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

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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.