CNS Core: Small: Automated testing for data- and compute-intensive distributed systems through feedback-based fuzzing

CNS 核心：小型：通过基于反馈的模糊测试对数据和计算密集型分布式系统进行自动测试

• 批准号: 2140305
• 负责人: Pedro Fonseca
• 金额: $ 49.65万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140305&HistoricalAwards=false
• 关键词: CNS; Core; Small; Automated; testing

The demand for large-scale data storage and processing has made distributed systems crucial. However, programming modern distributed systems is particularly challenging because it requires correct reasoning about concurrent code running across machines, unreliable networks, software and hardware failures, and complex application logic. In practice, despite the challenges faced by developers, there are currently no good options to test distributed systems automatically and comprehensively. This project focuses on developing techniques that effectively test data- and compute-intensive distributed applications used by millions of users, running on virtually every cloud service and data center.By bringing together expertise in operating systems, distributed systems, and systems testing, this project will make contributions to both theory and practice by (1) designing and building efficient test execution engines that transparently make large distributed systems amenable to low-overhead and high-throughput execution, (2) exploring test coverage and mutation policies that are tuned to address multi-node and fault-tolerant systems, and (3) developing techniques that use the likely root cause of distributed system bugs to minimize test cases and reproduce production failures.Distributed systems are now the foundation of society's most essential services, including e-commerce, banking, financial, medical, and logistics services. This project will allow distributed system developers to build more reliable distributed systems by developing practical and high-coverage testing approaches. The testing approaches developed will prevent service downtime, data loss, system malfunctions, and business and customer trust loss across a range of multi-billion dollar industries. Furthermore, the testing systems developed will reduce developer effort and time devoted to fixing distributed systems, which are notoriously challenging for developers.All project data is stored in public sites and university storage systems to ensure safe long-term storage for at least seven years from the award conclusion or public release, whichever comes later. The data produced includes system implementations and source code, documentation, test sets, analysis datasets, and educational material. The project information will be located at https://www.cs.purdue.edu/homes/pfonseca/projects/ds-fuzzing.html.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对大规模数据存储和处理的需求使得分布式系统变得至关重要。然而，现代分布式系统的编程尤其具有挑战性，因为它需要对跨机器运行的并发代码、不可靠的网络、软件和硬件故障以及复杂的应用程序逻辑进行正确的推理。在实践中，尽管开发人员面临挑战，但目前还没有好的选择来自动和全面地测试分布式系统。该项目致力于开发有效测试数百万用户使用的数据和计算密集型分布式应用程序的技术，这些应用程序运行在几乎每个云服务和数据中心上。通过汇集操作系统、分布式系统和系统测试方面的专业知识，该项目将通过以下方式为理论和实践做出贡献：(1)设计和构建高效的测试执行引擎，透明地使大型分布式系统易于低开销和高吞吐量执行；(2)探索针对多节点和容错系统进行调整的测试覆盖和突变策略，以及(3)开发使用分布式系统错误的可能根本原因的技术，以最大限度地减少测试用例并复制生产故障。分布式系统现在是社会最基本服务的基础，包括电子商务、银行、金融、医疗和物流服务。该项目将允许分布式系统开发人员通过开发实用和高覆盖率的测试方法来构建更可靠的分布式系统。开发的测试方法将在一系列数十亿美元的行业中防止服务停机、数据丢失、系统故障以及业务和客户信任损失。此外，开发的测试系统将减少开发人员用于修复分布式系统的工作和时间，这对开发人员来说是出了名的挑战。所有项目数据都存储在公共站点和大学存储系统中，以确保从获奖结论或公开发布起至少七年的安全长期存储，无论是较晚的时间。产生的数据包括系统实现和源代码、文档、测试集、分析数据集和教育材料。项目信息将位于https://www.cs.purdue.edu/homes/pfonseca/projects/ds-fuzzing.html.This奖上，该奖项反映了国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

KIT: Testing OS-Level Virtualization for Functional Interference Bugs

• DOI: 10.1145/3575693.3575731
• 发表时间: 2023-01
• 期刊: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
• 作者: Cong Liu;Sishuai Gong;Pedro Fonseca

Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor

• DOI: 10.1145/3600006.3613148
• 发表时间: 2023-10
• 期刊: Proceedings of the 29th Symposium on Operating Systems Principles
• 作者: Sishuai Gong;Dinglan Peng;Deniz Altinbüken;Google Deepmind;Petros Maniatis