SHF: Small: Program Analysis for Dependable Clustering

聚类分析，又名。聚类是一种机器学习技术，用于将相关或共享相似特征的实体分组在一起。聚类在医学、生物学、社会科学、机器人和地球科学中有应用，包括高风险领域，如医学图像处理或医学诊断、预测疾病相关基因或急性疾病中的资源分配。然而，目前，用户或开发人员的应用程序，包括集群没有保证的应用程序是可靠的，这就要求与使用集群获得的结果或诊断的问题，并劝阻研究人员或从业人员使用集群应用程序。这个项目将使集群实现更可靠，更容易开发，更容易修复。软件工程师将受益于更容易编程/修复/检查的实现。反过来，由此产生的软件将更加可靠，使最终用户受益。该项目将向学生和IT专业人员介绍可靠的机器学习的挑战和方法;这将使学生和专业人员更好地应对新兴的软件研究和开发挑战。目前针对少数群体和代表性不足群体的外联和支助工作将继续进行。随着人们对机器学习的兴趣越来越大，以及在软件驱动的产品中越来越多地采用机器学习实现（硬件或软件），聚类的使用将扩大。因此，集群实现必须是可靠的。 这一领域的研究人员缺乏基本聚类正确性属性的定义和有效/高效的分析来验证聚类实现的属性。本项目将通过从第一原则开始定义聚类正确性来解决上述问题，例如，程序决定论;以及通过程序分析验证正确性的构造方法。方法将包括差异执行，白盒和黑盒技术，动态切片和符号执行。这项工作的范围包括“纯软件”集群实现以及使用硬件加速的实现。使用这些工具，开发人员和研究人员将能够有效地了解集群实现行为和可靠性。研究人员将能够使用在这项工作中开发的一般原则来构建其他领域的程序分析，例如，科学计算，数值计算，高性能计算，并使用锁步执行，切片，或象征性地执行其他类别的数据密集型应用程序的工具/方法。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Cluster analysis, a.k.a. Clustering, is a machine-learning technique used to group together entities that are related or share similar characteristics. Clustering has applications in medicine, biology, social sciences, robotics, and earth sciences, including high-stakes domains such as medical image processing or medical diagnosis, predicting disease-related genes, or resource allocation in acute disease. However, currently, users or developers of applications that incorporate clustering have no assurances that the applications are reliable; this calls into question results or diagnoses obtained with the use of clustering, and discourages researchers or practitioners from using clustering applications. This project will make clustering implementations more reliable, easier to develop, and easier to fix. Software engineers will benefit from implementations that are easier to program/fix/check. In turn, the resulting software will be more reliable, benefiting end-users. The project will introduce students and IT professionals to challenges in, as well as approaches for, dependable machine learning; this will make students and professionals better equipped for tackling emerging software research and development challenges. Ongoing outreach and support efforts, to minorities and underrepresented groups, will continue. Clustering use will expand with the increasing interest in machine learning in general, and increased adoption of machine-learning implementations (hardware or software) in software-driven products. Hence it is imperative that clustering implementations be dependable. Researchers in this space lack definitions of basic clustering-correctness properties and effective/efficient analyses for verifying clustering implementations' properties. This project will address the aforementioned issues by defining clustering correctness starting from first principles, e.g., program determinism; and constructing approaches for verifying correctness via program analysis. Approaches will include differential execution, white-box as well as black-box techniques, dynamic slicing, and symbolic execution. The scope of this work includes "purely software" clustering implementations as well as implementations that use hardware acceleration. Using these tools, developers and researchers will be able to gain effective insights into clustering-implementation behavior and dependability. Researchers will be able to use the general principles introduce developed in this work to construct program analyses in other domains, e.g., scientific computing, numerical computing, high-performance computing, and use the tools/approaches for lockstep-executing, slicing, or symbolically executing other categories of data-intensive applications.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.