Creating physical-computing systems, especially selecting correct electronic components, assembling the circuit, and implementing the program, can be challenging for novice users. In this paper, we present FritzBot, a data-driven conversational agent supporting novice users on creating physical-computing systems through natural-language interaction. FritzBot is built upon the structure of a BiLSTM-CRF (bi-directional Long Short-term Memory Network and Conditional Random Field) neural network, as a plug-in for Fritzing. The neural network is trained on a lexical circuit-event database derived from 152 students’ reports on their physical-computing course projects. By processing the user’s textual description on his/her physical-computing idea, FritzBot can extract the causal relationships between the input and the output events, identify the corresponding electronic components, and generate the Arduino-based circuit and the code along with the step-by-step construction guidelines. Our user study shows that compared to the original Arduino software and the circuit-autocompletion software available in the commercial market, FritzBot significantly shortens the time spent, reduces the perceived workload, and enhances the satisfaction/joy for inexperienced users on designing and prototyping physical-computing systems.
Chen, Taizhou, Lantian Xu, and Kening Zhu. "FritzBot: A data-driven conversational agent for physical-computing system design." International Journal of Human-Computer Studies 155 (2021): 102699.