Assessing the Linguistic Quality of REST APIs for IoT Applications

TL;DR

This paper introduces SARAv2 and REST-Ling to analyze and detect linguistic patterns and antipatterns in REST APIs for IoT, showing that poor linguistic practices are common but detectable with high accuracy.

Contribution

It presents a novel approach and tool for syntactic and semantic analysis of IoT REST APIs, enabling effective detection of linguistic antipatterns.

Findings

Linguistic antipatterns are prevalent in IoT REST APIs.

REST-Ling detects patterns with over 80% accuracy.

Detection takes approximately 8.4 seconds per API.

Abstract

Internet of Things (IoT) is a growing technology that relies on connected 'things' that gather data from peer devices and send data to servers via APIs (Application Programming Interfaces). The design quality of those APIs has a direct impact on their understandability and reusability. This study focuses on the linguistic design quality of REST APIs for IoT applications and assesses their linguistic quality by performing the detection of linguistic patterns and antipatterns in REST APIs for IoT applications. Linguistic antipatterns are considered poor practices in the naming, documentation, and choice of identifiers. In contrast, linguistic patterns represent best practices to APIs design. The linguistic patterns and their corresponding antipatterns are hence contrasting pairs. We propose the SARAv2 (Semantic Analysis of REST APIs version two) approach to perform syntactic and semantic analyses of REST APIs for IoT applications. Based on the SARAv2 approach, we develop the REST-Ling tool and empirically validate the detection results of nine linguistic antipatterns. We analyse 19 REST APIs for IoT applications. Our detection results show that the linguistic antipatterns are prevalent and the REST-Ling tool can detect linguistic patterns and antipatterns in REST APIs for IoT applications with an average accuracy of over 80%. Moreover, the tool performs the detection of linguistic antipatterns on average in the order of seconds, i.e., 8.396 seconds. We found that APIs generally follow good linguistic practices, although the prevalence of poor practices exists.