Papers › On Using Machine Learning to Identify Knowledge in API Reference Documentation
On Using Machine Learning to Identify Knowledge in API Reference Documentation
Davide Fucci, Alireza Mollaalizadehbahnemiri, Walid Maalej
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Using API reference documentation like JavaDoc is an integral part of software development. Previous research introduced a grounded taxonomy that organizes API documentation knowledge in 12 types, including knowledge about the Functionality, Structure, and Quality of an API. We study how well modern text classification approaches can automatically identify documentation containing specific knowledge types. We compared conventional machine learning (k-NN and SVM) and deep learning approaches trained on manually annotated Java and .NET API documentation (n = 5,574). When classifying the knowledge types individually (i.e., multiple binary classifiers) the best AUPRC was up to 87%. The deep learning and SVM classifiers seem complementary. For four knowledge types (Concept, Control, Pattern, and Non-Information), SVM clearly outperforms deep learning which, on the other hand, is more accurate for identifying the remaining types. When considering multiple knowledge types at once (i.e., multi-label classification) deep learning outperforms na\"ive baselines and traditional machine learning achieving a MacroAUC up to 79%. We also compared classifiers using embeddings pre-trained on generic text corpora and StackOverflow but did not observe significant improvements. Finally, to assess the 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 Python documentation. The accuracy related to the remaining types seems API-specific. We discuss our results and how they inform the development of tools for supporting developers sharing and accessing API knowledge. Published article: https://doi.org/10.1145/3338906.3338943
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections