Mining Idioms in the Wild

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Sivaraman A. (Author) Other; Rui Abreu (Author) FEUP; Scott A. (Author) Other; Akomolede T. (Author) Other; Chandra S. (Author) Other

Conference proceedings International

Title: Proceedings - International Conference on Software Engineering

Pages: 187-196

44th ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion 2022

22 May 2022 through 27 May 2022

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-017-DKP

DOI: 10.1109/icse-seip55303.2022.9794062

Abstract (EN): Existing code repositories contain numerous instances of code patterns that are idiomatic ways of accomplishing a particular programming task. Sometimes, the programming language in use supports specific operators or APIs that can express the same idiomatic imperative code much more succinctly. However, those code patterns linger in repositories because the developers may be unaware of the new APIs or have not gotten around to them. Detection of idiomatic code can also point to the need for new APIs. We share our experiences in mining imperative idiomatic patterns from the Hack repo at Facebook. We found that existing techniques either cannot identify meaningful patterns from syntax trees or require test-suite-based dynamic analysis to incorporate semantic properties to mine useful patterns. The key insight of the approach proposed in this paper - Jezero - is that semantic idioms from a large codebase can be learned from canonicalized dataflow trees. We propose a scalable, lightweight static analysis-based approach to construct such a tree that is well suited to mine semantic idioms using nonparametric Bayesian methods. Our experiments with Jezero on Hack code show a clear advantage of adding canonicalized dataflow information to ASTs: Jezero was significantly more effective in finding new refactoring opportunities from unannotated legacy code than a baseline that did not have the dataflow augmentation.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9