AI-based Static Application Security Testing Guide

Code smells are usually ignored as they are neither a bug nor a vulnerability. Quality engineers and specially security architects ignore them. As some of the code smells may lead towards vulnerability which may further be exploited by the hackers therefore such vulnerable code smells must be considered and further mitigated by threat modelers. In order to provide a repository of such code smells to security designers a process had been devised and experimented. During the execution various web applications had been passed through SAST and resulting code smells had been extracted and then inserted into a new dataset via Python. Later on the code smells deposited in the dataset had been classified into various categories. Finally machine learning algorithms had been assessed through WEKA and the fastest as well the most accurate algorithm had been selected. Current security standards do not ensure mitigation of threats caused by leading-to-vulnerability code smells till to date. Typically threat modelers assess security of a system through modeling threats via CIA STRIDE and LINDDUN standards on its DFD and various architectural / infrastructural diagrams.