Security Bug Conversations
Bibliographic record
Abstract
This dataset will be released as part of the following publication. Benjamin S. Meyers, Nuthan Munaiah, Andrew Meneely, and Emily Prud'hommeaux. <strong>Pragmatic Characteristics of Security Conversation: An Exploratory Linguistic Analysis. </strong><em>Forthcoming.</em><strong> </strong>Proceedings of the 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE 2019). Montréal, QC, Canada. <strong>Files:</strong> <pre><code>security_bug_conversations.csv</code></pre> The full dataset containing over 2.1 million comments posted by developers discussing bugs in the Chromium project. The dataset also includes the values we calculated for the five pragmatic features (described in Section 3 of the paper cited above). <strong>CSV Fields:</strong> <strong>Organizational:</strong> <em>Bug ID:</em> Unique identifier of a bug discussion in the Chromium project. The URL https://bugs.chromium.org/p/chromium/issues/detail?id=<Bug ID> may be used to access the bug online <em>Comment ID:</em> Unique identifier of a comment in a bug discussion <strong>Classification:</strong> <em>Is Security:</em> Binary indicator of whether or not a comment is part of a bug that is about security <strong>Natural Language:</strong> <em>Comment Text:</em> The raw natural language text of the bug comment <strong>Linguistic Metrics:</strong> <em>Min. Formality:</em> Minimum of the formality of sentences in the bug comment <em>Max. Formality:</em> Maximum of the formality of sentences in the bug comment <em>Max. Informativeness:</em> Maximum of the informativeness of sentences in the bug comment <em>Max. Implicature:</em> Maximum of the implicature of sentences in the bug comment <em>Min. Politeness:</em> Minimum of the politeness of sentences in the bug comment <em>Max. Politeness:</em> Maximum of the politeness of sentences in the bug comment Number of Tokens Number of Sentences <em>Has Doxastic Uncertainty:</em> Binary indicator of presence of a sentence with doxastic uncertainty in the bug comment <em>Has Epistemic Uncertainty:</em> Binary indicator of presence of a sentence with epistemic uncertainty in the bug comment <em>Has Conditional Uncertainty:</em> Binary indicator of presence of a sentence with conditional uncertainty in the bug comment <em>Has Investigational Uncertainty:</em> Binary indicator of presence of a sentence with investigational uncertainty in the bug comment <em>Has Uncertainty:</em> Binary indicator of presence of a sentence with any uncertainty in the bug comment
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.076 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".