Intelligent API Engineering: Systematically Leveraging APIs Through Development Knowledge and Usage Data

智能 API 工程：通过开发知识和使用数据系统地利用 API

• 批准号: RGPIN-2022-03505
• 负责人: Lamothe, Maxime
• 金额: $ 1.82万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754202
• 关键词: Intelligent; API; Engineering; Systematically; Leveraging

Software is ever more prevalent in our daily lives and businesses. In today's interconnected world, software for servers, desktops, mobile devices, wearables, and IoT devices interact using software interfaces called Application Programming Interfaces (API). Furthermore, each software program may internally employ multiple APIs to access ready made software functionality (e.g., for machine learning, and data). Because software developers increasingly rely on APIs to leverage existing software, it is becoming more important than ever to streamline how these APIs are developed. The practice of providing access to software functionality while hiding implementation details behind an API can save software development time and lower costs. However, APIs are often developed in ad-hoc ways, leaving their users at the mercy of API developers. Because API developers often have little empirical evidence on which to base their decisions, they may not reliably know how to improve their APIs. These ad-hoc development practices can give rise to API user dissatisfaction and lead to multitudes of questions from API users on open forums complaining about APIs and asking how to circumvent them. Even well known and lucrative software APIs, such as the Android API, are not immune to these challenges and suffer from user complaints. Despite advances in the automation of API usage, relying on external APIs still requires some degree of blind faith. API users must trust that the APIs that they use will be maintained and remain stable and available. However, API developers do not currently have the resources to extract API users needs at scale. Additionally, few techniques exist to monitor and analyze deployed APIs in order to maintain and improve them. Techniques, approaches, and guidelines to help develop APIs are thus required to improve API development practices. The goal of this research proposal is to improve API engineering practices through the cultivation of developer knowledge and usage tendencies. To achieve this goal, my research program is geared towards an intelligent API lifecycle based on empirical evidence. Thus, I intend to provide API development feedback for API developers by creating techniques to monitor the API usage patterns contained in large software repository data. I plan to leverage the mined data to extract actionable improvement suggestions for API developers. Finally, I propose to create a repository of traceability links between known API changes and API usages and create a scalable analytical infrastructure to allow API developers to better engineer their APIs. The results of these research activities will advance the current state of practice for API developers, resulting in better, more usable, software. Furthermore, this research proposal aims to train highly qualified personnel (HQP), preparing them to excel in a data-driven world and contribute to the state-of-the-art in software engineering research and practice in Canada.

软件在我们的日常生活和业务中越来越普遍。在当今的互联世界中，服务器、台式机、移动的设备、可穿戴设备和物联网设备的软件使用称为应用程序编程接口（API）的软件接口进行交互。此外，每个软件程序可以在内部采用多个API来访问现成的软件功能（例如，机器学习和数据）。由于软件开发人员越来越依赖API来利用现有软件，因此简化这些API的开发方式变得比以往任何时候都更加重要。提供对软件功能的访问同时将实现细节隐藏在API后面的做法可以节省软件开发时间并降低成本。然而，API通常是以特定的方式开发的，这使得用户只能任由API开发人员摆布。由于API开发人员通常没有什么经验证据来作为他们决策的基础，他们可能不知道如何改进他们的API。这些特别的开发实践可能会引起API用户的不满，并导致API用户在开放论坛上提出大量问题，抱怨API并询问如何规避它们。即使是众所周知的和利润丰厚的软件API，如Android API，也不能幸免于这些挑战，并遭受用户投诉。尽管在API使用的自动化方面取得了进步，但依赖外部API仍然需要一定程度的盲目信仰。API用户必须相信他们使用的API将得到维护，并保持稳定和可用。然而，API开发人员目前没有资源来大规模地提取API用户需求。此外，很少有技术可以监控和分析已部署的API，以维护和改进它们。因此，需要帮助开发API的技术、方法和指南来改进API开发实践。本研究计画的目标是透过开发人员知识与使用倾向的培养，来改善API工程实务。为了实现这一目标，我的研究计划是面向一个智能的API生命周期的基础上的经验证据。因此，我打算通过创建技术来监视包含在大型软件存储库数据中的API使用模式，从而为API开发人员提供API开发反馈。我计划利用挖掘的数据为API开发人员提取可操作的改进建议。最后，我建议创建一个已知API更改和API使用之间的可追溯性链接库，并创建一个可扩展的分析基础设施，以允许API开发人员更好地设计他们的API。这些研究活动的结果将推动API开发人员的实践现状，从而产生更好、更可用的软件。此外，该研究计划旨在培养高素质人才（HQP），使他们在数据驱动的世界中脱颖而出，并为加拿大最先进的软件工程研究和实践做出贡献。