Execution Fault Localization in Large Dynamic Web Application Using Randomized Input Generation
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).
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In today's online oriented environment, localization of dynamic faults has become a major role in web application development. This paper describes the novel framework named as Pin Down, an automated root cause analysis of dynamically generated web pages. Execution fault localization in dynamic web application is a prominent problem of finding root location that causes failure in which source code changes need to be made in order to fix predicted faults. Execution faults are web application error that causes incorrect functionality to the dynamic web application that also called as execution bugs. The dynamic execution nature of the web application separates the source cause of an execution bug by various fault localization techniques. To identify execution faults in dynamic web applications, some fault localization algorithms improved by using source mapping and extended domain for conditional and functional call statements, but it did not focus on server side vulnerability. This study presents a randomized input generation technique for dynamic web application to check whether the web page is vulnerable to SQL injection or not. An automated random input generation is constructed for all executable statements in source code to determine the execution failures, such as missing include file, incorrect SQL query and uncaught exception of the corresponding statement. In addition, the framework used to determine the HTML failures such as generated HTML page is not syntactically correct according to an HTML validator by checking appropriate tags with closing tags using parsing the DOM tree. Other than execution and HTML failure novel framework Pin Down used to locate execution failures caused by deprecated language constructs that are all produce obstructive exception and error message during execution. Final result shows that code coverage improved from 95% to 100% were the result indicates 100% code covered is a reliable indicator of the effectiveness of a test case.
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Pin Down, Fault Localization, Bug, Random input generation, dynamic web application, SQL Injection, Execution failure, HTML failure, HTML validator, DOM tree.