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This study introduces TRACE, a benchmark revealing that current large language models struggle to equally trust conflicting code and documentation artifacts during reasoning tasks. The research highlights a critical asymmetry where LLMs reliably audit natural-language specifications but fail to detect subtle faults in implementation logic when documentation remains intact. For developers integrating AI into software engineering workflows, these findings underscore the urgent need for robust evaluation methods that expose such reliability gaps before deployment in correctness-critical applications.
Measuring LLM Trust Allocation Across Conflicting Software Artifacts
arXiv:2604.03447v2 Announce Type: replace-cross Abstract: LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mutually inconsistent. Existing evaluations primarily measure final outputs, leaving unclear whether a model recognized unreliable evidence, identified the faulty source, or prioritized the appropriate artifact. We introduce TRACE, a controlled method for evaluating how LLMs assess and prioritize conflicting software artifacts. TRACE constructs paired clean and perturbed versions of real-world Java method bundles by injecting known faults into the documentation, implementation, or both while holding the remaining artifacts fixed. Models then assess artifact quality, detect and localize inconsistencies, and rank the available sources by reliability. Using 22,339 valid responses from seven LLMs on 456 method bundles, we find that quality penalties are generally localized to the perturbed artifact and increase with fault severity. However, models exhibit a consistent source-origin asymmetry: they detect documentation faults at 67-94% and explicit documentation-implementation contradictions at 50-91%, but detection falls by 21-43 percentage points when only the implementation changes while documentation remains intact. Models also struggle to deprioritize faulty implementations, and confidence provides little separation between correct and incorrect judgments for six of seven models. These results show that current LLMs are not symmetric integrators of software evidence: they audit natural-language specifications more reliably than subtle implementation behavior. TRACE provides a controlled method for exposing this failure mode before LLMs are used in correctness-critical software engineering workflows.
Abstract:LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mutually inconsistent. Existing evaluations primarily measure final outputs, leaving unclear whether a model recognized unreliable evidence, identified the faulty source, or prioritized the appropriate artifact. We introduce TRACE, a controlled method for evaluating how LLMs assess and prioritize conflicting software artifacts. TRACE constructs paired clean and perturbed versions of real-world Java method bundles by injecting known faults into the documentation, implementation, or both while holding the remaining artifacts fixed. Models then assess artifact quality, detect and localize inconsistencies, and rank the available sources by reliability. Using 22,339 valid responses from seven LLMs on 456 method bundles, we find that quality penalties are generally localized to the perturbed artifact and increase with fault severity. However, models exhibit a consistent source-origin asymmetry: they detect documentation faults at 67-94% and explicit documentation-implementation contradictions at 50-91%, but detection falls by 21-43 percentage points when only the implementation changes while documentation remains intact. Models also struggle to deprioritize faulty implementations, and confidence provides little separation between correct and incorrect judgments for six of seven models. These results show that current LLMs are not symmetric integrators of software evidence: they audit natural-language specifications more reliably than subtle implementation behavior. TRACE provides a controlled method for exposing this failure mode before LLMs are used in correctness-critical software engineering workflows.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2604.03447 [cs.SE]
(or arXiv:2604.03447v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2604.03447 Focus to learn more arXiv-issued DOI via DataCite
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