bytedance / Fastbot_iOS

About Fastbot(2.0) is a model-based testing tool for modeling GUI transitions to discover app stability problems
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Introduction

Fastbot is a model-based testing tool for modeling GUI transitions to discover app stability problems. It combines machine learning and reinforcement learning techniques to assist discovery in a more intelligent way.

Related: Fastbot_Android

***More detail see at Fastbot architecture

update 2022.1

Prepare test environment

Run Test

key note sample
BUNDLEID Test App's Bundle ID com.apple.Pages
duration Test duration, units of minutes 300
launchenv Start arguments for Test APP, can be empty or key-values separated with ":" isAutoTestUI=1:channel=AutoTest
throttle Throttle for operate, units of millisecond 300

More detail see at 中文手册


Advanced Extension

Stub mode: Target dynamic library fastbot_stub. Stub mode requires injection of fastbot_stub into the test app. The library captures GUI structure by parsing the app under test for fastbot. More customized features (eg. hook callback, cut View) can be constructed by users for additional abilities such as blocking certain view from being clicked, customized ViewControllers, etc.

We highly appreciate any contribution from the community !!!

Usage: After injecting fastbot_stub to app, you need:

key sample
launchenv stubPort=9797
dataport 9797

Analytics

To prioritize and improve Fastbot-iOS, FastbotRunner collects usage data and uploads it to Google Analytics. FastbotRunner collects the md5 hash of the test app's Bundle ID, this information allows us to measure the volume of usage. If they wish, users can choose to disable the Analytics by skip step Open FastbotRunner network permission or change FastbotRunner's Wireless Data to off in System Preference.


Support


License

Copyright©2021 Bytedance

Licensed under Fastbot Revised

Fastbot-iOS required some features are based on or derives from projects below:

Publications

If you use our work in your research, please kindly cite us as:

  1. Lv, Zhengwei, Chao Peng, Zhao Zhang, Ting Su, Kai Liu, Ping Yang (2022). “Fastbot2: Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement Learning”. In proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE 2022). ACM, To appear. [pdf]
@inproceedings{fastbot2,
  title={Fastbot2: Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement Learning},
  author={Lv, Zhengwei and Peng, Chao and Zhang, Zhao and Su, Ting and Liu, Kai and Yang, Ping},
  booktitle={Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE 2022)},
  year={2022}
}
  1. Peng, Chao, Zhao Zhang, Zhengwei Lv, Ping Yang (2022). “MUBot: Learning to Test Large-Scale Commercial Android Apps like a Human”. In proceedings of the 38th International Conference on Software Maintenance and Evolution (ICSME 2022). IEEE, To appear. [pdf]
@inproceedings{mubot,
  title={MUBot: Learning to Test Large-Scale Commercial Android Apps like a Human},
  author={Peng, Chao and Zhang, Zhao and Lv, Zhengwei and Yang, Ping},
  booktitle={Proceedings of the 38th International Conference on Software Maintenance and Evolution (ICSME 2022)},
  year={2022}
}
  1. Cai, Tianqin, Zhao Zhang, and Ping Yang. “Fastbot: A Multi-Agent Model-Based Test Generation System”. In Proceedings of the IEEE/ACM 1st International Conference on Automation of Software Test. 2020. [pdf]
@inproceedings{fastbot,
  title={Fastbot: A Multi-Agent Model-Based Test Generation System},
  author={Cai, Tianqin and Zhang, Zhao and Yang, Ping},
  booktitle={Proceedings of the IEEE/ACM 1st International Conference on Automation of Software Test},
  pages={93--96},
  year={2020}
}