  Performance bugs challenge software development, degrading performance and
wasting computational resources. Software developers invest substantial effort
in addressing these issues. Curating these performance bugs can offer valuable
insights to the software engineering research community, aiding in developing
new mitigation strategies. However, there is no large-scale open-source
performance bugs dataset available. To bridge this gap, we propose PerfCurator,
a repository miner that collects performance bug-related commits at scale.
PerfCurator employs PcBERT-KD, a 125M parameter BERT model trained to classify
performance bug-related commits. Our evaluation shows PcBERT-KD achieves
accuracy comparable to 7 billion parameter LLMs but with significantly lower
computational overhead, enabling cost-effective deployment on CPU clusters.
Utilizing PcBERT-KD as the core component, we deployed PerfCurator on a 50-node
CPU cluster to mine GitHub repositories. This extensive mining operation
resulted in the construction of a large-scale dataset comprising 114K
performance bug-fix commits in Python, 217.9K in C++, and 76.6K in Java. Our
results demonstrate that this large-scale dataset significantly enhances the
effectiveness of data-driven performance bug detection systems.
