Replication Package for the Paper: "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community"

This repository contains the data and results from the paper "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community" submitted to ICPC 2021. <strong>1. "data.zip" contains the following three folders:</strong> <strong>1) data folder</strong> The data folder contains the retrieved 1,190 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL. <strong>2) scripts folder</strong> The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms. <em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead". <em>get_changes.py</em> is used for getting code changes from OpenStack. <em>get_comments.py</em> is used for getting review comments for each code change. <em>keywords_search.py</em> is used for searching review comments that contain at least one keyword. <em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword. <em>keywords_improve.py</em> is used for improving the keyword-based mining approach. <em>tools.py</em> is used for supporting the process of keywords improving. <strong>3) project folder</strong> The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download. <em>Data Labeling &amp; Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool. <em>Data Labeling &amp; Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool. <strong>2. Keywords associated with code smells.pdf</strong> This file contains the final set of keywords associated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.