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This content will become publicly available on June 10, 2026

Title: An Interactive Debugger for Rust Trait Errors
Compiler diagnostics for type inference failures are notoriously bad, and type classes only make the problem worse. By introducing a complex search process during inference, type classes can lead to wholly inscrutable or useless errors. We describe a system, Argus, for interactively visualizing type class inferences to help programmers debug inference failures, applied specifically to Rust’s trait system. The core insight of Argus is to avoid the traditional model of compiler diagnostics as one-size-fits-all, instead providing the programmer with different views on the search tree corresponding to different debugging goals. Argus carefully uses defaults to improve debugging productivity, including interface design (e.g., not showing full paths of types by default) and heuristics (e.g., sorting obligations based on the expected complexity of fixing them). We evaluated Argus in a user study whereN= 25 participants debugged type inference failures in realistic Rust programs, finding that participants using Argus correctly localized 2.2× as many faults and localized 3.3× faster compared to not using Argus.  more » « less
Award ID(s):
2227863
PAR ID:
10642215
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the ACM on Programming Languages
Volume:
9
Issue:
PLDI
ISSN:
2475-1421
Page Range / eLocation ID:
1293 to 1314
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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