DIRE: A Neural Approach to Decompiled Identifier Naming

This dataset is released as a companion to the paper "DIRE: A Neural Approach to Decompiled Identifier Naming", appearing in the proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019). It contains information generated by decompiling 3,195,962 functions found in 164,632 unique binaries generated from C code scraped from GitHub. For practicality, the dataset is partitioned into 16 archives by the first hexadecimal digit of the SHA-256 hash of the binary used to generate it. Each of the 16 archives contains approximately 10,000 JSONL files, named according to a binary's hash. Each JSONL file consists of a single JSON object per-line corresponding to a single function in the decompiled binary. Archives are provided in both GZIP and BZIP2 format. See the README file for more information.