[Colloquium] Lefan Zhang Dissertation Defense/Aug 11, 2022

[Megan Woodward]

This is an announcement of Lefan Zhang's Dissertation Defense.
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Candidate: Lefan Zhang

Date: Thursday, August 11, 2022

Time: 1 pm CST

Location: JCL 298

Remote Location: https://uchicago.zoom.us/j/91894037113?pwd=NE45bmw5RjZsTVN5K0VwVW9CVk8wdz09<https://www.google.com/url?q=https://urldefense.com/v3/__https://uchicago.zoom.us/j/91894037113?pwd%3DNE45bmw5RjZsTVN5K0VwVW9CVk8wdz09__;!!BpyFHLRN4TMTrA!62mcg2MrZBYMbzbXzCcGeE1QnEys60lkVDtuA6JumfUkeStzMrtya-1-zOkZj6RkXmYsmHQ6W8mdYyaHAabxU1vFr-Y$&sa=D&source=calendar&ust=1659881623931095&usg=AOvVaw0Ola9bayeZJGZE_HKz4MMp>

Meeting ID: 918 9403 7113
Passcode: 444056

Title: End-User Programming in Smart Homes with Trigger-Action Programs

Abstract: End-user programming on Internet of Things (IoT) smart devices enables end-users without programming experience to automate their homes. Trigger-action programming (TAP), supported by several smart home systems, is a common approach for such end-user programming. However, it can be hard for end-users to correctly express their intention in TAP [7, 80] even under some daily automation scenarios. This thesis introduces our efforts to enhance end-users’ trigger-action programming experience. We believe that help from automated tools can be provided to users. Across several projects, we helped end-users in all stages of TAP's life cycle including TAP creation, testing and refinement. Throughout these projects, automated tools communicate with end-users with different inputs, from their manual behaviors in their daily lives to high-level safety properties that they think should hold.

We developed AutoTap, a system that lets novice users easily specify desired properties for devices and services. AutoTap translates these properties to linear temporal logic (LTL). Then it both automatically synthesizes property-satisfying TAP rules from scratch and repairs existing TAP rules [80]. We also created Trace2TAP, a novel method for automatically synthesizing TAP rules from users' past behaviors. Given that end-users vary in their automation priorities, and sometimes choose rules that seem less desirable by traditional metrics like precision and recall, Trace2TAP comprehensively synthesizes TAP rules and brings humans into the loop during automation [81]. Lastly, we designed TapDebug, a system that automatically fix TAP rules with user-specified behavioral feedback either identified from their device usage history or explicitly specified by themselves through our novel interface. In the TapDebug study, we conducted an empirical user study to discover obstacles along the TAP debugging process and evaluated how well TapDebug's automated tool helped users overcome them.

Advisors: Shan Lu

Committee Members: Shan Lu, Blase Ur, Ravi Chugh, and Michael L. Littman


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