Collaborative Research: SHF: Medium: Learning Semantics of Code To Automate Software Assurance Tasks

协作研究：SHF：媒介：学习代码语义以自动化软件保障任务

• 批准号: 2313055
• 负责人: Baishakhi Ray
• 金额: $ 66.6万
• 依托单位: Columbia University
• 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=2313055&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Learning

Deep learning has demonstrated great potential for accomplishing software engineering tasks. However, its capabilities are limited for challenging yet very important software assurance tasks such as bug detection, debugging, test input generation, and test suite prioritization. These tasks are hard to formulate into a learning problem. A major part of the difficulty is that these complex tasks require modeling of program semantics.  To the best of our knowledge, even state-of-the-art deep learning models have an insufficient understanding of program semantics. As a result, the models fail to achieve sufficient precision and recall to be more widely deployed. The tools do not generalize well to unseen projects and are not robust to small perturbations in source code. It also takes large amounts of computational resources and data to train the models. In this project, the team of researchers aims to improve the performance, robustness, generalizability and efficiency of deep learning models for software assurance and to enable deep learning for complex tasks that have not yet successfully used deep learning. Solutions will target encoding program semantics into the program representation by combining program analysis, software engineering, and deep learning expertise to develop novel formulations to effectively reduce software assurance problems via deep learning. The project has three research thrusts: To learn with abstract semantics, the project will study how to combine static analysis algorithms and the results from static analysis with deep learning models. To learn with concrete semantics, the project will study how to use program execution traces to guide deep learning. Finally, the project will investigate how to identify spurious features used by the current models and then apply causal learning to discourage models that have spurious features.  Research results, datasets, and tools will be disseminated to the research community, and workshops will be organized to strengthen the research community of deep learning for code.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.

深度学习已经证明了完成软件工程任务的巨大潜力。但是，它的功能受到挑战但非常重要的软件保证任务的限制，例如错误检测，调试，测试输入生成和测试套件优先级。这些任务很难使学习问题提出。困难的主要部分是这些复杂的任务需要对程序语义进行建模。据我们所知，即使是最先进的深度学习模型也对程序语义的理解不足。结果，这些模型无法实现足够的精确度，并且召回了更广泛的部署。这些工具不能很好地推广到看不见的项目，并且对源代码中的小扰动并不强大。它还需要大量的计算资源和数据来训练模型。在这个项目中，研究人员的旨在提高深度学习模型以进行软件保证的性能，鲁棒性，概括性和有效性，并为尚未成功使用深度学习的复杂任务提供深度学习。解决方案将通过结合程序分析，软件工程和深度学习专业知识来开发新颖的公式，以通过深度学习有效地减少软件保证问题，以将程序语义为目标。该项目具有三个研究作用：为了学习抽象的语义，该项目将研究如何将静态分析算法和静态分析的结果与深度学习模型相结合。要学习具体语义，该项目将研究如何使用程序执行轨迹来指导深度学习。最后，该项目将研究如何识别当前模型使用的虚假特征，然后应用因果学习来阻止具有虚假特征的模型。研究结果，数据集和工具将被传播到研究界，将组织研讨会来加强代码深度学习的研究界。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估来通过评估来获得的支持。