based on expert opinion elicitation and fuzzy-based matrix methodology. The proposed methodology is able to accommodate the imprecise and inexact data involved in the problem of ranking of software engineering metrics, vagueness and ambiguity occurring during expert (human) decision making and to depart from the complexity of formulation of the objective and the constraint function. The matrices lend themselves to mechanical manipulations and are useful for analyzing and deriving systems functions expeditiously to meet the objectives. The current research is based on software engineering metrics identified in an earlier study conducted by Lawrence Livermore National Laboratory. A set of ranking criteria were identified. Software engineering metrics are then ranked in ascending order using experts' opinion in accordance with the value of Permanent function on their criteria matrix. The proposed methodology has also been compared with other known methodologies. Copyright © 2011 John Wiley & Sons, Ltd.

Ranking of software engineering metrics by fuzzy‐based matrix methodology

To obtained solutions to problems quickly, accurately and acceptably, a large number of soft computing techniques have been developed, but it is very difficult to find out which one is the most suitable and can be used globally. In this paper, we have provided an overview of existing soft computing techniques, and then critically analyzed the work done by the various researchers in the field of software reliability. The probability of failure-free operation of a software system for a specified time in a specifies environment. Further to this, we have also compared soft computing techniques in terms of software reliability modeling capabilities.

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Software Reliability Modeling using Soft Computing Techniques: Critical Review

A comparative study of statistical and soft computing techniques for reliability prediction of automotive manufacturing

Reliability of software is an essential concern for users for a long time. Software reliability is mainly obtained through modeling and estimating. There are numerous methods for reducing the failure rate. However, the existing methods are nonlinear. Hence the parameter estimation of these methods is difficult. This paper concerns on estimation and prediction of software reliability through different soft computing methods for improving the reliability of software. For estimation and prediction, the authors of this paper take two soft computing methodologies, including fuzzy logic and neural network. The outcomes seem to give satisfactory results on large datasets. For experiments, this paper is using two different large datasets of Apache server and MyLyn application software for showing the effectiveness of the results. The proposed methods of prediction would be useful for practitioners to simplify the procedures during software development in large datasets for reducing failures of software.

https://research-publication.com/wp-content/uploads/2021/vol-10-n01/AMSJ-2021-N01-54.pdf

Predicting software bugs of newly and large datasets through a unified neuro-fuzzy approach: reliability perspective

There is non-linear relationship between software metrics and defects, which results to a complex mapping. Therefore, to focus on the defect density area, it is a critical business requirement of effective and practical approach, which can help find the defect density in software releases. Soft computing provides a better platform to solve the non-linear and complex mapping problem. The aim of this paper is to formulate, build, evaluate, validate and compare two main sections of soft computing, fuzzy logic and artificial neural network approaches in prediction of defect density of subsequent software product releases. In this research, these two approaches are formulated and applied to predict the existence of a defect in file of software release. Both approaches have also been validated against various releases of two commercial software product release data sets. The validation criteria include mean absolute error, root mean square error and graphical analysis. The analysis of the study shows that artificial neural network provides better results compared to Fuzzy Inference System; but applicability of best approach depends on the data availability and the quantum of data.

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Applying Soft Computing Approaches to Predict Defect Density in Software Product Releases: An Empirical Study

Recent Advancements for Improving the Performance of Triboelectric Nanogenerator Devices

Given the huge economic burden caused by chronic and acute diseases on human beings, it is an urgent requirement of a cost-effective diagnosis and monitoring process to treat and cure the disease in their preliminary stage to avoid severe complications. Wearable biosensors have been developed by using numerous materials for non-invasive, wireless, and consistent human health monitoring. Graphene, a 2D nanomaterial, has received considerable attention for the development of wearable biosensors due to its outstanding physical, chemical, and structural properties. Moreover, the extremely flexible, foldable, and biocompatible nature of graphene provide a wide scope for developing wearable biosensor devices. Therefore, graphene and its derivatives could be trending materials to fabricate wearable biosensor devices for remote human health management in the near future. Various biofluids and exhaled breath contain many relevant biomarkers which can be exploited by wearable biosensors non-invasively to identify diseases. In this article, we have discussed various methodologies and strategies for synthesizing and pattering graphene. Furthermore, general sensing mechanism of biosensors, and graphene-based biosensing devices for tear, sweat, interstitial fluid (ISF), saliva, and exhaled breath have also been explored and discussed thoroughly. Finally, current challenges and future prospective of graphene-based wearable biosensors have been evaluated with conclusion. Graphene is a promising 2D material for the development of wearable sensors. Various biofluids (sweat, tears, saliva and ISF) and exhaled breath contains many relevant biomarkers which facilitate in identify diseases. Biosensor is made up of biological recognition element such as enzyme, antibody, nucleic acid, hormone, organelle, or complete cell and physical (transducer, amplifier), provide fast response without causing organ harm. 

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Advanced wearable biosensors for the detection of body fluids and exhaled breath by graphene

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In this paper, an overviews of different application areas are discussed, where the Artificial Bee Colony (ABC) optimization algorithm has applied. Artificial bee colony was introduced by Karabora in 2005. It was developed to solve real parameter optimization problem. BCO's (Bee Colony Optimization) foraging behavior is simulated in artificial bee colony. This system portraying organized team-work well coordinated interaction, labor division, simultaneous task performance and well-knit communication.

Kuldeep Singh Kaswan

Papers

Recent Advancements for Improving the Performance of Triboelectric Nanogenerator Devices

Software Reliability Modeling using Soft Computing Techniques: Critical Review

Advanced wearable biosensors for the detection of body fluids and exhaled breath by graphene

Software Reliability Modeling using Soft Computing Techniques: Critical Review

Applications of Artificial Bee Colony Optimization technique: Survey