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Programming language documentation refers to the set of technical documentsthat provide application developers with a description of the high-levelconcepts of a language. Such documentation is essential to support applicationdevelopers in the effective use of a programming language. One of thechallenges faced by documenters (i.e., personnel that produce documentation) isto ensure that documentation has relevant information that aligns with theconcrete needs of developers. In this paper, we present an automated approachto support documenters in evaluating the differences and similarities betweenthe concrete information need of developers and the current state ofdocumentation (a problem that we refer to as the topical alignment of aprogramming language documentation). Our approach leverages semi-supervisedtopic modelling to assess the similarities and differences between the topicsof Q&A posts and the official documentation. To demonstrate the application ofour approach, we perform a case study on the documentation of Rust. Our resultsshow that there is a relatively high level of topical alignment in Rustdocumentation. Still, information about specific topics is scarce in both theQ&A websites and the documentation, particularly related topics withprogramming niches such as network, game, and database development. For othertopics (e.g., related topics with language features such as structs, patternsand matchings, and foreign function interface), information is only availableon Q&A websites while lacking in the official documentation. Finally, wediscuss implications for programming language documenters, particularly how toleverage our approach to prioritize topics that should be added to thedocumentation.

Assessing the alignment between the information needs of developers and the documentation of programming languages: A case study on Rust