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The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further widened in the 2020 edition of the survey. Unfortunately, despite the rapid increase in Python's popularity, there are not many testing and debugging tools that are designed for Python. This is in stark contrast with the abundance of testing and debugging tools for Java. Thus, there is a need to push research on tools that can help Python developers. One factor that contributed to the rapid growth of Java testing and debugging tools is the availability of benchmarks. A popular benchmark is the Defects4J benchmark; its initial version contained 357 real bugs from 5 real-world Java programs. Each bug comes with a test suite that can expose the bug. Defects4J has been used by hundreds of testing and debugging studies and has helped to push the frontier of research in these directions. In this project, inspired by Defects4J, we create another benchmark database and tool that contain 493 real bugs from 17 real-world Python programs. We hope our benchmark can help catalyze future work on testing and debugging tools that work on Python programs.

Machine learning (ML) has gained much attention and been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle in understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects., Comment: Accepted by MSR 2023