Testing Apps With Real-World Inputs

To test mobile apps, one requires realistic and coherent test inputs. The Link approach for Web testing has shown that knowledge bases such as DBPedia can be a reliable source of semantically coherent inputs. In this paper, we adapt and extend the Link approach towards test generation for mobile app...

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Vydáno v:2020 IEEE/ACM 15th International Conference on Automation of Software Test (AST) s. 1 - 10
Hlavní autoři: Wanwarang, Tanapuch, Borges, Nataniel P., Bettscheider, Leon, Zeller, Andreas
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 01.10.2020
Témata:
Dynamic analysis
Human computer interaction
Human-centered computing → Graphical user interfaces
Knowledge based systems
Mobile applications
Natural language processing
Smartphones
Software
Software and its engineering → Software testing and debugging
Software testing
Test pattern generators
ISSN:2833-9061
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Shrnutí:To test mobile apps, one requires realistic and coherent test inputs. The Link approach for Web testing has shown that knowledge bases such as DBPedia can be a reliable source of semantically coherent inputs. In this paper, we adapt and extend the Link approach towards test generation for mobile applications:(1)We identify and match descriptive labels with input fields, based on the Gestalt principles of human perception;(2)We then use natural language processing techniques to extract the concept associated with the label;(3)We use this concept to query a knowledge base for candidate input values;(4)We cluster the UI elements according to their functionality into input and actions, filling the input elements first and then interacting with the actions.Our evaluation shows that leveraging knowledge bases for testing mobile apps with realistic inputs is effective. On average, our approach covered 9% more statements than randomly generated text inputs.
ISSN:2833-9061
DOI:10.1145/3387903.3389310