PYGGI is the lightweight and simple framework for Genetic Improvement. It helps one practice or experience GI with only few code lines by reducing the costs of implementing typical GI process such as source code manipulation and patch management.

• Python 3.5+
• srcML (optional if you want to use the XML engine on srcML translated files. example)

You can find the PYGGI's documentation here. (Currently outdated, will be updated soon!)

@inproceedings{An:2019:PLI:3338906.3341184,
 author = {An, Gabin and Blot, Aymeric and Petke, Justyna and Yoo, Shin},
 title = {PyGGI 2.0: Language Independent Genetic Improvement Framework},
 booktitle = {Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
 series = {ESEC/FSE 2019},
 year = {2019},
 isbn = {978-1-4503-5572-8},
 location = {Tallinn, Estonia},
 pages = {1100--1104},
 numpages = {5},
 url = {http://doi.acm.org/10.1145/3338906.3341184},
 doi = {10.1145/3338906.3341184},
 acmid = {3341184},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Genetic Improvement, Search-based Software Engineering},
}

The pdf file is available at link.

$ git clone https://github.com/coinse/pyggi
$ cd PYGGI

$ python setup.py install

• java

$ cd example
$ python improve_java.py --project_path ../sample/Triangle_fast_java --mode [line|tree] --epoch [EPOCH] --iter [MAX_ITER]

• python

$ cd example
$ python improve_python.py --project_path ../sample/Triangle_fast_python/ --mode [line|tree] --epoch [EPOCH] --iter [MAX_ITER]

• java

$ cd example
$ python repair_java.py --project_path ../sample/Triangle_bug_java --mode [line|tree] --epoch [EPOCH] --iter [MAX_ITER]

• python

$ cd example
$ python repair_python.py --project_path ../sample/Triangle_bug_python/ --mode [line|tree] --epoch [EPOCH] --iter [MAX_ITER]

For the Java samples (Triangle_fast_java, Triangle_bug_java), we provide the XML version of Triangle.java files translated by srcML (download). However, in the general case, you should translate the target Java, C++, or C files into XML files before initialising Program instances and provide the translated those XML files as target files.

Or, you can simply override the setup method of AbstractProgram, which is initially empty, to execute the translation command.

ex) Translating Triangle.java to Triangle.java.xml using srcML (See the context at example/improve_java.py)

class MyTreeProgram(TreeProgram):
    def setup(self):
        if not os.path.exists(os.path.join(self.tmp_path, "Triangle.java.xml")):
            self.exec_cmd("srcml Triangle.java -o Triangle.java.xml")

Then, PyGGI will manipulate the XML files using XmlEngine(in pyggi/tree/xml_engine.py) and convert it back to the original language by stripping all the XML tags before running the test command.

To run PyGGI, these files should be provided in the target directory:

1. A Configuration File (.pyggi.config) containing the information about the path to the target files that you want to improve, and the test command that can be used to calculate the fitness values of program variants during the GI process.
2. A Test Script

ex) sample/Triangle_fast_java/.pyggi.config

{
  "target_files": [
    "Triangle.java"
  ],
  "test_command": "./run.sh"
}

You can also specify the config file name in Python script if you want it be different from the default value .pyggi.config,

ex)

program = LineProgram(
    "sample/Triangle_fast_java",   # directory containing the program  
    config='.custom.pyggi.config') # config file name

or directly provide the dict type configuration when initialising Program. ex)

config = {
    "target_files": ["Triangle.java"],
    "test_command": "./run.sh"
}
program = LineProgram(
    "sample/Triangle_fast_java",
    config=config # config
)

{target_dir_path}/run.sh

ex) sample/Triangle_fast_java/run.sh

#!/bin/sh
set -e

# cd $1

rm -f *.class
javac -cp "./junit-4.10.jar" Triangle.java TriangleTest.java TestRunner.java
java -cp "./junit-4.10.jar:./" TestRunner TriangleTest

The output of the test command should be the fitness of the program (only number),

7.0

or, you can use own result parser by overriding the compute_fitness method of Program classes.

This is the example of a custom result parser from example/improve_python.py,

class MyProgram(AbstractProgram):
    def compute_fitness(self, elapsed_time, stdout, stderr):
        import re
        m = re.findall("runtime: ([0-9.]+)", stdout)
        if len(m) > 0:
            runtime = m[0]
            failed = re.findall("([0-9]+) failed", stdout)
            pass_all = len(failed) == 0
            if pass_all:
                return round(float(runtime), 3)
            else:
                raise ParseError
        else:
            raise ParseError

class MyLineProgram(LineProgram, MyProgram):
    pass

class MyTreeProgram(TreeProgram, MyProgram):
    pass

which can parse the output from pytest, such as:

======================================== test session starts ========================================
platform linux -- Python 3.6.2, pytest-3.2.3, py-1.4.34, pluggy-0.4.0
rootdir: /media/ssd/Workspace/PYGGI, inifile:
collected 4 items                                                                                    

test_triangle.py ....runtime: 0.22184443473815918


===================================== 4 passed in 0.23 seconds ======================================