Tool to extract the function definitions from the source code

It can be used to extract functions from,

• C

• C++

• C#

• Java

• Python

• TypeScript

• JavaScript

Advantage of using such function extractions are,

• Resolving technical debt

• Identify function similarity

• Identify pattern check (Supresswarnings, Assert, etc...)

• python 3.8 : 64 bit

• python packages (xlrd, xlsxwriter, pandas)

• third party packages [Ctags, grep]

INSTALL.md

pip install functiondefextractor

• General usage with out options.

from functiondefextractor import core_extractor
out_put = core_extractor.extractor (r"path_to_repo/code")
print(out_put)

• To exclude specific files from repository.

from functiondefextractor import core_extractor
out_put = core_extractor.extractor (r"path_to_repo/code", exclude=r'*\test\*,*.java')
print(out_put)

Sample regex patterns: Note: Space given after comma(,) in regex pattern is also treated as part of the pattern. For example

(*.java, *.cpp) != (*.java,*.cpp)

1. '*.java' =>  to exclude all java files in a repository.

2. '*/test/*' => to exclude test folder and files in it.

3. '*/src/*/*.cpp' => to exclude all cpp files in src and it's sub directories

• To extract functions based on annotation.

from functiondefextractor import core_extractor
out_put = core_extractor.extractor (r"path_to_repo/code", annot="@Test")
print(out_put)

• To extract delta lines(+/-) from code based on annotation/key word. Note: If user is unaware of complete annotation use this(annot with delta) feature to extract functions else use the above feature. Suggested to use delta=0 to get only line with annotation.

from functiondefextractor import core_extractor
out_put = core_extractor.extractor(r"path_to_repo/code", annot="@SupressWarning", delta="5")
print(out_put)

• To analyse various patterns in the code based on given condition. For example to search assert, suppress warnings patterns.

from functiondefextractor import condition_checker
out_put = core_extractor.check_condition("@SupressWarning", r"path_to_excelfile/dataframe", "(")
print(out_put[0], out_put[1])

• General usage with out options to extract functions from repo.

>>>python -m functiondefextractor --p "path/to/repo"

• To extract functions from repo having specific annotation.

>>>python -m functiondefextractor --p "path/to/repo" --a "@SuppressWarnings(\"UnusedReturnValue\")"

Note: If annotation contains double quotes as part of annotation(like above example) use backslash() before double quote inside annotation.

• To ignore files from repo using regex pattern.

>>>python -m functiondefextractor --p "path/to/repo" --i '*.java, *.cpp'

• To analyse various patterns in the code based on given condition.

>>>python -m functiondefextractor --c "Assert" --e "path/to/excel" --s "("

• Help option can be found at,

>>>python -m functiondefextractor --h

• To extract all functions from a repository

>>>python -m functiondefextractor --p "path/to/repo"

from functiondefextractor import core_extractor
out_put = core_extractor.extractor (r"path_to_repo/code")
print(out_put)

• To extract all functions with "@Test" annotation excluding all ".cpp" files in the repository

>>>python -m functiondefextractor --p "path/to/repo" --a "@Test" --i '*.cpp'

from functiondefextractor import core_extractor
out_put = core_extractor.extractor(r"path_to_repo/code", annot="@Test", exclude=r'*.cpp')
print(out_put)

Note:

1. functionstartwith argument can be used to specifically extract code from required functions whose names starts with "test_" or what ever name user is interested in.

2. delta and annot arguments together can be used to extract required number of lines below and above the given annotation/keyword.

• To analyze various patterns present in extracted code

>>>python -m functiondefextractor --c "Assert" --e "path/to/excel" --s "("

from functiondefextractor import condition_checker
out_put = core_extractor.check_condition("@SupressWarning", r"path_to_excelfile/dataframe", "(")
print(out_put[0], out_put[1])

• Executing functiondefextractor to extract functions from command line would generate an output excel file which contains FileName_FunctionName in Unique ID column and extracted functions in Code column

• Using functiondefextractor to extract functions from code would return a dataframe with same content as excel file.

• When functiondefextractor is executed from script to analyse patterns in code, a tuple with 2 data frames would be generated which contains the requested pattern statements with their count in various functions and a pivot table of the same respectively.

MAINTAINERS.md

License.md