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Static coding benchmarks are fixed at release. Over time their tasks leak into training data, and stronger agents eventually saturate them. We argue that a benchmark should instead aim to be self-scaling: its tasks and oracles are created at evaluation time, so no content exists in advance to leak, and its difficulty i...
This work introduces Path2Spec, a divide-and-conquer framework that leverages LLMs to extract all execution paths from an input program, generates path-specific specifications for each, and merges them into a comprehensive overall specification.
A typed domain-specific language that captures recurring tensor structures, such as repeated regions and floating-point fields, through a set of reversible operators, is designed, which formulates lossless tensor compression as program synthesis.
Evaluations across agent systems, benchmarks, and backbone LLMs under 65 fault configurations show that all systems degrade under fault injection, suggesting that robustness depends on system implementation rather than model capability.
Results show that comments help code generation not merely because they are comments, but because they can provide correct solution content that prompting cannot reliably elicit.
This work introduces early outcome prediction, a complementary axis of efficiency that instead cuts cost within each task within each task, and instantiates EarlyEval, a lightweight framework that trains a pair of LightGBM success and failure classifiers over behavioral, textual, and reference-solution features and hal...
This paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face, and describes a seven-stage workflow and five process shifts, including a move toward evaluation-driven development.
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