DeviceTalk: A No-Code Low-Code IoT Device Code Generation

Abstract

The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent system. To resolve this issue, a low-code or no-code (LCNC) approach has been purposed to automate code generation. As traditional LCNC solutions are highly generic, they tend to generate excess code and instructions, which will lack efficiency in terms of storage and processing. Fortunately, optimization of automated code generation can be achieved for IoT by taking advantage of the IoT characteristics. An IoT-based distributed intelligent system consists of the device domain (IoT devices) and the network domain (IoT server). The software of an IoT device in the device domain consists of the Device Application (DA) and the Sensor Application (SA). Most IoT LCNC approaches provide code generation in the network domain. Very few approaches automatically generate the DA code. To our knowledge, no approach supports the SA code generation. In this paper, we propose DeviceTalk, an LCNC environment for the DA and the SA code development. DeviceTalk automatically generates the code for IoT devices to speed up the software development in the device domain for a distributed intelligent system. We propose the DeviceTalk architecture, design and implementation of the code generation mechanism for the IoT devices. Then, we show how a developer can use the DeviceTalk Graphical User Interface (GUI) to exercise LCNC development of the device software.

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

“…Our Python MBPP pass@1 rates for Codex are slightly higher than what is reported in [35] (60.3% compared to 58.1%). [35] prompts with a function signature and docstring, even though the original MBPP problems do not include function signatures; we also include function signatures, which we infer from the provided test cases.…”

“…We evaluate a more recent Codex model (codedavinci-002) than the original paper and observe a large improvement on Python: a pass@1 rate of 45.9%, compared to 28.8% reported earlier [1]. Our pass@1 rate on the Python HumanEval subset replicates what is reported for code-davinci-002 in [35]. 10…”

“…10. We note that [35] also reports results for InCoder and a monolingual version of CodeGen, but with sampling differences that make the pass@1 rates difficult to compare. when compared to the HIGH category (p = 0.006; p < 0.001; p = 0.002), we observe that Codex performs very well on some LOW and NICHE languages.…”

CATtalk: An IoT-Based Interactive Art Development Platform

“…If so, the DSgen subsystem automatically generates the DA and pops up the DSgen GUI (Figure 9 (10)) to guide the artist to create the SA. For the details of the DA generation, the reader is referred to [21]. The SA pseudo code for SwingLight is listed in Figure 10, where the black lines without yellow marks are a general template already developed in CATtalk.…”

“…CATtalk also automatically generates the SA/DA of the art devices controlled by Raspberry Pi and Arduinobased microcontrollers. The details can be found in [21] [22].…”