LLMs achieve only modest understanding of HMSC formal semantics at 52 percent accuracy, performing strongly on basic constructs but weakly on abstractions and traces.
In: 2025 IEEE 22nd International Conference on Software Architecture (ICSA)
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A small recency window of 3-5 prior ADRs as context produces higher-fidelity LLM-generated Architecture Decision Records than no context, full history, or retrieval-augmented selection in typical sequential workflows.
LLM approaches ExArch and ArTEMiS reach F1 scores of 0.86 and 0.81 for architecture entity recognition and traceability, matching or approaching baselines that require manual models.
LLMs achieve 98.22% accuracy answering factual questions about ROS2 software architectures, with top models reaching 100%.
EnergyTrackr detects statistically significant energy regressions in Java commits from 3,232 changes across three projects and identifies recurring code anti-patterns such as missing early exits.
citing papers explorer
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(How) Do Large Language Models Understand High-Level Message Sequence Charts?
LLMs achieve only modest understanding of HMSC formal semantics at 52 percent accuracy, performing strongly on basic constructs but weakly on abstractions and traces.
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Context Matters: Evaluating Context Strategies for Automated ADR Generation Using LLMs
A small recency window of 3-5 prior ADRs as context produces higher-fidelity LLM-generated Architecture Decision Records than no context, full history, or retrieval-augmented selection in typical sequential workflows.
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Who's Who? LLM-assisted Software Traceability with Architecture Entity Recognition
LLM approaches ExArch and ArTEMiS reach F1 scores of 0.86 and 0.81 for architecture entity recognition and traceability, matching or approaching baselines that require manual models.
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Can Large Language Models Assist the Comprehension of ROS2 Software Architectures?
LLMs achieve 98.22% accuracy answering factual questions about ROS2 software architectures, with top models reaching 100%.
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Systematic Detection of Energy Regression and Corresponding Code Patterns in Java Projects
EnergyTrackr detects statistically significant energy regressions in Java commits from 3,232 changes across three projects and identifies recurring code anti-patterns such as missing early exits.