Stress-testing algorithms: generating new test instances to elicit insights

压力测试算法：生成新的测试实例以得出见解

• 批准号: FL140100012
• 负责人: Prof Kate Smith-Miles
• 金额: $ 196.73万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Australian Laureate Fellowships
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-12-08 至 2020-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FL140100012
• 关键词: Stress; testing; algorithms; generating; new

Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorithm testing for combinatorial optimisation, described as 'ground-breaking,' this project aims to tackle the challenges needed to generalise the paradigm to other fields such as machine learning, forecasting, software testing, and other branches of optimisation. An online repository of test instances and tools aim to provide a valuable resource to improve research practice and support new insights into algorithm performance.

压力测试算法：生成新的测试实例以获得见解。该项目旨在开发算法测试的新范例，创建新的测试实例和工具，以深入了解算法的优缺点。我们迫切需要这样的进步来支持学术界的良好研究实践，并在实践中部署算法时避免灾难。该项目扩展了我们最近在组合优化算法测试方面的工作，被称为“开创性”，旨在解决将范式推广到其他领域所需的挑战，如机器学习、预测、软件测试和其他优化分支。测试实例和工具的在线存储库旨在提供有价值的资源，以改进研究实践并支持对算法性能的新见解。