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INSTALL.md

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Setting Up Repo for Test Generation

  1. Clone the repository.
  2. Install Conda environment for the project using this environment file for Mac or environment file for Windows. you can use conda env create -f environment.yml to create the environment and activate the environment by conda activate usage.
  3. install Appium Desktop
  4. add app's configuration in App_Config.py -- you can find relevant info in news_app_info.csv and shopping_app_info.csv
  5. start Android Emulator -- make sure the AUT (App Under Test) is installed on the Emulator, e.g., abcnews.
  6. start Appium Server on Appium desktop (you do not need to change the default port).
  7. Change file paths in global_config.py. Final-Artifact can be downloaded here.
  8. run main method in dynamic_evaluation.py

At this point you should be able to see an output similar to the output below on your command line interface:

The screen classifier top1 guess for the screen: category The screen classifier top5 guesses for the screen: ['category', 'home', 'items', 'menu', 'popup'] Choose the closest screen tag from the top5 guesses: [You should type the closest screen tag on your cli] home

id:0 floating_search_view - matched with: to_search id:1 rl_search_box - matched with: to_search id:2 navigation_home - matched with: home id:3 navigation_feed - matched with: menu Choose the id of the widget you want to interact with:[You should type the id on your cli]: 0 Please enter the ground truth IR for the widget you chose:to_search executing event: resource-id floating_search_view click

Enabling REMAUI feature

  1. download REMAUI Java program
  2. install Eclipse for Java if you don't already have it
  3. use Eclipse to import the REMAUI Java program
  4. configure all the build paths (the REMAUI repo should already contain all the necessary jars except for openCV) -- see screenshots below regarding build path. you need to edit the paths of jar files to the file paths on your machine

image

image

  1. follow this tutorial to enable openCV (see notes below first if using Mac)
  2. double check the main method in edu.wm.cs.semeru.redraw.REMAUI, it should only have run_REMAUI_single_image(args[0]);.
  3. run the main method in edu.wm.cs.semeru.redraw.REMAUI through Eclipse's "run configurations" and give one argument which is the path of a screen image (see below). (Make sure to change file path in run_REMAUI_single_image function, including projectRootDirectory, and screenshots String which should be the folder name that contains the image). REMAUI output should be in the parent folder of the image path.
  4. After running sccessfully, copy the command and replace REMAUI_cmd in state.py

If you're using Mac, you need to build openCV yourself in order to have that "build" folder. follow steps below:

  1. after downloading openCV's source, make a build directory in it.
  2. then you build opencv using cmake command. If you are in the build folder you just created, cmake .. will do. Then run make -j8
  3. from the tutorial above, you should put build/lib as the native library location

image

Running for a usage

  1. usage_root_dir should point to V2S's results of a usage, e.g., "Combined/SignIn/".
  2. run 1_step_extraction/step_extraction.py to generate clicked_frames folder
  3. go to binaryClassifier and follow the instructions on the next section to generate final typing_result.csv.
  4. run 1_step_extraction/step_cleaning.py to generate steps_clean folder.
  5. run 1_step_extraction/special_step_recognition.py to append special actions in steps_clean folder.
  6. run 2_ir_classification/screen_widget_extraction.py to generate ir_data_auto folder with screens only.
  7. get UIED results (run UIED v2.3's run_mybatch.py) -- check terminal for running time as this process is time-consuming.
  8. run 2_ir_classification/screen_widget_extraction.py to add widget crops to ir_data_auto folder.