Developers build programs based on software libraries to reduce coding effort. If a program inappropriately sets an API parameter, the program may exhibit unexpected runtime behaviors. To help developers correctly use library APIs, researchers built tools to mine API parameter rules. However, it is still unknown (1) what types of parameter rules there are, and (2) how these rules distribute inside documents and source files. In this paper, we conducted an empirical study to investigate the above-mentioned questions. To analyze as many parameter rules as possible, we took a hybrid approach that combines automatic localization of constrained parameters with manual inspection. Our automatic approach---PaRu---locates parameters that have constraints either documented in Javadoc (i.e., document rules) or implied by source code (i.e., code rules). Our manual inspection (1) identifies and categorizes rules for the located parameters, and (2) establishes mapping between document and code rules. By applying PaRu to 9 widely used libraries, we located 5,334 parameters with either document or code rules. Interestingly, there are only 187 parameters that have both types of rules, and 79 pairs of these parameter rules are unmatched. Additionally, PaRu extracted 1,688 rule sentences from Javadoc and code. We manually classified these sentences into six categories, two of which are overlooked by prior approaches. We found that 86.2% of parameters have only code rules; 10.3% of parameters have only document rules; and only 3.5% of parameters have both document and code rules. Our research reveals the challenges for automating parameter rule extraction. Based on our findings, we discuss the potentials of prior approaches and present our insights for future tool design.

@inproceedings{paperbot5615,
  title = {An empirical study on API parameter rules},
  author = {Hao Zhong and Na Meng and Zexuan Li and Li Jia},
  booktitle = {ICSE},
  year = {2020},
  doi = {10.1145/3377811.3380922}
}