Compiler Bug Deduplication

• Compiler bug deduplication is the process of identifying, clustering, and pruning redundant bug reports to ensure each unique defect is addressed only once.
• Bisection-based and optimization-aware methods, like BugLens, significantly reduce manual triage effort by up to 64% while enhancing bug prioritization.
• Diverse techniques including analysis-based, output comparison, and LLM-assisted methods deliver scalable solutions for complex, large-scale compiler testing workflows.

Compiler bug deduplication refers to the process of identifying, clustering, and pruning redundant bug-triggering test cases or reports in compiler development and testing workflows, ensuring that each unique underlying defect is counted, triaged, and fixed only once. This is a critical task for maximizing developer efficiency, guiding triage, and accurately tracking defect density in large-scale, automated compiler validation and fuzzing environments.

1. Motivation and Challenges in Compiler Bug Deduplication

Modern compiler validation leverages large-scale automated testing and fuzzing, generating vast numbers of test cases—often thousands or millions—where many distinct programs expose the same underlying bug. For example, in a typical dataset from GCC-4.3.0, 1,235 programs exposed only 29 unique bugs, revealing a high incidence of duplicative triggers. Deduplication addresses key challenges:

• Effort Minimization: Without deduplication, developers manually triage excessive numbers of duplicate bug reports, draining resources and delaying fixes.
• Prioritization Accuracy: Unique bugs must be prioritized over repeats for resource allocation and bug fixing.
• Complexity of Miscompilation Bugs: Unlike crash bugs, which can be initially grouped by stack traces, wrong-code or miscompilation bugs often lack distinguishing signatures, complicating automated clustering.
• Scalability and Generalizability: With diverse codebases, language constructs, compilation options, and hardware memory models, deduplication must be both robust to syntactic variety and portable across toolchains.

The significance of deduplication in this domain lies in enabling efficient bug discovery curves, accurate defect monitoring, and manageable triage workloads (2506.23281).

2. Principal Deduplication Techniques

Several methodologies have been developed and empirically evaluated for compiler bug deduplication:

a. Analysis-Based Approaches

• Feature Extraction: Methods such as Tamer and D3 extract program features (e.g., AST nodes, types, operations), coverage profiles (via tools such as gcov), and execution traces or output differences to define similarity metrics for clustering (2506.23281).
• Optimization Analysis: D3 also considers which compiler optimizations must be enabled for a bug to trigger.
• Clustering Algorithms: Grouping is performed using multi-dimensional distance metrics incorporating aforementioned features.

Limitations: These methodologies incur substantial computational overhead, rely on complex external toolchains (e.g., GumTree for ASTs, tree-sitter), and are tightly coupled to specific languages and compilers (2506.23281).

b. Bisection-Based Deduplication (BugLens)

• Bisection Principle: Utilizing source control bisection (e.g., git bisect), the approach clusters test programs triggering failures at the same failure-inducing commit. Each test program is built and executed across a commit range to locate the earliest commit introducing the failure (2506.23281).
• Augmentation with Optimization Analysis: To reduce false negatives due to multi-bug commits, BugLens extends bisection by recording the minimal set of enabled compiler optimizations required to trigger each bug.
• Distance Metric: Test cases are grouped based on both the failure-inducing commit and a vector of active optimizations,

$$\mathcal{D}(p, q) = \mathcal{D}_{\text{bisect}}(p, q) + \mathcal{D}_{\text{opt}}(\mathbf{v}_p, \mathbf{v}_q)$$

with commit distance by timestamp, and optimization vector distance by Hamming similarity.

Empirical Result: BugLens significantly reduced human triage effort versus Tamer and D3, achieving effort savings ranging from 9.64% to 26.98% and requiring up to 64% fewer case examinations to cover all unique bugs (2506.23281).

c. Output Comparison and Oracle-Based Approaches

• Output Signature Deduplication: In differential testing, bugs are initially clustered by divergent observable behaviors (e.g., output differences, error messages, crash logs) between compiler versions (2401.06653).
• Test Oracles: Crash oracles (internal compiler error signatures), differential oracles (output disagreement), and metamorphic oracles (inconsistency across behavior-preserving transformations) assist in grouping triggers of identical root causes (2306.06884).

