Machine learning (ML) has gained much attention and has 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 to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence 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 adopted in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

Bug localization is the task of identifying parts of the source code that needs to be changed to resolve a bug report. As this task is difficult, automatic bug localization tools have been proposed. The development and evaluation of these tools rely on the availability of high-quality bug report datasets. In 2014, Kochhar et al. identified three biases in datasets used to evaluate bug localization techniques: (1) misclassified bug report, (2) already localized bug report, and (3) incorrect ground truth file in a bug report. They reported that already localized bug reports statistically significantly and substantially impact bug localization results, and thus should be removed. However, their evaluation is still limited, as they only investigated 3 projects written in Java. In this study, we replicate the study of Kochhar et al. on the effect of biases in bug report dataset for bug localization. Further investigation on this topic is necessary as new and larger bug report datasets have been proposed without being checked for these biases. We conduct our analysis on a collection of 2,913 bug reports taken from the recently released Bugzbook dataset that fix Python files. To investigate the prevalence of the biases, we check the bias distributions. For each bias, we select and label a set of bug reports that may contain the bias and compute the proportion of bug reports in the set that exhibit the bias. We find that 5%, 23%, and 30% of the bug reports that we investigated are affected by biases 1, 2, and 3 respectively. Then, we investigate the effect of the three biases on bug localization by measuring the performance of IncBL, a recent bug localization tool, and the classical Vector Space Model (VSM) based bug localization tool, which was used in the Kochhar et al. study. Our experiment results highlight that bias 2 significantly impact the bug localization results, while bias 1 and 3 do not have a significant impact. We also find that the effect sizes of bias 2 to IncBL and VSM are different, where IncBL has a higher effect size than VSM. Our findings corroborate the result reported by Kochhar et al. and demonstrate that bias 2 not only affects the 3 Java projects investigated in their study, but also others in another programming language (i.e., Python). This highlights the need to eliminate bias 2 from the evaluation of future bug localization tools. As a by-product of our replication study, we have released a benchmark dataset, which we refer to as CAPTURED, that has been cleaned from the three biases. CAPTURED contains Python programs and therefore augments the cleaned dataset released by Kochhar et al., which only contains Java programs.

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.