Artifact for "Predictive Program Slicing via Execution Knowledge-Guided Dynamic Dependence Learning"

ND-Slicer is a learning-based predictive slicing tool. The source code, data, and model artifacts are publicly available on GitHub and Zenodo.

Getting Started
Contributing Guidelines
License

Getting Started

This section describes the preqrequisites, and contains instructions, to get the project up and running.

Setup

Hardware Requirements

ND-Slicer requires a GPU to run fast and produce the results. On machines without a GPU, note that it can be notoriously slow.

Project Environment

Currently, ND-Slicer works well on Ubuntu OS, and can be set up easily with all the prerequisite packages by following these instructions (if conda is already installed, update to the latest version with conda update conda, and skip steps 1 - 3):

Download the latest, appropriate version of conda for your machine (tested with conda 23.11.0).
Install it by running the conda_install.sh file, with the command:
```
$ bash conda_install.sh
```
Add conda to bash profile:
```
$ source ~/.bashrc
```
Navigate to nd-slicer (top-level directory) and create a conda virtual environment with the included environment.yml file using the following command:
```
$ conda env create -f environment.yml
```
To test successful installation, make sure autoslicer appears in the list of conda environments returned with conda env list.
Activate the virtual environment with the following command:
```
$ conda activate autoslicer
```

Directory Structure

1. Data Artifacts

Navigate to nd-slicer/data/ to find:

Raw dataset file (codenetmut_test.json) -- use these files to build train/validation/test splits from scratch.
Processed dataset files ({full|train|val|test}-dataset.jsonl) -- use these files to benchmark predictive slicing approaches, or replicate intrinsic evaluation results in the paper (Section 5, Table 1).

2. Model Artifacts

Navigate to nd-slicer/outputs/ to find the trained model weights for CodeExecutor (B2), GraphCodeBERT+PointerTransformer (B3), GraphCodeBERT+Transformer (B4), CodeExecutor+PointerTransformer (B5), and CodeExecutor+Transformer (B6).

3. Code

Navigate to nd-slicer/src/ to find the source code for running all the experiments/using ND-Slicer to predict dynamic slices for a Python program.

Usage Guide

See link for details about replicating results in the paper, as well as using ND-Slicer to predict slices for Python programs. Here's an executive summary of the same:

Experiment	Table # in Paper	Run Command(s)	Model Artifact(s) for Direct Inference
(RQ1) Intrinsic Evaluation on Executable Python Code	1	click here	CodeExecutor (B2)
			GraphCodeBERT + PointerTransformer (B3)
			GraphCodeBERT + Transformer (B4)
			CodeExecutor + PointerTransformer (B5)
			CodeExecutor + Transformer (B6)
(RQ2) Intrinsic Evaluation on Non-Executable Python Code	-	click here	CodeExecutor + Transformer (B6)
(RQ3) Extrinic Evaluation (Crash Detection)	2	click here	CodeExecutor + Transformer (B6)
(RQ4) Qualitative Analysis (Statement Types)	3	click here	CodeExecutor + Transformer (B6)
(RQ5) Qualitative Analysis (Execution Iterations)	4	click here	CodeExecutor + Transformer (B6)
(RQ6) Inter-Procedural Analysis	-	click here	CodeExecutor + Transformer (B6)

Contributing Guidelines

There are no specific guidelines for contributing, apart from a few general guidelines we tried to follow, such as:

Code should follow PEP8 standards as closely as possible
Code should carry appropriate comments, wherever necessary, and follow the docstring convention in the repository.

If you see something that could be improved, send a pull request! We are always happy to look at improvements, to ensure that nd-slicer, as a project, is the best version of itself.

If you think something should be done differently (or is just-plain-broken), please create an issue.

License

See the LICENSE file for more details.

aashishyadavally / nd-slicer