TESTABILITY OF INFORMATION LEAK IN THE SOURCE CODE FOR INDEPENDENT TEST ORGANIZATION BY USING BACK PROPAGATION ALGORITHM

Abstract

A strategy for software testing integrates the design of software test cases into a wellplanned series of steps that results in a successful development of the software security. The strategy provides the secure source code test by Independent Test Organization (ITO) that describes the steps to be taken, when, and how much effort, time, and resources will be required. The strategy incorporates test planning, test case design, test execution, test result collection and test leak information and evaluation. In this work we speak about the testability of leak information in source code and how to detect and protect it inside the ITO. In this paper we present a privacy preserving algorithm for the neural network learning to detect and protect the leak information in source code between two parties the programmer (source code) and Independent Test Organization (Sensor). We show that our algorithm is very secure and the sensor inside Independent Test Organization is able to detect and protect all leaks information inside the source code. We demonstrate the efficiency of our algorithm by experiments on real world data. We present new technology for software Security using Back Propagation algorithm. That is embedded sensor to analyze the source code inside the ITO. By using embedded sensor we can detect and protect in real time all the attacks or leaks of information inside the source code. The connection between an Artificial Neural Networks and source code analysis inside Independent Test Organization is providing a great help for the software security.