Eki-Taste: Synthetic Developer Interaction Logs for AI Coding Agent Preference Learning

Synthetic interaction logs designed for continuous preference learning in AI coding agents. The dataset's origin, size, and temporal coverage are unspecified. It was sourced from Kaggle, but the author, organization, and license details are unknown.

Use Cases

• Training preference models for AI coding agents based on synthetic interaction logs.
• Benchmarking continuous learning algorithms for AI assistants based on the described interaction data.
• Simulating developer-AI interaction scenarios for research based on the synthetic logs.

Strengths

• Data is explicitly designed for a specific research task: continuous preference learning for AI coding agents.
• The synthetic nature of the logs suggests controlled generation for experimental reproducibility.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle

Collection Method

Synthetically generated, as indicated by the description.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

null