Linguistic patterns and antipatterns detection and their impact on understandability and readability of APIs

Abstract

Application Programming Interfaces (APIs) allow distributed systems to expose their functionalities. Despite well-known API design rules and guidelines (patterns), many poor design practices (antipatterns) are prevalent in APIs. This thesis aims to (1) assess the linguistic design quality of APIs and (2) evaluate the impact of patterns and antipatterns on the understandability and readability of APIs through a survey of API developers. We rely on syntactic and semantic analyses to automatically assess the design quality of APIs. Syntactic analysis involves analyzing the structure and syntax of the APIs, while semantic analysis involves analyzing API documentation, descriptions, and parameters. We found that linguistic antipatterns are prevalent in APIs. Our detection algorithms achieve an average detection accuracy of 94%. The survey confirms that adherence to linguistic patterns significantly enhances the understandability and readability of APIs. Our findings will assist API developers in improving the design quality of their APIs.