Recommending reference API documentation

Robillard, Martin P.; Chhetri, Yam B.

doi:10.1007/s10664-014-9323-y

Cited by 56 publications

(23 citation statements)

References 32 publications

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“…There is another thread of relevant research on applying the NLP techniques to documents or even discussions in natural languages to infer interested properties [27], such as resource specifications [28], method specifications [29], code-document traceability [30], document evolution/reference recommendation [31], [32], API type information [33], source code descriptions [34], [35], problematic API features [36], change requests based on user reviews [37], [38], [39], [40], [41]. They demonstrated the feasibility of applying NLP techniques to documentation, but did not deal with the defect detections.…”

Section: Related Workmentioning

confidence: 99%

Analyzing APIs Documentation and Code to Detect Directive Defects

Zhou

Chen

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

101

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Abstract-Application Programming Interface (API) documents represent one of the most important references for API users. However, it is frequently reported that the documentation is inconsistent with the source code and deviates from the API itself. Such inconsistencies in the documents inevitably confuse the API users hampering considerably their API comprehension and the quality of software built from such APIs. In this paper, we propose an automated approach to detect defects of API documents by leveraging techniques from program comprehension and natural language processing. Particularly, we focus on the directives of the API documents which are related to parameter constraints and exception throwing declarations. A first-order logic based constraint solver is employed to detect such defects based on the obtained analysis results. We evaluate our approach on parts of well documented JDK 1.8 APIs. Experiment results show that, out of around 2000 API usage constraints, our approach can detect 1146 defective document directives, with a precision rate of 81.6%, and a recall rate of 82.0%, which demonstrates its practical feasibility.

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Section: Related Workmentioning

confidence: 99%

Analyzing APIs Documentation and Code to Detect Directive Defects

Zhou

Chen

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

101

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“…Although retrievability is reported as an essential quality attribute, the authors show a lack of advanced ways to retrieve Previous research tried to automatically retrieve particular knowledge from API documentation. Robillard and Chhetri [5] presented an approach to identify API-related information that developers should not ignore as well as non-critical information. Their approach-based on natural language analysis (i.e., part-of-speech tagging, word patterns)-shows 90% precision and 69% recall when applied to 1000 Java documentation units.…”

Section: Knowledge Types In Api Documentationmentioning

confidence: 99%

“…Their approach-based on natural language analysis (i.e., part-of-speech tagging, word patterns)-shows 90% precision and 69% recall when applied to 1000 Java documentation units. However, the authors needed to manually assess, on top of the sensible knowledge items, also obvious, unsurprising, and predictable documentation-i.e., what we consider Non-information [5]. Our SVM classifier, trained using simple features, identifies Non-information with 71% accuracy.…”

Section: Knowledge Types In Api Documentationmentioning

confidence: 99%

On using machine learning to identify knowledge in API reference documentation

Fucci

Mollaalizadehbahnemiri

Maalej

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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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 naïve 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/10.1145/3338906.3338943Software developers reuse software libraries and frameworks through Application Programming Interfaces (APIs). They often rely on reference documentation to identify which API elements are relevant for the task at hand, how the API can be instantiated, configured, and combined [1]. Compared to other knowledge sources, such as tutorials and Q&A portals, reference documentation like JavaDoc and PyDoc are considered the official API technical documentation. They provide detailed and fundamental information about API elements, components, operations, and structures [2, 3]. Figure 1: A reference documentation page in the JDK API annotated with the knowledge types it contains. DirectiveAs API documentation can be thousands of pages long [4,5], accessing relevant knowledge can be tedious and timeconsuming [1]. Moreover, the information necessary to accomplish a task can be scattered across the documentation pages of multiple elements, such as classes, methods, and properties. Thus, developers try to use other sources to fulfill their information needs. For example, although the Java Development Kit (JDK) API documentation contains more than 7,000 pages, as of early 2019, there are more than 3 million StackOverflow posts tagged as java.Over the last decade, sof...

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“…There are a lot of API documentations helping developers to use APIs, e.g., API specifications, API tutorials, forums, and blogs [17][18][19]25]. A series of works exist in the literature related to API documentations [26][27][28][29][30][31][32][33]. We mainly introduce three research topics, i.e.…”

Section: Related Workmentioning

confidence: 99%

An Unsupervised Approach for Discovering Relevant Tutorial Fragments for APIs

Jiang

Zhang

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

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Developers increasingly rely on API tutorials to facilitate software development. However, it remains a challenging task for them to discover relevant API tutorial fragments explaining unfamiliar APIs. Existing supervised approaches suffer from the heavy burden of manually preparing corpus-specific annotated data and features. In this study, we propose a novel unsupervised approach, namely Fragment Recommender for APIs with PageRank and Topic model (FRAPT). FRAPT can well address two main challenges lying in the task and effectively determine relevant tutorial fragments for APIs. In FRAPT, a Fragment Parser is proposed to identify APIs in tutorial fragments and replace ambiguous pronouns and variables with related ontologies and API names, so as to address the pronoun and variable resolution challenge. Then, a Fragment Filter employs a set of nonexplanatory detection rules to remove non-explanatory fragments, thus address the non-explanatory fragment identification challenge. Finally, two correlation scores are achieved and aggregated to determine relevant fragments for APIs, by applying both topic model and PageRank algorithm to the retained fragments. Extensive experiments over two publicly open tutorial corpora show that, FRAPT improves the state-of-the-art approach by 8.77% and 12.32% respectively in terms of F-Measure. The effectiveness of key components of FRAPT is also validated.

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Recommending reference API documentation

Cited by 56 publications

References 32 publications

Analyzing APIs Documentation and Code to Detect Directive Defects

Analyzing APIs Documentation and Code to Detect Directive Defects

On using machine learning to identify knowledge in API reference documentation

An Unsupervised Approach for Discovering Relevant Tutorial Fragments for APIs

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