Chromium Conversations

This dataset was released as part of the following publication.

Benjamin S. Meyers, Nuthan Munaiah, Emily Prud'hommeaux, Andrew Meneely, Cecilia O. Alm, Josephine Wolff, and Pradeep Murukannaiah. A Dataset for Identifying Actionable Feedback in Collaborative Software Development. Proceedings of the 2018 Meeting for the Association for Computational Linguistics (ACL). Melbourne, Australia. http://www.aclweb.org/anthology/P18-2021

Files:

chromium_conversations.csv

This is the full dataset containing over 1.5 million comments posted by developers reviewing proposed code changes. The dataset also includes the values we calculated for all nine linguistic features (described in Section 4 of the paper cited above).

chromium_conversations_annotations.csv

This dataset is a subset of the chromium_conversations.csv dataset. It contains the data used in the classification experiment outlined in Section 5 of the paper cited above (2,994 comments automatically identified as acted-upon and 800 comments manually identified as not (known-to-be) acted-upon).

CSV Fields:

Organizational:

review_id: Unique identifier of a code review in the Chromium project. The URL https://codereview.chromium.org/ may be used to access the review online

patchset_id: Unique identifier of a code review patchset (i.e., collection of changes to the source code) associated with a review

patch_id: Unique identifier of a code review patch (i.e., individual change to the source code) associated with a patchset

file_path: The path to the file being modified in the patch

line_number: The line number in the file at which the comment was posted

posted_timestamp: The timestamp indicating when the comment was posted

author_email: The (de-identified) author of the comment

author_type: The role of the author (i.e., reviewer or developer)

Natural Language:

text: The raw natural language text of the code review comment

Linguistic Metrics:

yngve: The maximum Yngve score of sentences in the code review comment

frazier: The maximum Frazier score of sentences in the code review comment

pdensity: The Propositional Density score of the code review comment

cdensity: The Content Density score of the code review comment

pct_neg_tokens: Ratio (percentage) of total number of tokens in negative sentences to the total number of tokens in all sentences in the code review comment

pct_neu_tokens: Ratio (percentage) of total number of tokens in neutral sentences to the total number of tokens in all sentences in the code review comment

pct_pos_tokens: Ratio (percentage) of total number of tokens in positive sentences to the total number of tokens in all sentences in the code review comment

pct_nne_tokens: Ratio (percentage) of total number of tokens in non-neutral sentences to the total number of tokens in all sentences in the code review comment

min_politeness: Minimum of the politeness of sentences in the code review comment

max_politeness: Maximum of the politeness of sentences in the code review comment

min_formality: Minimum of the formality of sentences in the code review comment

max_formality: Maximum of the formality of sentences in the code review comment

num_tokens: Total number of tokens in the code review comment

num_sentences: Total number of sentences in the code review comment

has_doxastic: Binary indicator of presence of a sentence with doxastic uncertainty in the code review comment

has_epistemic: Binary indicator of presence of a sentence with epistemic uncertainty in the code review comment

has_conditional: Binary indicator of presence of a sentence with conditional uncertainty in the code review comment

has_investigative: Binary indicator of presence of a sentence with investigative uncertainty in the code review comment

has_uncertainty: Binary indicator of presence of a sentence with any uncertainty in the code review comment

Classification:

comment_type: Manual annotation of the type of code review comment between acted-upon and not (known to be) acted-upon