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Statistical bug-finding framework for API-using code
Python
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README.md

Salento

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)

Requirements

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

Training

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

  1. Setup environment:
export PYTHONPATH=$PYTHONPATH:/path/to/salento/src/main/python
  1. Ensure that the data is in the right JSON format using the schema file doc/json_schemas/salento_input_schema.json.

  2. (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.

  1. 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.

Inference

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.

  1. 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.

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