Speculative decoding accelerates LLM inference on SE tasks without accuracy loss, with model-based methods suiting code generation and model-free methods suiting repository-level repair and editing.
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SeGa extracts business semantics from requirements to generate unit tests that detect 22-25 more real-world business logic bugs than prior LLM-based methods in industrial Go projects.
DebugRepair improves LLM-based automated program repair by adding test semantic purification, simulated instrumentation, and debugging-driven conversational repair, fixing 224 Defects4J bugs with GPT-3.5 (26.2% above prior SOTA) and 295 with DeepSeek-V3.
DynaFix iteratively feeds execution-level dynamic information such as variable states and control flows into LLM prompts to repair 186 bugs on Defects4J, a 10% gain over baselines including 38 previously unrepaired cases.
CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.
citing papers explorer
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An Empirical Study of Speculative Decoding on Software Engineering Tasks
Speculative decoding accelerates LLM inference on SE tasks without accuracy loss, with model-based methods suiting code generation and model-free methods suiting repository-level repair and editing.
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Uncovering Business Logic Bugs via Semantics-Driven Unit Test Generation
SeGa extracts business semantics from requirements to generate unit tests that detect 22-25 more real-world business logic bugs than prior LLM-based methods in industrial Go projects.
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DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging
DebugRepair improves LLM-based automated program repair by adding test semantic purification, simulated instrumentation, and debugging-driven conversational repair, fixing 224 Defects4J bugs with GPT-3.5 (26.2% above prior SOTA) and 295 with DeepSeek-V3.
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DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information
DynaFix iteratively feeds execution-level dynamic information such as variable states and control flows into LLM prompts to repair 186 bugs on Defects4J, a 10% gain over baselines including 38 previously unrepaired cases.
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Call-Chain-Aware LLM-Based Test Generation for Java Projects
CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.