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-# kaprese
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+# 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`:
+
+```bash
+pip install kaprese
+```
+
+### Usage
+
+You can run `saver` engine on `flint-1` benchmark as follows:
+
+```bash
+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 LOV_LEVEL environment variable to INFO, e.g.,
+# LOG_LEVEL=info kaprese run -b flint-1 spearmint-1 -e saver
+```
+
+## 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](https://github.com/kupl/starlab-benchmarks)     |
+| `ocaml`    | OCaml benchmarks in [kupl/starlab-benchmarks](https://github.com/kupl/starlab-benchmarks) |
+
+### Engines
+| Engines | Repository                                               |
+| :------ | :------------------------------------------------------- |
+| `saver` | [kupl/SAVER_pubic](https://github.com/kupl/Saver_public) |
+| `cafe`  | [kupl/LearnML](https://github.com/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`:
+```python
+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** gets the benchmark image as an argument and starts build from it, e.g.,
+```dockerfile
+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.,
+```python
+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](https://github.com/kupl/kaprese/issues) or make a [pull request](https://github.com/kupl/kaprese/pulls).