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Increased reproducibility of machine learning research has been a drivingforce for dramatic improvements in learning performances. The scientificcommunity further fosters this effort by including reproducibility ratings inreviewer forms and considering them as a crucial factor for the overallevaluation of papers. Accompanying source code is not sufficient to make a workreproducible. The shared codes should meet the ML reproducibility checklist aswell. This work aims to support reproducibility evaluations of papers withsource codes. We propose an end-to-end system that operates on the Readme fileof the source code repositories. The system checks the compliance of a givenReadme to a template proposed by a widely used platform for sharing sourcecodes of research. Our system generates scores based on a custom function tocombine section scores. We also train a hierarchical transformer model toassign a class label to a given Readme. The experimental results show that thesection similarity-based system performs better than the hierarchicaltransformer. Moreover, it has an advantage regarding explainability since onecan directly relate the score to the sections of Readme files.

An End-to-End System for Reproducibility Assessment of Source Code Repositories via Their Readmes