While output-delta deduplication is automated and effective for crash and some wrong-code bugs, manual confirmation is often required for nuanced miscompilations or ambiguous errors.

d. Feature and Cluster-Based Methods

• Historical Bug Feature Extraction: Clustering bug-inducing test cases by program features (e.g., statement types, language constructs) has been shown to guide both test generation and deduplication. The K-Config approach leverages feature clustering to generate new, non-redundant bug triggers, making downstream deduplication more tractable (2012.10662).

e. LLM and LLM-Assisted Deduplication

• Semantic Summarization and Reasoning: Techniques leveraging LLMs produce source file function summaries and contextualize bug triggers, ranking suspected files and clustering bug reports with similar underlying semantics (2506.17647).
• Integration of Multi-Modal Evidence: Systems such as AutoCBI use failing test programs, suspicious file summaries, coverage information, and compiler output logs within LLM prompts to facilitate both isolation and deduplication (2506.17647).

LLM-enhanced approaches enable semantic-level grouping, robust to shallow syntactic variation.

3. Integration with Bug Triage and Workflow

Bisection-based deduplication is notable for its simplicity and workflow integration:

• Triage Pipeline: The recommended workflow is to run bisection-based deduplication on all test programs, minimize only the representatives of each deduplicated group, and submit these for bug fixing. This avoids the expensive upfront minimization of all test programs (2506.23281).
• Automation: Bisection and optimization analysis can be scripted, requiring only access to the project's version control system and build infrastructure.
• Scalability: The approach remains robust even for non-minimized, large-scale fuzzing datasets, outperforming analysis-driven baselines that become infeasible due to computational cost.

A plausible implication is that deduplication strategies requiring minimized inputs or deep program analysis may be supplanted in large-scale practice by methods like BugLens, which are language- and compiler-independent and exploit standard software engineering infrastructure.

4. Comparative Evaluation and Empirical Results

The comparative evaluation of deduplication methods across real-world compiler fuzzing datasets reveals clear trends:

Approach	Key Attributes	Average Effort per Unique Bug (GCC-4.3.0)	% Effort Saved vs Tamer	% Effort Saved vs D3
BugLens (bisection + opt)	Simple, VCS-based, optimization aware	175.82	64%	61%
Tamer	Analysis: static + dynamic features	489.83	—	—
D3	Analysis: features + optimization	453.21	—	—

• On non-minimized test programs, BugLens retains effective performance while analysis-based methods fail to scale (2506.23281).
• A plausible implication is that bisection-based deduplication will be increasingly adopted for its computational tractability and generality.

5. Limitations, Extensions, and Future Directions

While bisection-based deduplication is simple and effective, it introduces known limitations:

• False Negatives: If a commit introduces or uncovers multiple independent bugs, grouping all test cases under the same commit can conflate distinct bugs. BugLens mitigates this by incorporating bug-triggering optimization vectors, but residual ambiguity can remain (2506.23281).
• Noisy Inputs: Large, unminimized test cases can confuse grouping by making it more likely (via "noise") that disparate bugs are falsely merged or that a unique bug is scattered across optimizations.
• Featureless Deduplication: Absence of deep program semantic analysis may miss subtle duplicates that only feature-aware clustering would reveal.

Proposed future work includes integrating lightweight analyses for further refinement, extending to additional compiler and language ecosystems, employing richer similarity metrics for miscompilation bugs, and updating datasets to include more contemporary compilers (2506.23281).

6. Practical Implications for Compiler Development

The practical outcome is that compiler bug deduplication, particularly when using bisection-based or hybrid methods such as BugLens, can be made:

• Simple to implement: Employing only version control bisection, standard build scripts, and basic optimization disabling.
• Language and Compiler Agnostic: Unencumbered by deep toolchain dependencies or language-specific features.
• Scalable and Generalizable: Effective for both minimized and unminimized test inputs, making it suited for both academic and industrial-scale fuzzing pipelines.
• High Precision: Empirical benchmarks substantiate major reductions in developer workload, enabling rapid identification and resolution of unique bugs and avoiding redundant triage.

Summary Table: Key Attributes of Deduplication Approaches

Approach	Basis	Requirements	Pros	Cons
Analysis-based (Tamer, D3)	Code features, coverage	Program analysis tools, minimization	Good precision (on small/minimized sets)	Slow, language-specific, poor scalability
Output comparison	Output/cmd error	Custom scripting	Fast, simple	Limited for non-crash/miscompilations
Bisection-based (BugLens)	VCS commit history, optional optimizations	Git, build infra	Simple, fast, general, robust	False negatives (multi-bug commits)
LLM/semantic approaches	Code summaries, multi-modal features	LLMs, prompt engineering	Semantic grouping, robust to noise	Resource-intensive, emergent area

Bisection, with auxiliary optimization analysis, is thus established as a practical and efficient solution for compiler bug deduplication, serving as a cornerstone for scalable bug triage and resolution in real-world compiler testing pipelines (2506.23281).