Abstract
Docstring plays an important role in software development and maintanance as it is used in source code to document a specific segment of code. In dynamic language programming, docstring is usually used to annotate types of parameters and return values.
Docstrings can help developers remind the expected types of a parameter, without process of comprehending the context which is time-consuming. In this study, we propose an automatic approach to recognize potential types of a parameter from its description.
In our approach, we utilize feature selection to select useful features for classifier training. Then we adopt four different kinds of classifiers to recognize potential types and evaluate their performances using seven metrics.
We collect a dataset of 314 type descriptions from ten prevalent Python projects. Our experimental results show that, Decision Tree classifier has the best performances among four studied classifiers, whose precision, recall, F1-score, jaccard index, hamming loss, accuracy and MRR achieve 0.681, 0.548, 0.582, 0.542, 1.234, 0.432 and 0.778 respectively. Multi-layer perceptron has the weakest performances. Futher more, we discover that the performances of four classifiers achieve their best performances when select top 20% or 40% features with the highest \(\chi ^2\) statistic.
This study archive a dataset of type descriptions and propose a framework of automatically recognizing potential types of a parameter from its description.
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Acknowledgments
The work is supported by National Key R&D Program of China (2018YFB1003900), the Natural Science Foundation of Jiangsu Province of China (BK20140611), the National Natural Science Foundation of China (61872177, 61772263, 61432001), and the program B for Outstanding PhD candidate of Nanjing University.
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Luo, Y., Ma, W., Li, Y., Chen, Z., Chen, L. (2018). Recognizing Potential Runtime Types from Python Docstrings. In: Bu, L., Xiong, Y. (eds) Software Analysis, Testing, and Evolution. SATE 2018. Lecture Notes in Computer Science(), vol 11293. Springer, Cham. https://doi.org/10.1007/978-3-030-04272-1_5
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