README.md

metadata

language:
  - en
license: apache-2.0
task_categories:
  - time-series-forecasting
tags:
  - temporal-point-process
  - event-sequences
  - github
  - software-engineering
  - developer-workflows
  - marked-temporal-point-process
size_categories:
  - n<1K

GitHub User Event Streams

Curated event sequences from individual GitHub developers, designed for temporal point process (TPP) and marked temporal point process (MTPP) modeling. Each sequence captures a single developer's activity across multiple repositories over a one-week window.

Dataset Description

• Source: GH Archive raw JSON dumps
• Time window: June 1 - 7, 2024
• Grouping: Events grouped by user (actor)
• Sequences: ~382
• Sequence length: 80-100 events per sequence
• Time unit: hours
• Language: English only

Schema

Each record is a dictionary with 8 fields:

Field	Type	Description
seq_idx	int	Sequence index
seq_len	int	Number of events in the sequence
description	str	User name, active repos (with descriptions), and event window
metadata	str (JSON)	actor_login, num_repos, top_repos, time_unit
time_since_start	list[float]	Time since the first event (in time_unit, default hours)
time_since_last_event	list[float]	Time since the previous event (in time_unit, default hours)
type_event	list[str]	Event type labels (see below)
type_text	list[str]	Natural language description of each event

Event Types (8 categories)

Label	Description
issue_opened	New issue filed
issue_closed	Issue resolved/closed
pr_opened	Pull request opened
pr_merged	Pull request merged
push	Code pushed (with commit messages)
release	Release published
pr_reviewed	PR approved or changes requested
comment	Comment on issue, PR, or review

Curation Filters

Sequences are selected to represent moderately active developers working across a focused set of repositories:

Filter	Value	Description
min-raw-events	50	Min kept-type events in archive (pre-classification)
max-raw-events	300	Max kept-type events in archive (pre-classification)
min-events	80	Min classified events per sequence
max-events	100	Max classified events (skip, not truncate)
min-unique-types	5	At least 5 distinct event types
min-repos	2	Active in at least 2 repos
max-repos	5	Active in at most 5 repos
min-avg-text-len	150	Min average text length per event
max-type-fraction	0.5	No single event type >50% of events
require-english	true	All event texts must be in English (non-Latin script detection)

Additional processing:

• Bot users are excluded (detected by name patterns like [bot], dependabot, etc.)
• Consecutive duplicate events (same type + text) are deduplicated

Key Difference from Repository Events

	User Events	Repo Events
Grouping	By developer	By repository
Cross-repo	Yes (2-5 repos)	No (single repo)
Text perspective	Mentions repository name	Mentions actor name
Use case	Developer workflow modeling	Project activity modeling

Example

{
  "seq_idx": 0,
  "seq_len": 90,
  "description": "GitHub user alice: active in org/repo-a (A web framework for Rust), org/repo-b Event window: June 02 - 07, 2024.",
  "metadata": "{\"actor_login\": \"alice\", \"num_repos\": 2, \"top_repos\": [\"org/repo-a\", \"org/repo-b\"], \"time_unit\": \"hours\"}",
  "time_since_start": [0.0, 0.004, ...],
  "time_since_last_event": [0.0, 0.004, ...],
  "type_event": ["pr_reviewed", "pr_merged", "push", ...],
  "type_text": ["PR approved in org/repo-a: Add new feature...", ...]
}

Intended Use

• Training and evaluating temporal point process models
• Studying individual developer workflow patterns across repos
• Benchmarking next-event prediction and event forecasting
• Modeling marked temporal point processes with rich text marks

Citation

If you use this dataset, please cite:

@dataset{github_user_events_2024,
  title={GitHub User Event Streams},
  author={XiaoBB},
  year={2024},
  url={https://huggingface.co/datasets/XiaoBB/github_user_events},
  note={Curated from GH Archive, June 2024}
}