Optimization of Software Testing for Discrete Testsuite using Genetic Algorithm and Sampling Technique

PrasadaTripathy, Siba; Kanhar, Devanand

doi:10.5120/10475-5207

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…A mutation would instead be the introduction of some kind of random noise or change to a single parameter value. A general scheme for an evolutionary algorithm is presented in Figure 26 [76].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Johansson¹

2017

Linköping Studies in Science and Technology. Dissertations

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The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model.Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data.In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discri...

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Johansson¹

2017

Linköping Studies in Science and Technology. Dissertations

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Software Source Code: Theoretical Analyzing and Practical Reviewing Model

Odeh¹

2021

TURCOMAT

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The high-quality software product is one of the most important goals of software engineering in general, source coding or implementation phase is one of the main steps of the software development process; when this phase is going in a good way and according to the source code quality standards and rules, the final product will be a high-quality product. This research focuses on the issues that can affect source code quality, such as errors, faults, defects, and failures; a practical system model will be provided (proposed) to predict some possible types of errors and defects and suggests some guidance and recommendations on source code error detection and analysis.

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Optimization of Test Case Generation using Genetic Algorithm (GA)

Mateen¹,

Nazir²,

Awan³

2016

IJCA

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Testing provides means pertaining to assuring software performance. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. However, associated with software testing has quite a few underlying concerns, which are very important and need to pay attention on these issues. These issues are effectively generating, prioritization of test cases, etc. These issues can be overcome by paying attention and focus. Solitary of the greatest Problems in the software testing area is usually how to acquire a great proper set associated with cases to confirm software. Some other strategies and also methodologies are proposed pertaining to shipping care of most of these issues. Genetic Algorithm (GA) belongs to evolutionary algorithms. Evolutionary algorithms have a significant role in the automatic test generation and many researchers are focusing on it. In this study explored software testing related issues by using the GA approach. In addition to right after applying some analysis, better solution produced, that is feasible and reliable. The particular research presents the implementation of GAs because of its generation of optimized test cases. Along these lines, this paper gives proficient system for the optimization of test case generation using genetic algorithm.Comment: 9 page

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Optimization of Software Testing for Discrete Testsuite using Genetic Algorithm and Sampling Technique

Cited by 4 publications

References 8 publications

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Software Source Code: Theoretical Analyzing and Practical Reviewing Model

Optimization of Test Case Generation using Genetic Algorithm (GA)

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