SHF: SMALL: DockerizeME: Automatic Inference and Repair of Computing Environments

在开发或部署软件时，程序员必须确保运行应用程序所需的任何外部库或服务都已正确安装和配置。准备计算基础设施以正确执行应用程序的过程称为软件配置。目前，程序员经常手动执行软件配置，这可能导致错误和可维护性差;因此，不正确的软件配置可能会给业务造成数十亿美元的损失，导致服务意外停机，并导致关键基础设施故障和数据丢失。不幸的是，正确的软件配置所需的技能可能与软件开发正交，这意味着只有有限的程序员接受过软件配置技能的培训。本项目将开发自动执行运行任意应用程序所需的软件配置的技术。本项目将研究两项主要研究任务。其中一项任务是开发一种自动推断Dockerfile的方法，Dockerfile是Docker容器系统的配置脚本，能够执行应用程序。该方法将使用现有软件库的自动代码分析来构建能够恢复它们之间的依赖关系的离线知识库。该方法将通过挖掘现有的Dockerfiles、配置脚本和Stack Overflow等开发人员资源来学习规则，从而增强知识库。此外，该方法将应用最小化技术从这个知识库中提取的环境规格，以达到一个最小的应用程序的依赖关系。第二项任务将开发一个系统，以检测配置脚本何时与代码不兼容，例如在依赖关系升级的情况下，并使用基于搜索的技术自动修复这些配置脚本。此外，迁移学习将用于指导成功的推理和修复。最后，这些方法应用于两个应用：检测社区资源中的代码片段何时与API版本不兼容，以及构建修复机器人，该修复机器人可以自动创建用于修复配置脚本的拉取请求。该奖项反映了NSF的法定使命，并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，被认为值得支持。

When developing or deploying software, programmers must ensure that any external libraries or services which are necessary to run an application are properly installed and configured. The process of preparing computing infrastructure to properly execute an application is referred to as software configuration. Currently, programmers often manually perform software configuration, which can result in errors and poor maintainability; as a result, improper software configuration can cost billions of dollars of loss for business, lead to unexpected downtime of services, and cause failure of critical infrastructure and loss of data. Unfortunately, the skills required for proper software configuration can be orthogonal to software development, meaning there are limited programmers who are trained in software configuration skills.This project will develop techniques to automatically perform the software configuration necessary to run an arbitrary application. Two main research tasks will be investigated for this project. One task will be to develop an approach for automatically inferring a Dockerfile, a configuration script for the Docker container system, capable of executing an application. The approach will use automatic code analysis of existing software libraries to build an offline knowledge base capable of recovering the dependencies between them. The approach will augment the knowledge base with rules learned by mining existing Dockerfiles, configuration scripts, and developer resources like Stack Overflow. Further, the approach will apply minimization techniques on environment specifications extracted from this knowledge base to arrive at a minimal set of application dependencies. The second task will develop a system to detect when configuration scripts are incompatible with code, such as in the event of a dependency upgrade, and use search-based techniques to automatically repair these configuration scripts. In addition, transfer learning will be used to guide successful inferences and repairs. Finally, these approaches are applied in two applications: detecting when code snippets in community resources are incompatible with an API version, and building repair bots that can automatically create a pull request for repairing configuration scripts.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.