cs.SE · 2026-09-23 · No. 122
Software Engineering, 2026-09-23.
5 new papers in cs.SE. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
5 entries-
01
Metrics Failure in LLM-Based Code Vulnerability Repair: An Empirical Study and a Change-Aware Screen
Om Nepal, Sushant Aryal, Oluseyi Olukola, Nick Rahimi
cs.SE · cs.AI · cs.CR
Large language models (LLMs) are increasingly applied to the automated repair of C/C++ security vulnerabilities, and compile rate is a commonly reported proxy for progress: whether the generated patch compiles. We argue that compile rate is a scientifically unreliable metric for single-function vulnerability repair, and we support this with five controlled experiments over 203 vulnerable functions from Big-Vul, three open-source code LLMs...
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02
TraceVIC: Causal Reasoning over Code Evolution for Identifying Vulnerability-Inducing Commits
Fnu Tanish, Samiha Shimmi, Samikshya Chapagain, Hamed Okhravi, Mona Rahimi, Lei Zhang
cs.SE · cs.AI
Software vulnerabilities are often discovered long after they are introduced, making it difficult to identify the vulnerability-inducing commit (VIC) responsible for introducing the underlying vulnerable condition. Existing VIC identification techniques largely rely on git blame to trace vulnerable code through revision history and use positional heuristics, such as selecting its earliest or most recent modification. However, the true VIC may...
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03
FeatLens: Feature-Guided Dynamic Code Graph Construction and Retrieval for Repository-Level Code Generation
Xutian Li, Bo Xiong, Yifeng Zhu, Kunze Li, Xianlin Zhao, Runbang Yan, Yanzhen Zou, Lu Zhang, Bing Xie
cs.SE · cs.AI
Recent code generation research has moved from isolated function completion toward repository-level generation in existing codebases. To implement a target function correctly, an LLM must identify reusable repository dependencies such as existing functions, APIs, and cross-file definitions. Existing retrieval methods provide such context through code similarity search, persistent whole-repository graphs, or LLM-driven graph exploration, but...
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04
On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality
Xin Shen, San-Zhuo Xi, Yali Du, Ming Li
cs.SE · cs.CL · cs.LG
Recent advances in large language models (LLMs) have made them widely used for code-related tasks. Identifier names are statistically informative in naturally occurring code, but their information is not always reliable. We investigate whether current LLMs assign disproportionate weight to lexical cues when renaming preserves program structure. We introduce Face/Off, a semantics-preserving identifier-renaming framework, and evaluate...
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05
Compiling Sufficient Governance Context from Declared Losses and Reachable States: Exact Observation-Contract Synthesis with Cardinality and Cost Objectives
Gaston Besanson
cs.SE · cs.AI · cs.LO
We call the object this paper derives and certifies a minimal sufficient governance context: given a finite reachable-state model, a deterministic declared verdict, and candidate observable attributes, we compute sufficient observation sets, distinguish attributes that are individually indispensable from contracts that are jointly sufficient, and select among sufficient contracts under a cardinality or declared-cost objective. An observation...
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