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

软件系统中的性能问题通常会导致负面的用户体验、广为人知的商业失败、巨额的罚款，甚至放弃数百万美元的项目。性能问题可能会导致软件系统运行的时间出乎意料地长，或者使用令人难以置信的大量计算机内存，从而可能导致计算机停机。性能问题通常与输入有关，只有在使用某些特定输入运行软件时才会发生。如果恶意攻击者发现程序的输入存在性能问题，他们可以将这些输入强制到程序上，并导致关键软件系统宕机或不可用(即，一种拒绝服务攻击)。该项目的目标是构建能够自动识别给定软件系统在其上存在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易受攻击者攻击或部署到公众之前识别软件中的性能问题。这个项目的成功完成将帮助程序员及早发现并修复软件性能问题，从而提高软件的效率、可靠性和安全性。大量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定性能分析的测试输入可用。这个项目建议开发技术来自动发现现实世界软件系统中的潜在性能问题。对于这种输入生成来说，全系统分析是必要的，但不适用于真实世界的软件系统。该项目分阶段解决这一可伸缩性挑战：一种自上而下的方法来识别可能存在性能瓶颈的组件，一种复杂的探索和泛化技术来学习最坏情况下的行为模式，以及一种自下而上的方法来为整个程序创建性能降级的输入。该技术还将使用作为项目一部分开发的新性能覆盖指标为测试中的软件系统创建性能回归测试套件。该奖项反映了NSF的法定使命，并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。

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.