Past research provided evidence that developers making code changes sometimes omit to update the related documentation, thus creating inconsistencies that may contribute to faults and crashes. In dynamically typed languages, such as Python, an inconsistency in the documentation may lead to a mismatch in type declarations only visible at runtime.
With our study, we investigate how often the documentation is inconsistent in a sample of 239 methods from five Python open- source software projects. Our results highlight that more than 20% of the comments are either partially defined or entirely missing and that almost 1% of the methods in the analyzed projects contain type inconsistencies. Based on these results, we create a tool, PyID, to early detect type mismatches in Python documentation and we evaluate its performance with our oracle.
Original languageEnglish
Title of host publicationMachine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)
StateAccepted/In press - 2018

ID: 40300814