Towards Understanding Performance Bugs in Popular Data Science Libraries

With the increasing demand for handling large-scale and complex data, data science (DS) applications often suffer from long execution time and rapid RAM exhaustion, which leads to many serious issues like unbearable delays and crashes in financial transactions. As popular DS libraries are frequently used in these applications, their performance bugs (PBs) are a major contributing factor to these issues, making it crucial to address them for improving overall application performance. However, PBs in popular DS libraries remain largely unexplored. To address this gap, we conducted a study of 202 PBs collected from seven popular DS libraries. Our study examined the impact of PBs and proposed a taxonomy for common root causes. We found over half of the PBs arise from inefficient data processing operations, especially within data structure. We also explored the effort required to locate their root causes and fix these bugs, along with the challenges involved. Notably, around 20% of the PBs could be fixed using simple strategies (e.g. Conditions Optimizing), suggesting the potential for automated repair approaches. Our findings highlight the severity of PBs in core DS libraries and offer insights for developing high-performance libraries and detecting PBs. Furthermore, we derived test rules from our identified root causes, identifying eight PBs, of which four were confirmed, demonstrating the practical utility of our findings.