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Shengyu Cheng

Publications and source records attributed to Shengyu Cheng.

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Isolating Compiler Bugs through Compilation Steps Analysis

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.

cs.SE

PredicateFix: Repairing Static Analysis Alerts with Bridging Predicates

Fixing static analysis alerts in source code with Large Language Models (LLMs) is becoming increasingly popular. However, LLMs often hallucinate and perform poorly for complex and less common alerts. Retrieval-augmented generation (RAG) aims to solve this problem by providing the model with a relevant example, but existing approaches face the challenge of unsatisfactory quality of such examples. To address this challenge, we utilize the predicates in the analysis rule, which serve as a bridge between the alert and relevant code snippets within a clean code corpus, called key examples. Based on this insight, we propose an algorithm to retrieve key examples for an alert automatically, and build PredicateFix as a RAG pipeline to fix alerts from two static code analyzers: CodeQL and GoInsight. Evaluation with multiple LLMs shows that PredicateFix increases the number of correct repairs by 27.1% ~ 69.3%, significantly outperforming other baseline RAG approaches.

cs.SE

PTracer: A Linux Kernel Patch Trace Bot

We present PTracer, a Linux kernel patch trace bot based on an improved PatchNet. PTracer continuously monitors new patches in the git repository of the mainline Linux kernel, filters out unconcerned ones, classifies the rest as bug-fixing or non bug-fixing patches, and reports bug-fixing patches to the kernel experts of commercial operating systems. We use the patches in February 2019 of the mainline Linux kernel to perform the test. As a result, PTracer recommended 151 patches to CGEL kernel experts out of 5,142, and 102 of which were accepted. PTracer has been successfully applied to a commercial operating system and has the advantages of improving software quality and saving labor cost.

cs.SE