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Using API reference documentation like JavaDoc is an integral part ofsoftware development. Previous research introduced a grounded taxonomy thatorganizes API documentation knowledge in 12 types, including knowledge aboutthe Functionality, Structure, and Quality of an API. We study how well moderntext classification approaches can automatically identify documentationcontaining specific knowledge types. We compared conventional machine learning(k-NN and SVM) and deep learning approaches trained on manually annotated Javaand .NET API documentation (n = 5,574). When classifying the knowledge typesindividually (i.e., multiple binary classifiers) the best AUPRC was up to 87%.The deep learning and SVM classifiers seem complementary. For four knowledgetypes (Concept, Control, Pattern, and Non-Information), SVM clearly outperformsdeep learning which, on the other hand, is more accurate for identifying theremaining types. When considering multiple knowledge types at once (i.e.,multi-label classification) deep learning outperforms na\"ive baselines andtraditional machine learning achieving a MacroAUC up to 79%. We also comparedclassifiers using embeddings pre-trained on generic text corpora andStackOverflow but did not observe significant improvements. Finally, to assessthe generalizability of the classifiers, we re-tested them on a different,unseen Python documentation dataset. Classifiers for Functionality, Concept,Purpose, Pattern, and Directive seem to generalize from Java and .NET to Pythondocumentation. The accuracy related to the remaining types seems API-specific.We discuss our results and how they inform the development of tools forsupporting developers sharing and accessing API knowledge. Published article:https://doi.org/10.1145/3338906.3338943

On Using Machine Learning to Identify Knowledge in API Reference Documentation