/AST-2022-submission

Primary LanguageHTML

AST-2022-submission

Overview of folders

Folder application

It stores the source code of 13 applications that we use in the experiment. They are from FSE19-submission-material-DIG and FSE19-submission-material-TEDD.

Folder crawl-results

It stores the files that crawled from 13 applications by Crawljax.

Folder manual-labels

It stores the files that contain labels manually assigned by us. Each file contains:

  • id: unique id assigned by Crawljax
  • details: dictionary format of the clickable
  • current dom: dom format of the clickable
  • context dom: dom forst of the context of the clickable
  • source state url: the url that stores the screenshot of source state
  • target state url: the url that stores the screenshot of target state
  • state diff url: the text difference between two states
  • manual label: the label that represent the meaning of the clickable
  • manual label action: the action verb in manual label
  • manual label context: to which degree we can extract meaningful information from the dom of clickable and its source and target state. It contains 3 different scores, 0 represents that the manual label is irrelevant to the clickable and states 1 represents that a part of manual label exists in the clickable and states 2 represents that the manual label exists completely in the clickable and states

Folder supervised-labels

It stores the model output of Supervised approach and its evaluation results.

  • heuristic label in ${project_name}-labels.xlsx means the model output
  • ${project_name}-labels-eval.xlsx contains the scores of evaluation metrics for each clickable

Folder unsupervised-labels

It stores the model output of Unsupervised approach and its evaluation results.

  • pcfg label in ${project_name}-pcfg-labels.xlsx means the model output
  • ${project_name}-labels-eval.xlsx contains the scores of evaluation metrics for each clickable
  • ${project_name}-pcfg-labels_no_context.xlsx contains the model output after removing the usage of clickable context from input (use clickable only)
  • ${project_name}-pcfg-postag.xlsx contains production rules and the probability of each production rule

Folder keybert-labels

It stores the model output of KeyBERT (one of baselines) and its evaluation result.

  • keybert label in ${project_name}-labels.xlsx means the model output.
  • ${project_name}-labels-eval.xlsx contains the scores of evaluation metrics for each clickable

Folder preprocess-labels

It stores the model output of PreProcess (one of baselines) and its evaluation result.

  • preprocess label in ${project_name}-labels.xlsx means the model output
  • ${project_name}-labels-eval.xlsx contains the scores of evaluation metrics for each clickable

CrawLabel

Folder labelled-testcasegenerator-plugin contains the source code of our tool CrawLabel to assign labels to test cases generated by Crawljax.

Steps of generating labels

  1. Go to folder python, run pip install -r requirements.txt (install necessary python libraries)
  2. (1) If using supervised approach, go to folder python, run python -m ui.main rank_attributes --projects={project_names} (here {project_names} is a string split by comma, such as "addressbook,jpetstore"), the output is a list of ranked attributes stored in results/ranked_attributes.json. It is possible for you to add your own training file in results/training. Remember to follow the format in results/training. Then go to the folder, run python -m ui.main labeled_tests --project={project_name} --crawlfolder={crawl_folder_path} (here {project_name} is the name for project, {crawlfolder_path} is the absolute path of crawl folder generated by Crawljax, example python -m ui.main labeled_tests --project=addressbook --crawlfolder=/Users/xyz/AST-2022-submission/crawl-results/addressbook. Make sure there is a file ${crawl_folder_path}/Crawlpaths.json). The output is file with labels (column name is "heuristic label") in results/heuristic/{project_name}-labels.xlsx

(2) If using unsupervised approach, go to folder python, run python -m ui.labels.PCFG --project_name {project_names} apply_pcfg_postag and python -m ui.labels.PCFG --project_name {project_names} apply_pcfg_postag_forest

Example

Folder example contains the labeled test cases. Here the default approach is KeyBert.

  • example/jpetstore/src/generated/GeneratedTests.java is the test class generated by Crawljax

  • example/jpetstore/src/generated_labels/GeneratedTests.java is the test class generated with our tool