Automatic Detection and Repair Recommendation of Directive Defects in Java API Documentation

• Published in: IEEE transactions on software engineering Vol. 46; no. 9; pp. 1004 - 1023
• Main Authors: Zhou, Yu, Wang, Changzhi, Yan, Xin, Chen, Taolue, Panichella, Sebastiano, Gall, Harald
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; IEEE Computer Society
• Subjects: Androids; API documentation; Application programming interface; Defects; directive defects; Documentation; Drones; Empirical analysis; Evaluation; Exception handling; Facebook; Humanoid robots; Maintenance engineering; Mobile operating systems; Natural language processing; Recommender systems; Repair; repair recommendation; Software; Software development
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/TSE.2018.2872971

Application Programming Interfaces (APIs) represent key tools for software developers to build complex software systems. However, several studies have revealed that even major API providers tend to have incomplete or inconsistent API documentation. This can severely hamper the API comprehension and, as a consequence, the quality of the software built on them. In this paper, we propose DRONE ( D etect and R epair of d O cumentatio N d E fects), a framework to automatically detect and repair defects from API documents by leveraging techniques from program analysis, natural language processing, and constraint solving. Specifically, we target at the directives of API documents, which are related to parameter constraints and exception handling declarations. Furthermore, in presence of defects, we also provide a prototypical repair recommendation system. We evaluate our approach on parts of the well-documented APIs of JDK 1.8 APIs (including javaFX) and Android 7.0 (level 24). Across the two empirical studies, our approach can detect API defects with an average F-measure of 79.9, 71.7, and 81.4 percent, respectively. The API repairing capability has also been evaluated on the generated recommendations in a further experiment. User judgments indicate that the constraint information is addressed correctly and concisely in the rendered directives.