Methods › General › Bot Detection › BIMAN

BIMAN

1 paper tagged archive 2025-07-28

Introduced by Tapajit Dey et al. in Detecting and Characterizing Bots that Commit Code

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

BIMAN, or Bot Identification by commit Message, commit Association, and author Name, is a technique to detect bots that commit code. It is comprised of three methods that consider independent aspects of the commits made by a particular author: 1) Commit Message: Identify if commit messages are being generated from templates; 2) Commit Association: Predict if an author is a bot using a random forest model, with features related to files and projects associated with the commits as predictors; and 3) Author Name: Match author’s name and email to common bot patterns.

PaperSource

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

The archive attaches no task to a paper tagged with this method.

Usage over time archive 2025-07-28

Papers per year tagged with BIMAN: 2020 to 2020, peak 1 1 0 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Bot Detection

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections