This repository is the official implementation of MACER: A Modular Framework for Accelerated Compilation Error Repair. The paper describing the development of this framework can be accessed at [this arXiv link]

This project runs on Python 3.6. To install all packages, and curate the datasets from TRACER and DeepFix, install conda and run

sudo make install

The MakeFile provided executes several steps. These are enumerated below for sake of clarity. If using the Makefile itself, you need not execute the following steps individually.

1. Ubuntu/Debian packages

   conda install python=3.6.5

   sudo apt install clang python3-pip unzip gzip curl sqlite3

2. Anaconda environment

   conda create --name macer36 python=3.6
   conda activate macer36
   

3. Python packages

   pip3 install --version -r requirements.txt

4. Set Clang paths

   Create symbolic link (if it doesn't already exist) to enable Python-Clang bind

   sudo apt-get install -y libclang-dev
   sudo ln -sf libclang-11.so libclang.so
   cd /usr/lib/x86_64-linux-gnu/
   sudo ln -s libclang-XX.YY.so.1 libclang.so
   

   Where, XX.YY is the version number of Clang installed on your system.

5. Pull TRACER's dataset, which is used for training and testing of MACER

   git clone https://github.com/umairzahmed/tracer.git
   unzip tracer/data/dataset/singleL/singleL_Test.zip -d tracer/data/dataset/singleL/
   unzip tracer/data/dataset/singleL/singleL_Train+Valid.zip -d tracer/data/dataset/singleL/
   

6. Repair class creation (refer Section 2. of our paper)

   python3 -m srcT.DataStruct.ClusterError

7. Pull DeepFix dataset, used for testing MACER

   curl -O https://www.cse.iitk.ac.in/users/karkare/prutor/prutor-deepfix-09-12-2017.zip
   unzip prutor-deepfix-09-12-2017.zip
   gzip -d prutor-deepfix-09-12-2017/prutor-deepfix-09-12-2017.db.gz
   

8. Extract DeepFix dataset into Macer's format

   sqlite3 -header -csv prutor-deepfix-09-12-2017/prutor-deepfix-09-12-2017.db "select * from Code where error<>'';" > prutor-deepfix-09-12-2017/deepfix_test.csv
   
   python3 -m srcT.DataStruct.PrepDeepFix
   

The MACER toolchain makes use of various standard libraries. A minimal list is provided in the requirements.txt file. If you are a pip user, please use the following command to install these libraries:

pip3 install -r requirements.txt

Note about version dependency: although the requirements file specifies version dependencies to be exact, this is to err on the side of caution. For most of the libraries (e.g. pandas or scikit-learn), a more recent version should work well too. However, the dependency on the 1.2.4 version of the edlib library seems to be strict. Having a different version of this library may cause the toolchain to malfunction at evaluation time. Similarly, for tensorflow, it seems that version 2.0 can cause issues with training. We advise caution while using versions of libraries different from those mentioned in the requirements file.

To train the model(s) afresh on TRACER's training dataset consisting of single-line incorrect-correct C program pairs (please refer to Section 4 in the manuscript linked above), execute the command given below.

python3 train.py 

Training might take between 7 minutes to 20 minutes depending on the machine configuration. Please note that executing the training routine will overwrite any previously trained model.

To perform repair on a single C program using MACER, use

python3 testRepair.py <path-to-file.c> <PredK>

Example: an example program provided at data/input/fig_1a.c contains errors in a for loop statement specifier. To repair this program, execute the following command

$ python3 testRepair.py data/input/fig_1a.c 5

To obtain Pred@K accuracy on TRACER's test dataset (please refer to Table 4. in the manuscript linked above), execute the command given below. In the following, the parameter PredK specifies how many of the ranked suggestions by MACER should be considered (MACER offers, for any input program, a ranked list of repair suggestions). Please refer to Section 6.2 in the manuscript linked above for a description of this metric.

Evaluating Pred@K accuracy on all programs in the test dataset should take about 2-3 minutes depending on the machine configuration. Pred@K accuracy obtained will be printed at the end of execution.

python3 testPredAtK.py <PredK>

Example:

$ python3 testPredAtK.py 5
Pred@5: 0.693

To obtain MACER's repair accuracy on TRACER's single-line test dataset (please refer to Table 4. in the manuscript linked above), execute the command given below. The parameter PredK continues to specify how many of the ranked suggestions by MACER should be considered. Please refer to Section 6.2 in the manuscript linked above for a description of this metric.

Evaluating repair accuracy on all programs in the test dataset might take 30 - 50 minutes depending on the machine configuration. The time taken is longer here since unlike Pred@K where the repaired program is merely checked for (abstract) match with the gold standard (the students's own repaired program), here the program is recompiled. Repair accuracy obtained will be printed at the end of execution.

python3 testRepair.py tracer_single <PredK>

Example:

python3 testRepair.py tracer_single 5

To obtain MACER's repair accuracy on DeepFix's test dataset (please refer to Table 5. in the manuscript linked above), execute the command given below. The parameter PredK continues to specify how many of the ranked suggestions by MACER should be considered.

Evaluating repair accuracy on all programs in the test dataset might take around 30 minutes depending on the machine configuration. Repair accuracy obtained will be printed at the end of execution.

python3 testRepair.py deepfix <PredK>

Example:

python3 testRepair.py deepfix 5

MACER should offer the following performance on the given datasets. Minor deviations are expected if training afresh due to various randomizations used during the training process.

This repository is released under the MIT license. If you would like to submit a bugfix or an enhancement to MACER, please open an issue on this GitHub repository. We welcome other suggestions and comments too (please mail the corresponding author at [email protected])

We are thankful to Bhaskar Mukhoty for helping us identify certain cross-platform dependecies of the MACER toolchain.