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Investigating type declaration mismatches in Python


Pascarella, Luca; Ram, Achyudh; Nadeem, Azqa; Bisesser, Dinesh; Knyazev, Norman; Bacchelli, Alberto (2018). Investigating type declaration mismatches in Python. In: 2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE), Campobasso, 20 April 2018 - 20 April 2018, 43-48.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Physical Sciences > Computer Science Applications
Physical Sciences > Software
Physical Sciences > Safety, Risk, Reliability and Quality
Language:English
Event End Date:20 April 2018
Deposited On:24 Aug 2018 13:11
Last Modified:08 Apr 2020 23:51
Publisher:IEEE
ISBN:978-1-5386-5920-5
OA Status:Green
Publisher DOI:https://doi.org/10.1109/MALTESQUE.2018.8368458
Other Identification Number:merlin-id:16638
Project Information:
  • : FunderSNSF
  • : Grant IDPP00P2_170529
  • : Project TitleData-driven Contemporary Code Review

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