arXiv subjects

Doehyun Baek

Publications and source records attributed to Doehyun Baek.

4 recordsLinked to original sources

arXiv2026

Automated Table Reproduction via Code Generation

Reproducibility is an important goal in computer science research, e.g., for artifact evaluation and to build upon experimental results of prior work. Recently, LLM agents are being used to automatically reproduce research results, but they fail to provide executable evidence of reproduction and do not consider the method of reproduction, which limits their usefulness. We present Artisan, an LLM agent that reproduces tables of numeric results, given a paper and its artifact. The approach is enabled by two key contributions: First, we frame the reproduction problem as a code generation task, enabling users to audit and re-run the resulting reproduction script independently of the agent. Second, we design automated judging mechanisms that steer the agent toward correct results without exposing them, while preventing shortcuts like copying precomputed results. To evaluate Artisan, we introduce Artisan-Bench, the first benchmark assessing the ability to generate code that reproduces research results. Artisan-Bench comprises 60 tasks derived from 23 software engineering papers. Our experiments show that Artisan is effective and efficient, with the added benefit of aiding the discovery of 20 new errors in either the paper or artifact.

cs.SE↗

arXiv2026

READU: Inconsistency-Driven Just-in-Time Detection and Repair of README Bugs

Repository-level documentation, such as READMEs, is often the first point of contact between users and a repository. When this documentation is incorrect, users may encounter runtime errors or waste their time debugging. We call such mistakes in repository-level documentation README bugs. Addressing README bugs is challenging because documentation mixes prose with code, its connection to the source of truth is loose, and finding a bug still leaves developers to craft a repair. This paper presents READU, an inconsistency-driven technique for just-in-time detection and repair of README bugs. The key insight behind READU is that README bugs often manifest as inconsistencies between documentation and another source of truth: either repository-internal facts, such as source code, or repository-external facts, such as external dependencies. READU applies a high-recall commit filter, runs internal and external consistency checkers in parallel, uses an alert judge to remove false positives, and automatically synthesizes documentation patches. On 6,000 recent commits from six popular repositories including Linux and Spring Boot, READU detects 244 true positives with 75% precision, while consuming less than $0.01 and less than one minute per commit, on average. Of these true positives, READU correctly repairs 217. We report 66 found README bugs, of which (so far) 44 are confirmed and 26 are fixed.

cs.SE↗

arXiv2025

Execution-Aware Program Reduction for WebAssembly via Record and Replay

WebAssembly (Wasm) programs may trigger bugs in their engine implementations. To aid debugging, program reduction techniques try to produce a smaller variant of the input program that still triggers the bug. However, existing execution-unaware program reduction techniques struggle with large and complex Wasm programs, because they rely on static information and apply syntactic transformations, while ignoring the valuable information offered by the input program's execution behavior. We present RR-Reduce and Hybrid-Reduce, novel execution-aware program reduction techniques that leverage execution behaviors via record and replay. RR-Reduce identifies a bug-triggering function as the target function, isolates that function from the rest of the program, and generates a reduced program that replays only the interactions between the target function and the rest of the program. Hybrid-Reduce combines a complementary execution-unaware reduction technique with RR-Reduce to further reduce program size. We evaluate RR-Reduce and Hybrid-Reduce on 28 Wasm programs that trigger a diverse set of bugs in three engines. On average, RR-Reduce reduces the programs to 1.20 percent of their original size in 14.5 minutes, which outperforms the state of the art by 33.15 times in terms of reduction time. Hybrid-Reduce reduces the programs to 0.13 percent of their original size in 3.5 hours, which outperforms the state of the art by 3.42 times in terms of reduced program size and 2.26 times in terms of reduction time. We envision RR-Reduce as the go-to tool for rapid, on-demand debugging in minutes, and Hybrid-Reduce for scenarios where developers require the smallest possible programs.

cs.PL↗

arXiv2024

Wasm-R3: Record-Reduce-Replay for Realistic and Standalone WebAssembly Benchmarks

WebAssembly (Wasm for short) brings a new, powerful capability to the web as well as Edge, IoT, and embedded systems. Wasm is a portable, compact binary code format with high performance and robust sandboxing properties. As Wasm applications grow in size and importance, the complex performance characteristics of diverse Wasm engines demand robust, representative benchmarks for proper tuning. Stopgap benchmark suites, such as PolyBenchC and libsodium, continue to be used in the literature, though they are known to be unrepresentative. Porting of more complex suites remains difficult because Wasm lacks many system APIs and extracting real-world Wasm benchmarks from the web is difficult due to complex host interactions. To address this challenge, we introduce Wasm-R3, the first record and replay technique for Wasm. Wasm-R3 transparently injects instrumentation into Wasm modules to record an execution trace from inside the module, then reduces the execution trace via several optimizations, and finally produces a replay module that is executable sandalone without any host environment - on any engine. The benchmarks created by our approach are (i) realistic, because the approach records real-world web applications, (ii) faithful to the original execution, because the replay benchmark includes the unmodified original code, only adding emulation of host interactions, and (iii) standalone, because the replay benchmarks run on any engine. Applying Wasm-R3 to web-based Wasm applications in the wild demonstrates the correctness of our approach as well as the effectiveness of our optimizations, which reduce the recorded traces by 99.53 percent and the size of the replay benchmark by 9.98 percent. We release the resulting benchmark suite of 27 applications, called Wasm-R3-Bench, to the community, to inspire a new generation of realistic and standalone Wasm benchmarks.

cs.PL↗