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Code summarization, the task of generating useful comments given the code,has long been of interest. Most of the existing code summarization models aretrained and validated on widely-used code comment benchmark datasets. However,little is known about the quality of the benchmark datasets built fromreal-world projects. Are the benchmark datasets as good as expected? To bridgethe gap, we conduct a systematic research to assess and improve the quality offour benchmark datasets widely used for code summarization tasks. First, wepropose an automated code-comment cleaning tool that can accurately detectnoisy data caused by inappropriate data preprocessing operations from existingbenchmark datasets. Then, we apply the tool to further assess the data qualityof the four benchmark datasets, based on the detected noises. Finally, weconduct comparative experiments to investigate the impact of noisy data on theperformance of code summarization models. The results show that these datapreprocessing noises widely exist in all four benchmark datasets, and removingthese noisy data leads to a significant improvement on the performance of codesummarization. We believe that the findings and insights will enable a betterunderstanding of data quality in code summarization tasks, and pave the way forrelevant research and practice.

Are We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization