kaprese

Collection of APR tools by SAL (Software Analysis Lab) at Korea University.

Quick Start

You can run kaprese by following the steps below.

Prerequisites

kaprese requires the following software to be installed:

• Python 3.12 or higher (required)
• Docker 24 or higher (recommended, we did not test it on other versions)

Installation

kaprese can be installed via pip:

pip install kaprese

Usage

You can run saver engine on flint-1 benchmark as follows:

kaprese benchmark preset c
kaprese engine preset saver
kaprese run -b flint-1 spearmint-1 -e saver
# See the logs. If you want to see more logs set LOG_LEVEL environment variable to INFO, e.g.,
# LOG_LEVEL=info kaprese run -b flint-1 spearmint-1 -e saver

Options

kaprese provides the following options:

Benchmarks	Repository
benchmark	command to do something about benchmark
engine	command to do something about engine
run	command to run APR tools on benchmarks
eval	command to evaluate the results
-h, --help	command to show help

Benchmarks and Engines

kaprese provides a set of benchmarks and engines. You can register them by calling kaprese benchmark preset <language> and kaprese engine preset <engine>. The following table shows the list of supported benchmarks and engines.

Benchmarks

Benchmarks	Repository
c	C benchmarks in kupl/starlab-benchmarks
ocaml	OCaml benchmarks in kupl/starlab-benchmarks

Engines

Engines	Repository
saver	kupl/SAVER_public
cafe	kupl/LearnML

How to Add Your Own

All benchmarks and engines are registered to the global registry (by default, registry is located in ~/.kaprese). You can add your own benchmarks and engines by registering them with a small python code.

Add a Benchmark

A benchmark is an instance of kaprese.core.benchmark.Benchmark. You can initialize a benchmark with the following 4 parameters:

• name: name of the benchmark (must be unique).
• image: docker image name, e.g., ghcr.io/kupl/starlab-benchmarks/c:flint-1.
• language_command or _language: command to find the language of the benchmark, if you set _language, language_command is ignored. the command will be run inside the docker container from image.
• workdir_command or _workdir: command to find the working directory of the benchmark, if you set _workdir, workdir_command is ignored. the command will be run inside the docker container from image. Then by calling register method of Benchmark, you can register the benchmark to the global registry.

For example, the following code defines a benchmark named flint-1:

from kaprese.core.benchmark import Benchmark

flint_1 = Benchmark(
    name="flint-1",
    image="ghcr.io/kupl/starlab-benchmarks/c:flint-1",
    language_command="cat metadata.json | jq -r .language", # c
    workdir_command="cd $(cat metadata.json | jq -r .buggyPath) && pwd", # /workspace/buggy
)
flint_1.register()

Add an Engine

An engine is an instance of kaprese.core.engine.Engine. You can initialize an engine with the following 6 parameters:

• name: name of the engine (must be unique).
• supported_languages: list of supported languages, e.g., ["c", "java"].
• supported_os: list of supported operating systems, e.g., ["ubuntu:20.04", "debian:12"].
• location: location to the docker context directory of the engine, e.g., ~/saver.
• build_args: arguments to pass to docker build command to fill ARG variables in the Dockerfile, e.g., {"SOME_ARG": "some_value"}.
• exec_commands: list of commands to execute the engine, e.g., ["saver ...", "cp ..."], all commands are joined with ;, in other words, all commands are executed and the return code of the last command is returned. Note that, your dockerfile MUST get the benchmark image as an argument and build from it, e.g.,

ARG BENCHMARK_IMAGE
FROM ${BENCHMARK_IMAGE}
...

In the same way, you MUST specify the benchmark image as an argument when you build the engine image, e.g.,

from kaprese.core.engine import Engine

saver = Engine(
    "saver",
    supported_languages=["c"],
    supported_os=["ubuntu:20.04"],
    location="https://github.com/kupl/kaprese-engines#main:context/saver/starlab-benchmarks", # location of saver context for preset benchmarks
    build_args={"BENCHMARK_IMAGE": "ghcr.io/kupl/starlab-benchmarks/c:flint-1"},
    exec_commands= ["saver ...", "cp ..."], # see kaprese/engines/saver.py
)

Then by calling register method of Engine, you can register the engine to the global registry.

saver.register()

How to Contribute

Any contributions are welcome! You can leave an issue or make a pull request.