A text classification approach to API type resolution for incomplete code snippets

The Stack Overflow Q&A platform boasts an active community of users who often include code snippets in their questions and answers. Several development tools rely on these code snippets as a source of information. Although code snippets are intended as examples for humans, they may not form comp...

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Vydáno v:Science of computer programming Ročník 227; s. 102941
Hlavní autoři: Velázquez-Rodríguez, Camilo, Di Nucci, Dario, De Roover, Coen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.04.2023
Témata:
Fully qualified name resolution
Machine learning
Stack overflow
Text classification
Fully qualified name resolution
Stack overflow
Machine learning
Text classification
ISSN:0167-6423, 1872-7964
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Popis
Shrnutí:The Stack Overflow Q&A platform boasts an active community of users who often include code snippets in their questions and answers. Several development tools rely on these code snippets as a source of information. Although code snippets are intended as examples for humans, they may not form compilation units. For instance, snippets illustrating how to use an API might lack the import statements for the corresponding API types. Thus, it becomes essential to determine the fully-qualified name of API types in incomplete snippets. We present RESICO, a machine learning-based text classification approach to resolving the simple name of API types to their fully-qualified names. RESICO is trained on a corpus of Java programs for which a compiler can determine the fully-qualified names. For four machine learning classifiers, we evaluate the type resolution accuracy of the resulting models on the original and an extended version of datasets of snippets previously used to evaluate the current state-of-the-art approach based on information retrieval. Results show that our approach outperforms the state-of-the-art one, although the training phase is slightly slower. We observe that most of the incorrect type resolutions are not due to ambiguities among the simple names for API types but due to similarities among the contexts in which these types are used, representing a future research challenge. •Stack Overflow code snippets might lack information about referenced API types.•RESICO is an approach to resolve simple API names to their fully qualified versions.•RESICO encodes API references and their contexts to train a classification algorithm.•Our approach outperforms the state-of-the-art COSTER in an in-depth evaluation.•Mispredictions by the models are mainly due to similar contexts around API usages.
ISSN:0167-6423
1872-7964
DOI:10.1016/j.scico.2023.102941