Salento is a statistical bug-detection framework based on the machine learning model used by Bayou. For technical details about Salento refer to the paper Bayesian Specification Learning for Finding API Usage Errors, FSE'17 (link)

• Python3 (Tested with 3.5.1)
• Tensorflow (Tested with 1.4)

To train a Salento model on a data file, say DATA.json:

1. Setup environment:

export PYTHONPATH=$PYTHONPATH:/path/to/salento/src/main/python

2. Ensure that the data is in the right JSON format using the schema file doc/json_schemas/salento_input_schema.json.

3. (Optional.) Extract evidences from the data:

python3 src/main/python/scripts/evidence_extractor.py DATA.json DATA-training.json

This will create a DATA-training.json after extracting evidences from each package in DATA.json. Run with --help for more options that you can use to filter the sequences selected for training.

4. Go to the model folder and start training with a model configuration:

cd src/main/python/salento/models/low_level_evidences
python3 train.py /path/to/DATA-training.json --config config.json

Run with --help to see a description of the model configuration options. Edit config.json as needed.

To test a trained model on some test data:

1-3. Follow steps 1-3 above to produce a file DATA-testing.json with evidences.

4. Go to the aggregators folder and run one of the aggregators on the test data:

cd src/main/python/salento/aggregators
python3 sequence_aggregator.py --data_file /path/to/DATA-testing.json --model_dir /path/to/model/directory

The model directory should contain the trained model's files, such as checkpoint, config.json, etc.