LLMs frequently specify library versions with known CVEs in generated code (36-56% of tasks), show low compatibility (20-63%), and converge on the same risky versions across models.
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A survey on large language models for code generation.ACM Transactions on Software Engineering and Methodology, 35 (2):1–72
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representative citing papers
SaaSBench introduces a heterogeneous benchmark for enterprise SaaS engineering and shows that state-of-the-art coding agents fail over 95% of the time before reaching deep business logic due to setup and integration problems.
RepoDoc uses a repository knowledge graph with module clustering and semantic impact propagation to generate more complete documentation 3x faster with 85% fewer tokens and handle incremental updates 73% faster than prior LLM-based tools.
CodeSpecBench shows LLMs achieve at most 20.2% pass rate on repository-level executable behavioral specification generation, revealing that strong code generation does not imply deep semantic understanding.
Merging fine-tuned models for multilingual translation fails because fine-tuning redistributes language-specific neurons rather than sharpening them, increasing representational divergence in output-generating layers.
MURPHY improves code generation pass rates by up to 6% through retrospective credit assignment on multi-turn feedback trees using max or mean reward propagation.
LPDS quantifies difficulty of logic-preserving problem variations and searches for the hardest ones, producing up to 5x larger performance drops than random sampling and better robustness gains from fine-tuning on difficult examples.
Coding agents exhibit action bias by proposing undesirable changes on already-fixed issues 35-65% of the time, and explicit reproduction instructions only partially mitigate this while creating new abstention errors.
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
RuC generates language-agnostic, grammar-based benchmarks for evaluating LLMs on RTL code completion at controllable granularities, demonstrated on SystemVerilog designs from Tiny Tapeout and a RISC-V core where Fill-in-the-Middle prompting performed best.
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
Probabilistic programs of thought let LLMs produce many program variants from one generation by building a compact probabilistic representation of the token distribution.
Continuous latent-vector compression improves BLEU scores on repository-level code tasks by up to 28.3% at 4x compression while cutting inference latency.
Babbling Suppression stops LLM code generation upon test passage to reduce token output and energy consumption by up to 65% across Python and Java benchmarks.
MICA combines incremental per-turn distance rewards and Monte Carlo returns from a shared potential function over user support states to create a mixed advantage signal that enables stable multi-turn RL optimization for emotional support dialogues.
Frontier LLMs like GPT-5.2 show large accuracy drops on perturbed program-output prediction tasks while open-source reasoning models remain more stable, exposing limits in code semantics understanding.
Iterative refinement boosts LLM success in generating quantum solvers that match classical results, but more advanced models shift from execution errors to hard-to-detect numerical inaccuracies.
Empirical study identifies patterns in how model classes respond to structured prompts, optimization, and other techniques across two Verilog benchmarks.
A research roadmap analyzing the current state of search-based software engineering with foundation models, outlining challenges and directions across three integration aspects.
A research proposal for three studies on multi-agent LLM pair programming that externalizes intent and uses automated validation to increase trustworthiness.
citing papers explorer
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Correct Code, Vulnerable Dependencies: A Large Scale Measurement Study of LLM-Specified Library Versions
LLMs frequently specify library versions with known CVEs in generated code (36-56% of tasks), show low compatibility (20-63%), and converge on the same risky versions across models.
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SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering
SaaSBench introduces a heterogeneous benchmark for enterprise SaaS engineering and shows that state-of-the-art coding agents fail over 95% of the time before reaching deep business logic due to setup and integration problems.
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RepoDoc: A Knowledge Graph-Based Framework to Automatic Documentation Generation and Incremental Updates
RepoDoc uses a repository knowledge graph with module clustering and semantic impact propagation to generate more complete documentation 3x faster with 85% fewer tokens and handle incremental updates 73% faster than prior LLM-based tools.
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CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation
CodeSpecBench shows LLMs achieve at most 20.2% pass rate on repository-level executable behavioral specification generation, revealing that strong code generation does not imply deep semantic understanding.
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One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging
Merging fine-tuned models for multilingual translation fails because fine-tuning redistributes language-specific neurons rather than sharpening them, increasing representational divergence in output-generating layers.
-
MURPHY: Feedback-Aware GRPO with Retrospective Credit Assignment for Multi-Turn Code Generation
MURPHY improves code generation pass rates by up to 6% through retrospective credit assignment on multi-turn feedback trees using max or mean reward propagation.
-
LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling
LPDS quantifies difficulty of logic-preserving problem variations and searches for the hardest ones, producing up to 5x larger performance drops than random sampling and better robustness gains from fine-tuning on difficult examples.
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Coding Agents Don't Know When to Act
Coding agents exhibit action bias by proposing undesirable changes on already-fixed issues 35-65% of the time, and explicit reproduction instructions only partially mitigate this while creating new abstention errors.
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Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
-
RuC: HDL-Agnostic Rule Completion Benchmark Generation
RuC generates language-agnostic, grammar-based benchmarks for evaluating LLMs on RTL code completion at controllable granularities, demonstrated on SystemVerilog designs from Tiny Tapeout and a RISC-V core where Fill-in-the-Middle prompting performed best.
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HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
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Probabilistic Programs of Thought
Probabilistic programs of thought let LLMs produce many program variants from one generation by building a compact probabilistic representation of the token distribution.
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On the Effectiveness of Context Compression for Repository-Level Tasks: An Empirical Investigation
Continuous latent-vector compression improves BLEU scores on repository-level code tasks by up to 28.3% at 4x compression while cutting inference latency.
-
Babbling Suppression: Making LLMs Greener One Token at a Time
Babbling Suppression stops LLM code generation upon test passage to reduce token output and energy consumption by up to 65% across Python and Java benchmarks.
-
MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue
MICA combines incremental per-turn distance rewards and Monte Carlo returns from a shared potential function over user support states to create a mixed advantage signal that enables stable multi-turn RL optimization for emotional support dialogues.
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How Robustly do LLMs Understand Execution Semantics?
Frontier LLMs like GPT-5.2 show large accuracy drops on perturbed program-output prediction tasks while open-source reasoning models remain more stable, exposing limits in code semantics understanding.
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Can LLMs Solve Science or Just Write Code? Evaluating Quantum Solver Generation
Iterative refinement boosts LLM success in generating quantum solvers that match classical results, but more advanced models shift from execution errors to hard-to-detect numerical inaccuracies.
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VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
Empirical study identifies patterns in how model classes respond to structured prompts, optimization, and other techniques across two Verilog benchmarks.
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Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
A research roadmap analyzing the current state of search-based software engineering with foundation models, outlining challenges and directions across three integration aspects.
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From Helpful to Trustworthy: LLM Agents for Pair Programming
A research proposal for three studies on multi-agent LLM pair programming that externalizes intent and uses automated validation to increase trustworthiness.
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