FMitF: Collaborative Research: User-Centered Verification and Repair of Trigger-Action Programs

University Of Chicago, Chicago IL

Investigators

Abstract

Modern data-centric systems, ranging from Internet-of-Things devices to online services, can benefit from helping people make clear their intent for how their devices and services should behave and interact with each other. Generally, this requires people to engage in some amount of end-user programming, or programming by people who are not typically trained in programming. Common examples of this include specifying that a light should only turn on when a room is occupied or that emails with certain words in the subject line should be routed into a particular folder. Trigger-action programming (TAP), which consists of "if-this-then-that" rules, is the most common model for end-user programming because it is relatively easy to write simple TAP programs. However, as the number and complexity of both rules and devices increases, TAP programs increasingly suffer from bugs and dependability problems and are hard to correct for inexperienced and trained programmers alike. This project's goal is to make TAP programming, and thus people's ability to interact with devices that act on their behalf, more robust through developing a better understanding of end users' needs and abilities to write and debug TAP programs, computational techniques to both better model user intents and suggest TAP programs that meet them, and tools that use those techniques to help people more easily create correct TAP programs. Apart from the potential benefits to people's well-being, the project will also provide educational benefits by developing course materials that increase awareness of both human aspects of, and formal methods for, programming. Further, the tangible nature of such devices and the familiarity of popular online services are a fertile domain for engaging the public and training undergraduate students, K-12 students, and early-career graduate students in the computer science research lifecycle. To accomplish these goals, the work combines techniques from formal methods, human-computer interaction, and machine learning. Contributions to formal methods include the design of systematic solutions to unique program repair, synthesis, and specification-refinement problems in the context of end-user programming. Contributions to cyber human systems include empirical studies and the design of data-driven interfaces for more accurately expressing intent. Specifically, the empirical human subjects studies seek to understand and improve the debugging process for trigger-action programming, create and distribute needed data sets of user-centric collections of trigger-action programs, and comparatively evaluate proposed interfaces. The interfaces developed in this work use data-driven methods to help users pinpoint and understand bugs in trigger-action programs, as well as to choose among candidates for automatically repaired trigger-action programs. Underlying these interfaces will be formal models of trigger-action programs, which are verified against specified properties written in linear temporal logic. The system developed will systematically synthesize program repairs, taking into account users' experiences and preferences. The system will also use a combination of machine learning and formal methods to automatically generate trigger-action programs and summarize specifications based on historical traces of user interaction with the system. In sum, helping non-technical users accurately communicate their intent through trigger-action programming benefits widely deployed end-user-programming systems for integrating internet-connected devices and online services. 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.