{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/using-semantic-similarity-for-input-topic","title":"Using Semantic Similarity for Input Topic Identification in Crawling-based Web Application Testing","arxiv_id":"1608.06549","date":"2016-08-23","proceeding":null,"authors":["Jun-Wei Lin","Farn Wang"],"abstract":"To automatically test web applications, crawling-based techniques are usually\nadopted to mine the behavior models, explore the state spaces or detect the\nviolated invariants of the applications. However, in existing crawlers, rules\nfor identifying the topics of input text fields, such as login ids, passwords,\nemails, dates and phone numbers, have to be manually configured. Moreover, the\nrules for one application are very often not suitable for another. In addition,\nwhen several rules conflict and match an input text field to more than one\ntopics, it can be difficult to determine which rule suggests a better match.\nThis paper presents a natural-language approach to automatically identify the\ntopics of encountered input fields during crawling by semantically comparing\ntheir similarities with the input fields in labeled corpus. In our evaluation\nwith 100 real-world forms, the proposed approach demonstrated comparable\nperformance to the rule-based one. Our experiments also show that the accuracy\nof the rule-based approach can be improved by up to 19% when integrated with\nour approach.","url_abs":"http://arxiv.org/abs/1608.06549v1","url_pdf":"http://arxiv.org/pdf/1608.06549v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"using-semantic-similarity-for-input-topic","repo_url":"https://github.com/jwlin/arxiv-160430","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}