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CIDR represents a significant advancement in AI research by providing access to 832 million lines of real-world industrial code, enabling the training and evaluation of large language models on authentic enterprise software patterns. The inclusion of fine-tuning results for a 3-billion-parameter model demonstrates practical pathways for improving code intelligence tools with high-quality, anonymized datasets from diverse programming languages. This resource directly addresses critical needs in machine learning by bridging the gap between synthetic data and genuine industrial engineering practices.
CIDR: A Large-Scale Industrial Source Code Dataset for Software Engineering Research
arXiv:2605.12153v2 Announce Type: replace-cross Abstract: We present the Curated Industrial Developer Repository (CIDR), a large-scale dataset of real-world software repositories collected from industrial partners. The dataset comprises 4,225 repositories spanning 75 programming languages, totaling 832 million raw lines of code (581 million logical lines), along with structured metadata at the repository level, full version control history, and engineering-practice attributes such as continuous integration usage and the presence of automated tests. All repositories were collected, filtered, and anonymized through a multi-stage pipeline developed specifically for this purpose. We additionally report an exploratory fine-tuning study that adapts a 3-billion-parameter code language model to CIDR and quantifies the effect on held-out enterprise code. CIDR is intended to support research in code intelligence, software quality analysis, developer tooling, and related software engineering tasks. Access to CIDR is provided under a restricted license; details on eligibility and terms are available at https://fermatix.ai/#Contact.
Abstract:We present the Curated Industrial Developer Repository (CIDR), a large-scale dataset of real-world software repositories collected from industrial partners. The dataset comprises 4,225 repositories spanning 75 programming languages, totaling 832 million raw lines of code (581 million logical lines), along with structured metadata at the repository level, full version control history, and engineering-practice attributes such as continuous integration usage and the presence of automated tests. All repositories were collected, filtered, and anonymized through a multi-stage pipeline developed specifically for this purpose. We additionally report an exploratory fine-tuning study that adapts a 3-billion-parameter code language model to CIDR and quantifies the effect on held-out enterprise code. CIDR is intended to support research in code intelligence, software quality analysis, developer tooling, and related software engineering tasks. Access to CIDR is provided under a restricted license; details on eligibility and terms are available at this https URL.
60 pages, 13 figures, 8 appendices. Dataset access: this https URL. Anonymization tool: this https URL. Metadata utility: this https URL
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2605.12153 [cs.SE]
(or arXiv:2605.12153v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2605.12153 Focus to learn more arXiv-issued DOI via DataCite
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