CSR: Medium: Improving the Interface between Machine Learning and Software Systems

CSR：中：改进机器学习和软件系统之间的接口

• 批准号: 2313190
• 负责人: Shan Lu
• 金额: $ 60万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313190&HistoricalAwards=false
• 关键词: CSR; Medium; Improving; Interface; between

Machine-Learning-as-a-Service (MLaaS) is a new software paradigm that gives developers access to powerful machine learning models trained on massive data sets without requiring those developers know how to train the models or have access to the required training data. This approach makes machine learning accessible to a much wider range of software systems, but it also creates new challenges. Specifically, there is a tension between the desire to provide a very general MLaaS interface (to make it as widely applicable as possible) and the specific needs of individual applications that use MLaaS. For example, many MLaaS providers offer general object detection models as a service, which can recognize tens of thousands of different objects in a picture, but most applications require only a small subset of that capability; e.g., applications concerned with traffic only care about objects that could appear on a roadway. This project will explore this tension – to preserve the generality of MLaaS while improving the robustness, accuracy, and performance of individual applications that use these services. Specifically, the project will first create a benchmark suite of real-world applications to drive an empirical study of the software bugs that arise due to the tension between general MLaaS interfaces and specific application needs. Based on that study, the project will create a set of tools that automatically adapt software to fix inconsistencies and ambiguities that arise due to the use of general MLaaS interfaces in application-specific contexts. Finally, the project will create methods and tools for refactoring software to use additional information –including the MLaaS’s confidence in its results– that is available from MLaaS providers, but is typically ignored by software applications.Machine learning is now a major part of software systems that affect our daily lives, including transportation, medical systems, and even news distribution. The rise of MLaaS makes it even easier for non-experts to incorporate machine learning into these software systems, but it also increases the opportunities for a new class of software bugs and software failures. This project will identify and categorize the novel class of bugs that can arise from the use of MLaaS in larger software systems and create tools and methodologies to identify and fix those bugs. All benchmarks, data, and software tools developed through this project will be released as open source so that the larger community can freely benefit from this work. By improving the correctness and performance of software systems that use machine learning services, this project will not only make it easier to develop such software, but also improve the quality of people’s daily lives as the software they use will be more reliable.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.

机器学习即服务 (MLaaS) 是一种新的软件范例，使开发人员能够访问在海量数据集上训练的强大机器学习模型，而无需这些开发人员知道如何训练模型或有权访问所需的训练数据。这种方法使机器学习可以用于更广泛的软件系统，但它也带来了新的挑战，具体来说，提供非常通用的 MLaaS 接口（使其尽可能广泛适用）的愿望与实际应用之间存在着矛盾。使用的各个应用程序的特定需求例如，许多 MLaaS 提供商提供通用对象检测模型作为服务，可以识别图片中的数以万计的不同对象，但大多数应用程序只需要该功能的一小部分，例如只关心流量的应用程序；该项目将探讨这种张力，以保留 MLaaS 的通用性，同时提高使用这些服务的各个应用程序的稳健性、准确性和性能。具体来说，该项目将首先创建一个基准套件。现实世界应用程序来推动对由于一般 MLaaS 接口和特定应用程序需求之间的紧张关系而出现的软件错误进行实证研究。基于该研究，该项目将创建一套工具，自动调整软件以修复由于通用 MLaaS 接口和特定应用程序需求而出现的不一致和模糊性。最后，该项目将创建重构软件的方法和工具，以使用附加信息（包括 MLaaS 对其结果的信心）。 MLaaS 提供商，但影响通常被软件应用程序忽略。机器学习现在是我们日常生活的软件系统的主要组成部分，包括交通、医疗系统，甚至新闻发布。MLaaS 的兴起让非专家变得更加容易。将机器学习融入到这些软件系统中，但它也增加了出现新一类软件错误和软件故障的机会。该项目将识别和分类在大型软件系统中使用 MLaaS 时可能出现的新一类错误。创建工具和通过该项目开发的所有基准、数据和软件工具都将作为开源发布，以便更大的社区可以通过提高使用机器的软件系统的正确性和性能，自由地从这项工作中受益。学习服务，该项目不仅将使此类软件的开发变得更加容易，而且还可以提高人们的日常生活质量，因为他们使用的软件将更加可靠。该奖项体现了 NSF 的法定使命，经评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。