DiffCodeGen clusters code candidates by behavioral similarity from fuzzing-synthesized inputs and selects the largest cluster's medoid, matching or exceeding prior test-time scaling methods with far less token and time cost.
UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing
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2026 5representative citing papers
PickleFuzzer applies differential testing with a custom grammar to uncover 14 previously unknown discrepancies across Python pickle implementations, four of which bypass security scanners.
A systematic mapping study of 248 papers introduces a taxonomy of synergistic effects, inter-analysis workflows, and mapping functions to catalog patterns in combined program analysis techniques.
WarpL uses mutation to find and isolate suboptimal instruction sequences causing performance issues in WebAssembly runtimes by comparing machine code of original and non-problematic mutant programs.
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.
citing papers explorer
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Code Generation by Differential Test Time Scaling
DiffCodeGen clusters code candidates by behavioral similarity from fuzzing-synthesized inputs and selects the largest cluster's medoid, matching or exceeding prior test-time scaling methods with far less token and time cost.
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Combined Program Analysis Techniques: A Systematic Mapping Study
A systematic mapping study of 248 papers introduces a taxonomy of synergistic effects, inter-analysis workflows, and mapping functions to catalog patterns in combined program analysis techniques.
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Debugging Performance Issues in WebAssembly Runtimes via Mutation-based Inference
WarpL uses mutation to find and isolate suboptimal instruction sequences causing performance issues in WebAssembly runtimes by comparing machine code of original and non-problematic mutant programs.
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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.