Isolating Compiler Bugs through Compilation Steps Analysis
- URL: http://arxiv.org/abs/2510.13128v1
- Date: Wed, 15 Oct 2025 03:58:02 GMT
- Title: Isolating Compiler Bugs through Compilation Steps Analysis
- Authors: Yujie Liu, Mingxuan Zhu, Shengyu Cheng, Dan Hao,
- Abstract summary: CompSCAN is a novel compiler bug isolation technique that applies analysis over the sequence of compilation steps.<n>We evaluate CompSCAN on 185 real-world LLVM and GCC bugs.<n>Compared with ETEM and ODFL, CompSCAN achieves relative improvements of 44.51% / 50.18% / 36.24% / 24.49% over ETEM, and 31.58% / 49.12% / 44.93% / 21.78% over ODFL on those metrics.
- Score: 5.783745108952491
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Compilers are essential to software systems, and their bugs can propagate to dependent software. Ensuring compiler correctness is critical. However, isolating compiler bugs remains challenging due to the internal complexity of compiler execution. Existing techniques primarily mutate compilation inputs to generate passing and failing tests, but often lack causal analysis of internal steps, limiting their effectiveness. To address this limitation, we propose CompSCAN, a novel compiler bug isolation technique that applies analysis over the sequence of compilation steps. CompSCAN follows a three-stage process: (1) extracting the array of compilation steps that leads to the original failure, (2) identifying bug-causing steps and collecting corresponding compiler code elements, and (3) calculating suspicious scores for each code element and outputting a suspicious ranking list as the bug isolation result. We evaluate CompSCAN on 185 real-world LLVM and GCC bugs. Results show that CompSCAN outperforms state-of-the-art techniques in both effectiveness and efficiency. CompSCAN successfully isolates 50, 85, 100, and 123 bugs within the Top-1/3/5/10 ranks, respectively. Compared with ETEM and ODFL, two state-of-the-art compiler bug isolation techniques, CompSCAN achieves relative improvements of 44.51% / 50.18% / 36.24% / 24.49% over ETEM, and 31.58% / 49.12% / 44.93% / 21.78% over ODFL on those metrics. Moreover, CompSCAN runs faster on average per bug than both baselines.
Related papers
- Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All [57.23434868678603]
Live-kBench is an evaluation framework for self-evolving benchmarks that scrapes and evaluates agents on freshly discovered kernel bugs.<n> kEnv is an agent-agnostic crash-resolution environment for kernel compilation, execution, and feedback.<n>Using kEnv, we benchmark three state-of-the-art agents, showing that they resolve 74% of crashes on the first attempt.
arXiv Detail & Related papers (2026-02-02T19:06:15Z) - Isolating Compiler Faults via Multiple Pairs of Adversarial Compilation Configurations [13.835199384689645]
MultiConf is a novel approach that automatically isolates compiler faults by constructing multiple pairs of adversarial compilation configurations.<n>We evaluate MultiConf on a benchmark of 60 real-world GCC compiler bugs.<n>In particular, MultiConf successfully localizes 27 out of 60 bugs at the Top-1 file level, representing improvements of 35.0% and 28.6% over the two state-of-the-art approaches.
arXiv Detail & Related papers (2025-12-27T09:40:35Z) - Understanding Accelerator Compilers via Performance Profiling [1.1841612917872066]
Accelerator design languages (ADLs) are high-level languages that compile to hardware units.<n>We introduce Petal, a cycle-level tool for understanding how the compiler's decisions affect performance.<n>We show that Petal's cycle-level profiles can identify performance problems in existing designs.
arXiv Detail & Related papers (2025-11-24T22:40:11Z) - Context-Guided Decompilation: A Step Towards Re-executability [50.71992919223209]
Binary decompilation plays an important role in software security analysis, reverse engineering and malware understanding.<n>Recent advances in large language models (LLMs) have enabled neural decompilation, but the generated code is typically only semantically plausible.<n>We propose ICL4Decomp, a hybrid decompilation framework that leverages in-context learning (ICL) to guide LLMs toward generating re-executable source code.
