Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R

<p><strong><span>The folder organization and datasets within each are as follows:</span></strong></p> <p><strong><span>Collection Folder:</span></strong><span> the original dataset that we scraped is placed. We have removed the user names and email addresses to keep the users&rsquo; privacy.&nbsp;</span><strong><span>RQ1 Folder</span></strong><span> has three subfolders: </span></p> <p><span><span>❖<span>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><strong><span>Manual Training:</span></strong><span> The initial manually labeled data we used to initially train the classifiers is included. Note that columns A-O in this dataset are all extracted from GitHub&rsquo;s API. Column O (heading &ldquo;message&rdquo;) is the commit message itself. The following columns P and Q (heading &ldquo;author_a&rdquo; and &ldquo;author_b&rdquo;) are the final classification (upon which the Cohen Kappa was calculated). Column R (heading &ldquo;notes&rdquo;) contains some commentaries on specific cases that may be meaningful.</span></p> <p><span><span>❖<span>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><strong><span>Predicted:</span></strong><span> The results of the automatic classifiers (both 1st and 2nd round) are included. The additional columns are generated by the classifiers.</span></p> <p><span><span>❖<span>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><strong><span>Verifications</span></strong><span> contain the manually labeled data that we used as 1st and 2nd verification rounds. This is a simplified dataset with the commit&rsquo;s sha and the parsed message. The authors classified columns E and F independently and individually. The labels stated here are those that the authors agreed to (without having access to column D). Note that column D was added afterward by sha-matching by another author to calculate the Cohen Kappa. The yellow rows are those with disagreements.</span></p> <p><span>&nbsp;</span><strong><span>RQ2_RQ3 Folder</span></strong><span> contains the manually labeled dataset for RQ2 and RQ3 (SATD Types and Activities).&nbsp;</span></p> <p><span>NOTE: Kindly note that many messages or classifications are <em>multiline</em>. This means that the cells have to be expanded to be capable of reading all text included in a cell.</span></p>