This page provides the data set for the paper "Automated Localization for Unreproducible Builds". Here is a locally built version in which the figures are clear.
Reproducibility is the ability of recreating identical binaries under pre-defined build environments. Due to the need of quality assurance and the benefit of better detecting attacks against build environments, the practice of reproducible builds has gained popularity in many open-source software repositories such as Debian and Bitcoin. However, identifying the unreproducible issues remains a labour intensive and time consuming challenge, because of the lacking of information to guide the search and the diversity of the causes that may lead to the unreproducible binaries.
In this paper we propose an automated framework called RepLoc to localize the problematic files for unreproducible builds. RepLoc features a query augmentation component that utilizes the information extracted from the build logs, and a heuristic rule-based filtering component that narrows the search scope. By integrating the two components with a weighted file ranking module, RepLoc is able to automatically produce a ranked list of files that are helpful in locating the problematic files for the unreproducible builds. We have implemented a prototype and conducted extensive experiments over 671 real-world unreproducible Debian packages in four different categories. By considering the topmost ranked file only, RepLoc achieves an accuracy rate of 47.09%. If we expand our examination to the top ten ranked files in the list produced by RepLoc, the accuracy rate becomes 79.28%. Considering that there are hundreds of source code, scripts, Makefiles, etc., in a package, RepLoc significantly reduces the scope of localizing problematic files. Furthermore, with the help of RepLoc, we successfully identified and fixed six new unreproducible packages from the Debian and the Guix repositories.
In this study, we construct the data set by mining the Debian repository.
To help replicate the experimental results, we make the data publicly available. (old version of data set, being updated soon)
- Timestamps: dataset, ground truth, diff log
- Locale: dataset, ground truth, diff log
- File-ordering: dataset, ground truth, diff log
- Randomness: dataset, ground truth, diff log
In case the above links do not work, try this link