SHF: Small: Automatic Exploration and Analysis of Software Performance Responses

SHF：小型：软件性能响应的自动探索和分析

• 批准号: 1908870
• 负责人: Koushik Sen
• 金额: $ 50万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908870&HistoricalAwards=false
• 关键词: SHF; Small; Automatic; Exploration; Analysis

Performance issues in software systems often result in negative user experience, highly publicized commercial failures, substantial monetary penalties, and even abandonment of multi-million dollar projects. Performance issues can cause a software system to run for an unexpectedly long time, or use an incredibly large amount of computer memory, potentially stalling the computer. Performance issues are often input-dependent, occurring only when the software is run with certain specific inputs. If malicious attackers find out the inputs on which programs have performance issues, they can force these inputs on the program and cause vital software systems to go down or become unavailable (i.e., a kind of denial-of-service attack). This project's goal is to build tools which can automatically identify the inputs on which a given software system has serious performance issues. These tools can then be used by programmers to identify performance issues in software before the software is vulnerable to attackers or deployed to the public. Successful completion of this project will help programmers identify and fix software performance issues early, thus increasing the efficiency, reliability, and security of software.A large body of research has focused on diagnosing performance problems by analyzing dynamically collected performance profiles. Almost all of these techniques assume the availability of test inputs for performance profiling. This project proposes to develop techniques to discover potential performance issues in real-world software systems automatically. Whole-system analysis, which is necessary for such input generation, does not scale for real-world software systems. The project addresses this scalability challenge in phases: a top-down approach to identify components that may have performance bottlenecks, a sophisticated exploration and generalization technique to learn worst-case behavior patterns, and a bottom-up approach for creating a performance-degrading input for the entire program. The technique will also create a performance regression test suite for a software system under test using a novel performance-coverage metric developed as part of the project.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.

软件系统中的性能问题通常会导致负面的用户体验，高度宣传的商业失败，大量的货币罚款，甚至放弃了数百万美元的项目。 性能问题可能会导致软件系统运行持续很长时间，或者使用大量的计算机内存，可能会使计算机停滞不前。 性能问题通常与输入有关，仅在软件运行某些特定输入时才发生。 如果恶意攻击者找出有关哪些程序出现性能问题的输入，则可以强制这些输入在程序上，并导致重要的软件系统下降或变得不可用（即一种拒绝服务攻击）。 该项目的目标是构建可以自动确定给定软件系统严重性能问题的输入的工具。 然后，程序员可以使用这些工具在软件容易受到攻击者或部署给公众之前识别软件中的性能问题。 该项目的成功完成将帮助程序员尽早识别和解决软件性能问题，从而提高软件的效率，可靠性和安全性。大量的研究集中在通过分析动态收集的性能配置文件来诊断性能问题。 几乎所有这些技术都假设了用于性能分析的测试输入的可用性。 该项目建议开发技术，以自动发现现实世界软件系统中的潜在性能问题。 全系统分析是这种输入生成所必需的，并不能扩展实际软件系统。 该项目在阶段中解决了这一可伸缩性挑战：一种自上而下的方法，用于识别可能具有性能瓶颈的组件，一种复杂的探索和概括技术，以学习最坏的行为模式，以及为整个程序创建性能降低输入的自下而上的方法。 该技术还将使用开发的新型性能覆盖度度量为项目的一部分而为正在测试的软件系统创建性能回归测试套件。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查标准来通过评估来获得支持的。

项目成果

Gauss: program synthesis by reasoning over graphs

高斯：通过图推理进行程序综合

• DOI: 10.1145/3485511
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Bavishi, Rohan;Lemieux, Caroline;Sen, Koushik;Stoica, Ion
• 通讯作者: Stoica, Ion

FUZZFACTORY: Domain-Specific Fuzzing with Waypoints

• DOI: 10.1145/3360600
• 发表时间: 2019-10-01
• 期刊: PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL
• 影响因子: 1.8
• 作者: Padhye, Rohan;Lemieux, Caroline;Vijayakumar, Hayawardh
• 通讯作者: Vijayakumar, Hayawardh

VizSmith: Automated Visualization Synthesis by Mining Data-Science Notebooks

• DOI: 10.1109/ase51524.2021.9678696
• 发表时间: 2021-11
• 期刊: 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Rohan Bavishi;Shadaj Laddad;H. Yoshida;M. Prasad;Koushik Sen

Growing a Test Corpus with Bonsai Fuzzing

• DOI: 10.1109/icse43902.2021.00072
• 发表时间: 2021-03
• 期刊: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
• 作者: Vasudev Vikram;Rohan Padhye;Koushik Sen

ObliCheck: Efficient Verification of Oblivious Algorithms with Unobservable State

• 发表时间: 2021
• 作者: Jeongseok Son;G. Prechter;Rishabh Poddar;Raluca A. Popa;Koushik Sen