arXiv Detail & Related papers (2025-11-03T17:21:39Z) - Finding Compiler Bugs through Cross-Language Code Generator and Differential Testing [4.072167151876496]
CrossLangFuzzer generates cross-language test programs with diverse type parameters and complex inheritance structures.<n>It successfully uncovered 10 confirmed bugs in the Kotlin compiler, 4 confirmed bugs in the Groovy compiler, 7 confirmed bugs in the Scala 3 compiler, 2 confirmed bugs in the Scala 2 compiler, and 1 confirmed bug in the Java compiler.
arXiv Detail & Related papers (2025-07-09T06:33:06Z) - Improving Compiler Bug Isolation by Leveraging Large Language Models [14.679589768900621]
We propose an innovative compiler bug isolation approach named AutoCBI.<n>We evaluate AutoCBI against state-of-the-art approaches (DiWi, RecBi and FuseFL) on 120 real-world bugs from the widely-used GCC and LLVM compilers.<n>Specifically, AutoCBI isolates 66.67%/69.23%, 300%/340%, and 100%/57.14% more bugs than RecBi, DiWi, and FuseFL, respectively, in the Top-1 ranked results for GCC/LLVM.
arXiv Detail & Related papers (2025-06-21T09:09:30Z) - CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System [52.048087777953064]
We propose CompileAgent, an agent framework dedicated to repo-level compilation.<n>CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution.<n>We show that our method significantly improves the compilation success rate, ranging from 10% to 71%.
arXiv Detail & Related papers (2025-05-07T08:59:14Z) - EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking [58.15568681219339]
We introduce EquiBench, a new benchmark for evaluating large language models (LLMs)<n>This task directly tests a model's ability to reason about program semantics.<n>We evaluate 19 state-of-the-art LLMs and find that in the most challenging categories, the best accuracies are 63.8% and 76.2%, only modestly above the 50% random baseline.
arXiv Detail & Related papers (2025-02-18T02:54:25Z) - ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.<n>This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
arXiv Detail & Related papers (2025-02-17T12:38:57Z) - Finding Missed Code Size Optimizations in Compilers using LLMs [1.90019787465083]
We develop a novel testing approach which combines large language models with a series of differential testing strategies.<n>Our approach requires fewer than 150 lines of code to implement.<n>To date we have reported 24 confirmed bugs in production compilers.
arXiv Detail & Related papers (2024-12-31T21:47:46Z) - DebugBench: Evaluating Debugging Capability of Large Language Models [80.73121177868357]
DebugBench is a benchmark for Large Language Models (LLMs)
It covers four major bug categories and 18 minor types in C++, Java, and Python.
We evaluate two commercial and four open-source models in a zero-shot scenario.
arXiv Detail & Related papers (2024-01-09T15:46:38Z) - Isolating Compiler Bugs by Generating Effective Witness Programs with Large Language Models [10.660543763757518]
Existing compiler bug isolation approaches convert the problem into a test program mutation problem.
We propose a new approach named LLM4CBI to utilize LLMs to generate effective test programs for compiler bug isolation.
Compared with state-of-the-art approaches over 120 real bugs from GCC and LLVM, our evaluation demonstrates the advantages of LLM4CBI.
arXiv Detail & Related papers (2023-07-02T15:20:54Z) - HDCC: A Hyperdimensional Computing compiler for classification on
embedded systems and high-performance computing [58.720142291102135]
This work introduces the name compiler, the first open-source compiler that translates high-level descriptions of HDC classification methods into optimized C code.
name is designed like a modern compiler, featuring an intuitive and descriptive input language, an intermediate representation (IR), and a retargetable backend.
To substantiate these claims, we conducted experiments with HDCC on several of the most popular datasets in the HDC literature.
arXiv Detail & Related papers (2023-04-24T19:16:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.