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Ashley Sheng

dblp:426/2178 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Compilers and program optimization › code generation
instruction selection
0.912025
Scaling Instruction-Selection Verification against Authoritative ISA Semantics · Proc. ACM Program. Lang. 2025
Compilers and program optimization
verified compilation
0.912025
Scaling Instruction-Selection Verification against Authoritative ISA Semantics · Proc. ACM Program. Lang. 2025

Methods — techniques the papers use, named apart from their topics

automated verification · 0.9ISA semantics · 0.9
YearPublicationVenuePosition
2025 Scaling Instruction-Selection Verification against Authoritative ISA Semantics
abstract
Secure, performant execution of untrusted code—as promised by WebAssembly (Wasm)—requires correct compilation to native code that enforces a sandbox. Errors in instruction selection can undermine the sandbox’s guarantees, but prior verification work struggles to scale to the complexity of realistic industrial compilers. We present Arrival , an instruction-selection verifier for the Cranelift production Wasm-to-native compiler. Arrival enables end-to-end, high-assurance verification while reducing developer effort. Arrival ( 1 ) automatically reasons about chains of instruction-selection rules, thereby reducing the need for develop-er-supplied intermediate specifications, ( 2 ) introduces a lightweight, efficient method for reasoning about stateful instruction-selection rules, and ( 3 ) automatically derives high-assurance machine code specifications. Our work verifies nearly all AArch64 instruction-selection rules reachable from Wasm core. Furthermore, Arrival reduces the developer effort required: 60 % of all specifications benefit from our automation, thereby requiring 2.6 × fewer hand-written specifications than prior approaches. Arrival finds new bugs in Cranelift’s instruction selection, and it is viable for integration into production workflows.
Michael McLoughlin, Ashley Sheng, Chris Fallin, Bryan Parno, Fraser Brown, Alexa VanHattum
Proc. ACM Program. Lang.2