Abstract
Among web application vulnerabilities, XSS is the most frequently occurring. Where a web application accepts a user-input, it is possible for such vulnerability to inject malicious scripts. The greater part of the literature concentrated on the application of static analysis in order to locate XSS vulnerabilities. The reason for this is its capability of achieving effectively a 100 percent code coverage and observing every path of the program. Nevertheless, the main restriction of static analysis, being the false positive rate shown in the results, continues. Consequently, researchers began to merge static analysis with other algorithms, such as genetic algorithm, machine learning and pattern matching. This is to improve the XSS detection results as well as the static analysis run time. This essay defines the algorithms which formerly improved the static analysis outcomes regarding XSS vulnerability detection. Furthermore, each method’s restriction was mentioned in which the studies continue to lack an efficient detection of XSS vulnerability in PHP web application.
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