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AppFlow: using machine learning to synthesize robust, reusable UI tests

Authors:

Junfeng YangAuthors Info & Claims

ESEC/FSE 2018: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 269 - 282

https://doi.org/10.1145/3236024.3236055

Published: 26 October 2018 Publication History

Abstract

UI testing is known to be difficult, especially as today’s development cycles become faster. Manual UI testing is tedious, costly and error- prone. Automated UI tests are costly to write and maintain. This paper presents AppFlow, a system for synthesizing highly robust, highly reusable UI tests. It leverages machine learning to automatically recognize common screens and widgets, relieving developers from writing ad hoc, fragile logic to use them in tests. It enables developers to write a library of modular tests for the main functionality of an app category (e.g., an “add to cart” test for shopping apps). It can then quickly test a new app in the same category by synthesizing full tests from the modular ones in the library. By focusing on the main functionality, AppFlow provides “smoke testing” requiring little manual work. Optionally, developers can customize AppFlow by adding app-specific tests for completeness. We evaluated AppFlow on 60 popular apps in the shopping and the news category, two case studies on the BBC news app and the JackThreads shopping app, and a user-study of 15 subjects on the Wish shopping app. Results show that AppFlow accurately recognizes screens and widgets, synthesizes highly robust and reusable tests, covers 46.6% of all automatable tests for Jackthreads with the tests it synthesizes, and reduces the effort to test a new app by up to 90%. Interestingly, it found eight bugs in the evaluated apps, including seven functionality bugs, despite that they were publicly released and supposedly went through thorough testing.

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Cited By

Lan YLu YPan MLi XFilkov VRay BZhou M(2024)Navigating Mobile Testing Evaluation: A Comprehensive Statistical Analysis of Android GUI Testing MetricsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695476(944-956)Online publication date: 27-Oct-2024
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Index Terms

AppFlow: using machine learning to synthesize robust, reusable UI tests
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software evolution
    2. Software verification and validation
      1. Empirical software validation
      2. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ESEC/FSE 2018: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

October 2018

987 pages

ISBN:9781450355735

DOI:10.1145/3236024

General Chair:
Gary T. Leavens
University of Central Florida, USA
,
Program Chairs:
Alessandro Garcia
PUC-Rio, Brazil
,
Corina S. Păsăreanu
NASA Ames Research Center, USA

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Lan YLu YPan MLi XFilkov VRay BZhou M(2024)Navigating Mobile Testing Evaluation: A Comprehensive Statistical Analysis of Android GUI Testing MetricsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695476(944-956)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695476
Feng SLu HJiang JXiong THuang LLiang YLi XDeng YAleti AFilkov VRay BZhou M(2024)Enabling Cost-Effective UI Automation Testing with Retrieval-Based LLMs: A Case Study in WeChatProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695260(1973-1978)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695260
Zhao YHarrison BYu T(2024)DinoDroid: Testing Android Apps Using Deep Q-NetworksACM Transactions on Software Engineering and Methodology10.1145/365215033:5(1-24)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3652150
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Lan YLu YLi ZPan MYang WZhang TLi XRoychoudhury APaiva AAbreu RStorey M(2024)Deeply Reinforcing Android GUI Testing with Deep Reinforcement LearningProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623344(1-13)Online publication date: 20-May-2024
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Sharma TKechagia MGeorgiou STiwari RVats IMoazen HSarro F(2024)A survey on machine learning techniques applied to source codeJournal of Systems and Software10.1016/j.jss.2023.111934209:COnline publication date: 14-Mar-2024
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