Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"

This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020. <strong>1. data folder</strong> The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL. <strong>2. scripts floder</strong> The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms. <em>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell". <em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code". <em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell". <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>smell_search.py</em> is used for searching review comments that contain code smell terms. <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.