Large Language Models (LLMs) have been increasingly used to optimize code efficiency. Evaluating their effectiveness and further suggesting optimization opportunities often rely on high-quality tests to demonstrate the performance bottlenecks presented in the program. However, existing approaches rely on a limited set of hand-curated inputs or LLM-generated uninteresting length-stressing tests, failing to reveal more nuanced optimization opportunities. We present WEDGE, a framework for generating performance-stressing input given the program under test. WEDGE synthesizes explicit performance-characterizing constraints in the form of branch conditions to partition the programs’ execution space into performance-specific regions. When integrated with the coverage-guided fuzzer, reaching different regions introduces explicit rewards for test generation to explore inefficient implementations. Our evaluation shows that WEDGE introduces a significant slowdown compared to the tests in CodeContests and those claimed to be optimized by existing approaches. From the utility perspective, integrating our tests substantially improves the existing code optimization approaches that rely on test-driven execution feedback. We release PERFFORGE, the performance tests generated by WEDGE, to benchmark future approaches for efficient code generation at https://github.com/UChiSeclab/perfforge.

Retry---the re-execution of a task on failure---is a common mechanism to enable resilient software systems. Yet, despite its commonality and long history, retry remains difficult to implement and test.

Concurrency bugs are difficult to detect, reproduce, and diagnose, as they manifest under rare timing conditions. Recently, active delay injection has proven efficient for exposing one such type of bug --- thread-safety violations --- with low overhead, high coverage, and minimal code analysis. However, how to efficiently apply active delay injection to broader classes of concurrency bugs is still an open question.