research-article

Open access

Inferring and applying type changes

Authors:

Nikolaos Tsantalis,

Timofey BryksinAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

Pages 1206 - 1218

https://doi.org/10.1145/3510003.3510115

Published: 05 July 2022 Publication History

Abstract

Developers frequently change the type of a program element and update all its references to increase performance, security, or maintainability. Manually performing type changes is tedious, error-prone, and it overwhelms developers. Researchers and tool builders have proposed advanced techniques to assist developers when performing type changes. A major obstacle in using these techniques is that the developer has to manually encode rules for defining the type changes. Handcrafting such rules is difficult and often involves multiple trial-error iterations. Given that open-source repositories contain many examples of type-changes, if we could infer the adaptations, we would eliminate the burden on developers. We introduce TC-Infer, a novel technique that infers rewrite rules that capture the required adaptations from the version histories of open source projects. We then use these rules (expressed in the Comby language) as input to existing type change tools. To evaluate the effectiveness of TC-Infer, we use it to infer 4,931 rules for 605 popular type changes in a corpus of 400K commits. Our results show that TC-Infer deduced rewrite rules for 93% of the most popular type change patterns. Our results also show that the rewrite rules produced by TC-Infer are highly effective at applying type changes (99.2% precision and 93.4% recall). To advance the existing tooling we released IntelliTC, an interactive and configurable refactoring plugin for IntelliJ IDEA to perform type changes.

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cover image ACM Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
,
Andreas Zeller
CISPA, Germany

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 05 July 2022

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ICSE '22: 44th International Conference on Software Engineering

May 21 - 29, 2022

Pennsylvania, Pittsburgh

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https://dl.acm.org/doi/10.1145/3656429
Wang RLu MYu CLai YZhang TChristakis MPradel M(2024)Automated Deep Learning Optimization via DSL-Based Source Code TransformationProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652143(479-490)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3652143
Dilhara MBellur ABryksin TDig D(2024)Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by ExampleProceedings of the ACM on Software Engineering10.1145/36437551:FSE(631-653)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643755
Islam MJha AAkhmetov INadi S(2024)Characterizing Python Library MigrationsProceedings of the ACM on Software Engineering10.1145/36437311:FSE(92-114)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643731
Zhong HMeng NRoychoudhury APaiva AAbreu RStorey M(2024)Compiler-directed Migrating API Callsite of Client CodeProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639084(1-12)Online publication date: 20-May-2024
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