ArduinoProg: Towards automating Arduino programming

• Main Authors: IMAM NUR BANI YUSUF, DIYANAH BINTE ABDUL JAMAL, JIANG, Lingxiao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Arduino programming; code generation; deep learning; information retrieval; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8483; https://ink.library.smu.edu.sg/context/sis_research/article/9486/viewcontent/arduprog_ase23.pdf
• Institution: Singapore Management University

Writing code for Arduino poses unique challenges. A developer 1) needs hardware-specific knowledge about the interface configuration between the Arduino controller and the I/Ohardware, 2) identifies a suitable driver library for the I/O hardware, and 3) follows certain usage patterns of the driver library in order to use them properly. In this work, based on a study of real-world user queries posted in the Arduino forum, we propose ArduinoProg to address such challenges. ArduinoProg consists of three components, i.e., Library Retriever, Configuration Classifier, and Pattern Generator. Given a query, Library Retriever retrieves library names relevant to the I/O hardware identified from the query using vector-based similarity matching. Configuration Classifier predicts the interface configuration between the I/O hardware and the Arduino controller based on the method definitions of each library. Pattern Generator generates the usage pattern of a library using a sequence-to-sequence deep learning model. We have evaluated ArduinoProg using real-world queries, and our results show that the components of ArduinoProg can generate accurate and useful suggestions to guide developers in writing Arduino code. Demo video: bit.ly/3Y3aeBe Tool: https://huggingface.co/spaces/imamnurby/ArduinoProg Code and data: https://github.com/imamnurby/ArduProg