Filling query-type text inputs for Android applications via inner-app mining and GPT recommendation

Abstract

GUI testing often requires filling reasonable text inputs to activate specific GUI behaviors, which is particularly challenging for query-type inputs used to search contents. Existing techniques may generate input data inconsistent with the query semantic or semantically consistent but not aligned with the current query domain. These data often result in trivial empty searches and are hard to test the internal matching conditions in the query. No search results also affect the further exploration of GUI pages depending on the query. This paper presents MATI (Mining App to generate Text Input), a new test generation method designed to fill query-type input items in Android applications with an intent to activate non-empty query searches. The method is built on a novel idea that the data suitable to input to a query often already occur on the GUI. Instead of creating new data by algorithms or retrieving data from external sources like the existing methods, MATI does inner-app mining to obtain proper candidate data for query inputs in an app. It then recommends the candidate data best fitting a specific query in both the query semantic and domain for test inputting via a GPT model. In experiments on 25 applications, MATI achieved a 94.6% GUI passing rate for query-type inputs–32.4% higher than the leading baseline, QTypist, thanks to its more generated non-empty searches. Its higher passing rates also led to 9.7% increments in the Android Activity coverage achieved during testing when integrating these methods into an automated GUI testing process. Such results suggest that MATI might benefit the practical testing of Android applications.