ArduinoProg: Towards Automating Arduino Programming

• Published in: 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) pp. 2030 - 2033
• Main Authors: Bani Yusuf, Imam Nur, Abdul Jamal, Diyanah Binte, Jiang, Lingxiao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.09.2023
• Subjects: arduino programming; code generation; Codes; Deep learning; Hardware; information retrieval; Libraries; Natural languages; Programming; Writing
• ISSN: 2643-1572
• DOI: 10.1109/ASE56229.2023.00055

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