Improving black box testing by using neuro-fuzzy classifiers and multi-agent systems

Automated software testing has become a fundamental requirement for several software engineering methodologies. Software development companies very often outsource the test of their products. In such cases, the hired companies sometimes have to test softwares without any access to the source code. This type of service is called black box testing, which includes presentation of some ad-hoc input to the software followed by an assessment of the outcome. The common place for black box testing is sequential approach and slow pace of work. This ineffectiveness is due to the combinatorial explosion of software parameters and payloads. This work presents a neuro-fuzzy and multi-agent system architecture for improving black box testing tools for client-side vulnerability discovery, specifically, memory corruption flaws. Experiments show the efficiency of the proposed hybrid intelligent approach over traditional black box testing techniques.