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To automatically test web applications, crawling-based techniques are usuallyadopted to mine the behavior models, explore the state spaces or detect theviolated invariants of the applications. However, in existing crawlers, rulesfor identifying the topics of input text fields, such as login ids, passwords,emails, dates and phone numbers, have to be manually configured. Moreover, therules for one application are very often not suitable for another. In addition,when several rules conflict and match an input text field to more than onetopics, it can be difficult to determine which rule suggests a better match.This paper presents a natural-language approach to automatically identify thetopics of encountered input fields during crawling by semantically comparingtheir similarities with the input fields in labeled corpus. In our evaluationwith 100 real-world forms, the proposed approach demonstrated comparableperformance to the rule-based one. Our experiments also show that the accuracyof the rule-based approach can be improved by up to 19% when integrated withour approach.

Using Semantic Similarity for Input Topic Identification in Crawling-based Web Application Testing