Online Repository for ICSME'2022 Paper "PEELER: Learning to Effectively Predict Flakiness without Running Tests"

The DOI of the repository is 10.5281/zenodo.7066904.

Summary

Peeler is a fully static flaky tests detector that relies on data dependency in a Test Dependency Graph. Peeler captures test flakiness by modeling how the values of the variables involved in assertion statements are transferred as reflected in the embedded contextual paths. The evaluation on 17,532 tests from 21 projects (of which 689 are flaky) shows that Peeler outperforms prior works by about 20% in terms of Precision and F-score. During a live study, Peelr has already helped developers identify 12 flaky test cases in real-world project test suites.

Environment

• OS: Ubuntu 18.04
• Python version: >= 3.6.13
• Java JDK version: >= 11.0.2
• CUDA version: 11.1

Experiments

Benchmark

Before extracting contextual paths from flaky/non-flaky tests, a csv file contain the information of the flaky/non-flaky tests is required. Generate FlakeFlagger_benchmark.csv from the FlakeFlagger dataset through the following command:

python dataset/benchmark/transform_FlakeFlagger_dataset.py

Notice that this process will clone all of the java projects related with the dataset, the projects directory is set in transform_FlakeFlagger_dataset.py. Also, you can build your own dataset in the form of FlakeFlagger_benchmark.csv.

Corpus

Once we got the benchmark, the features of flaky/non-flaky tests is able to be extracted by the pathextractor, which generates a TDG for each flaky/non-flaky method and extracts a set of contextual paths from it. Command:

java -jar pathextractor/target/pathextractor-1.0-SNAPSHOT-jar-with-dependencies.jar --csvFile --projectsDir --output_dir

• --csvFile: the benchmark csv file contains the information of all the flaky/non-flaky tests. (e.g., benchmark/FlakeFlagger_benchmark.csv)
• --projectsDir: the directory to the cloned projects in step Benchmark. (e.g., ~/projects)
• --output_dir: the directory where a file named corpus.txt will be generated, which contains features of all flaky/non-flaky tests of the dataset. (e.g., corpus)

Dataset

Parse corpus.txt to final dataset through command:

python corpus/parse_corpus.py --file --output_dir

• --file: path to the corpus.txt generated in step Corpus. (e.g., corpus/corpus.txt)
• --output_dir: the output directory. (e.g., dataset)

Three files named functions.txt(edge information in paths), samples.txt(paths of each flaky/non-flaky tests) and tokens.txt(token dictionary) respectively will be generated under output_dir.

The processed corpus and dataset can be downloaded here.

Pretrained Embeddings

We utilize pretrained code2vec to generate embeddings for tokens and functions. The commands are:

python3 embeddings/method2embeddings.py --input_file --code2vec_dir --output_dir

• --input_file: path to the functions.txt generated in step Dataset. (e.g., dataset/functions.txt)
• --code2vec_dir: directory of the code2vec project, notice that we modify code2vec slightly for compatibility, the pretrained model can be downloaded here. (e.g., code2vec)
• --output_dir: the output directory where methods_embeddings.txt will be generated. (e.g., embeddings)

python3 embeddings/tokens2embeddings.py --file --vecs --output_dir

• --file: path to the tokens.txt generated in step Dataset. (e.g., dataset/tokens.txt)
• --vecs: path to the pretrained token embedding file, which can be downloaded here. (e.g., code2vec/token_vecs.txt)
• --output_dir: the output directory where tokens_embeddings.txt will be generated. (e.g., embeddings)

Train Peeler

Run the following command to train a Peeler model from scratch, detailed parameters can be seen in flaky_detect.py:

python flaky_detect.py

test_result.csv and the trained .model file will be generated under output directory. Training logs will be found in logs directory.

Evaluate Peeler

Evaluate pretrained Peeler model through this command:

python flaky_detect.py --predict_model --predict_mode True

• --predict_model: path to the pretrained model file. (e.g., evaluation/10FoldCrossValidation/b64_encode384_pl20_pc100_hd128_tv256_lr001_10FoldCrossValidationcross_0.model)

Evaluation Results

We apply 10-fold validation for evaluating Peeler on the whole dataset, log in directory logs.

• RQ1: The pretrained models for each fold, evaluation results, as well as the comparison results are released in directory evaluation/10FoldCrossValidation.
• RQ2: The inner-file and inter-file ablation study results can be seen in evaluation/ablation study.

In RQ3, Peeler is utilized for detecting previously-unknown flaky tests in real-world java projects, Peeler predicts 65 out of 1,835 tests as flaky in total, we report 21 of them, among which 12 have been accepted by developers, the links to the issues are as follows:

• google/guava#5864
• google/guava#5865
• google/guava#5871
• GoogleCloudPlatform/DataflowTemplates#326

For the remain 9 reports, the developers havn't yet reply to us, their links are:

• alibaba/fastjson#4004
• GoogleCloudPlatform/DataflowTemplates#326

Note that we reported 9 tests in the issue of project DataflowTemplates, one of them has been confirmed as flaky test and the remain 8 havn't been reproduced yet according to the developers.