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Dataset . 2019
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Mining Rule Violations in JavaScript Code Snippets

Authors: Ferreira Campos, Uriel; Smethurst, Guilherme; Moraes, João Pedro; Bonifácio, Rodrigo; Pinto, Gustavo;

Mining Rule Violations in JavaScript Code Snippets

Abstract

Content of this repository This is the repository that contains the scripts and dataset for the MSR 2019 mining challenge Github Repository with the software used : here. ======= DATASET The dataset was retrived utilizing google bigquery and dumped to a csv file for further processing, this original file with no treatment is called jsanswers.csv, here we can find the following information : 1. The Id of the question (PostId) 2. The Content (in this case the code block) 3. the lenght of the code block 4. the line count of the code block 5. The score of the post 6. The title A quick look at this files, one can notice that a postID can have multiple rows related to it, that's how multiple codeblocks are saved in the database. Filtered Dataset: Extracting code from CSV We used a python script called "ExtractCodeFromCSV.py" to extract the code from the original csv and merge all the codeblocks in their respective javascript file with the postID as name, this resulted in 336 thousand files. Running ESlint Due to the single threaded nature of ESlint, we needed to create a script to run ESlint because it took a huge toll on the machine to run it on 336 thousand files, this script is named "ESlintRunnerScript.py", it splits the files in 20 evenly distributed parts and runs 20 processes of esLinter to generate the reports, as such it generates 20 json files. Number of Violations per Rule This information was extracted using the script named "parser.py", it generated the file named "NumberofViolationsPerRule.csv" which contains the number of violations per rule used in the linter configuration in the dataset. Number of violations per Category As a way to make relevant statistics of the dataset, we generated the number of violations per rule category as defined in the eslinter website, this information was extracted using the same "parser.py" script. Individual Reports This information was extracted from the json reports, it's a csv file with PostID and violations per rule. Rules The file Rules with categories contains all the rules used and their categories.

Related Organizations
Keywords

MSR2019, stack overflow, Javascript, Data Mining, Software Engineering

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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