Academic journal article American Academic & Scholarly Research Journal

Software Quality Assurance Development Using Bayesian Graphical Model and Safe Growth Model

Academic journal article American Academic & Scholarly Research Journal

Software Quality Assurance Development Using Bayesian Graphical Model and Safe Growth Model

Article excerpt

ABSTRACT: Software quality assurance is a planned and systematic approach to ensure that software processes and products confirms to the established standards, processes, and procedures. The goals of software quality assurance are to improve software quality by appropriately monitoring both software and the development process to ensure full compliance with the established standards and procedures. There are several models for software quality assurance, such as the ISO/IEC 90003, and the capability maturity model integration. As the software in today's systems grows larger, it has more defects, and these defects adversely affect the safety, security, and reliability of the systems. Software engineering is the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Quality is conformance to product requirements and should be free. This research concerns the role of software Quality. Software reliability is an important fact of software quality. It is the probability of failure-free operation of a computer program in a specified environment for a specified time. In software reliability modeling, the parameters of the model are typically estimated from the test data of the corresponding component. This research describes a new approach to the problem of software testing. The approach is based on Bayesian graphical models and presents formal mechanisms for the logical structuring of the software testing problem, the probabilistic and statistical treatment of the uncertainties to be addressed, the test design and analysis process, and the incorporation and implication of test results.

Keywords-: Bayesian Data Analysis, Probabilistic Reasoning, Software Testing, Program Analysis, Software Reliability, Uncertainty Analysis, Bayesian Graphical Model (BGM), Test Design, MEP, MC, TTF.

1 INTRODUCTION

The theory of BGMs has led to many new applications of uncertainty modeling, in particular, to complex problems where a large number of factors contribute to overall uncertainty. BGMs derive from Bayesian statistical methodology, which is characterized by providing a formal framework for the combination of data with the judgments of experts such as software testers. For the application area of software testing, we demonstrate how the problem should be structured and how the resulting models may be used. We illustrate the methodology with case studies arising from applying the approach to large scale software testing problems for a major UK company.

In software reliability modeling, the parameter of the model is typically estimated from the test data of the corresponding component. However, the widely used point estimators are subject to random variations in the data, resulting in uncertainties in these estimated parameters. Ignoring the parameter uncertainty can result in grossly underestimating the uncertainty in the total system reliability to apply the models for predicting the reliability of the component; the parameters of the models need to be known or estimated. Field data or data from components with similar functionalities are usually available to help estimate these parameters, but the estimators are subject to random variation because they are functions of random phenomena. Parameter uncertainty arises when the input parameters are unknown. Moreover, the reliability computed from the models, which are functions of these parameters, is not sufficiently precise when the parameters are uncertain.

2 RELATED WORK

Sofware reliability have attracted attention from statistical researchers. However, much of this work attempts to fit problems related to software reliability within existing mathematical frameworks rather than attempting carefully to model the actual uncertainties occuring in software testing and the process of learning from tests. We make case studies central to the development of the methods described in this paper as the emphasis of our approach is in modeling the actual testing process and thus contributing to better testing. …

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