  To automatically test web applications, crawling-based techniques are usually
adopted to mine the behavior models, explore the state spaces or detect the
violated invariants of the applications. However, in existing crawlers, rules
for identifying the topics of input text fields, such as login ids, passwords,
emails, dates and phone numbers, have to be manually configured. Moreover, the
rules 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 one
topics, it can be difficult to determine which rule suggests a better match.
This paper presents a natural-language approach to automatically identify the
topics of encountered input fields during crawling by semantically comparing
their similarities with the input fields in labeled corpus. In our evaluation
with 100 real-world forms, the proposed approach demonstrated comparable
performance to the rule-based one. Our experiments also show that the accuracy
of the rule-based approach can be improved by up to 19% when integrated with
our approach.
