arXiv (All)
open access
Towards Automated Recommendation of Steady-State Detection Algorithms for Software Performance Benchmarks
Steady-state detection is a central step in software performance benchmarking, yet different detection methods rely on different assumptions and may perform best on different execution profiles. This paper formulates steady-state detection as an algorithm-recommendation problem: given a benchmark ti
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Towards Automated Recommendation of Steady-State Detection Algorithms for Software Performance Benchmarks#1653044
- CAROL: Context-Aware Online Learning for Fuzzer Scheduling#974861
- Harnessing MPI mutations for AI error detection#1355605
- Fail-Aware and Explainable Test Oracle Prediction#363322
- Regression-Based Prediction for Task-Based Program Performance#1027257
Record · ID 1660448
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive