A new benchmark for 0-to-1 CLI tool generation shows state-of-the-art LLMs achieve under 43% success rate with black-box equivalence testing against real oracles.
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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.
Fine-tuning 7B code LLMs on a custom multi-file DSL dataset achieves structural fidelity of 1.00, high exact-match accuracy, and practical utility validated by expert survey and execution checks.
LLMs produce executable code only 42.55% of the time under API evolution without full documentation, improving to 66.36% with structured docs and by 11% more with reasoning strategies, yet outdated patterns persist.
LLM-generated cryptographic Rust code compiles successfully only 23% of the time and contains detectable vulnerabilities in 57% of the cases that do compile.
LLM-based SE tools lack stable ground truth and deterministic outputs, making standard evaluation assumptions invalid and requiring new approaches for reliable assessment.
citing papers explorer
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Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios
A new benchmark for 0-to-1 CLI tool generation shows state-of-the-art LLMs achieve under 43% success rate with black-box equivalence testing against real oracles.
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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.
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Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
Fine-tuning 7B code LLMs on a custom multi-file DSL dataset achieves structural fidelity of 1.00, high exact-match accuracy, and practical utility validated by expert survey and execution checks.
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When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation
LLMs produce executable code only 42.55% of the time under API evolution without full documentation, improving to 66.36% with structured docs and by 11% more with reasoning strategies, yet outdated patterns persist.
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An Empirical Security Evaluation of LLM-Generated Cryptographic Rust Code
LLM-generated cryptographic Rust code compiles successfully only 23% of the time and contains detectable vulnerabilities in 57% of the cases that do compile.
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Evaluation of LLM-Based Software Engineering Tools: Practices, Challenges, and Future Directions
LLM-based SE tools lack stable ground truth and deterministic outputs, making standard evaluation assumptions invalid and requiring new approaches for reliable assessment.