Synthesizing API usage examples

By Raymond P. L. Buse, Westley Weimer. 2012

192 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems, Code Generation & Program Synthesis

How researchers see this paper

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In Computer Networks and Communications

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Citations in the corpus

A Survey of Machine Learning for Big Code and Naturalness (2017, background): “ware engineering problems. Mining common API patterns is a recurring theme and there is a large literature of non-probabilistic methods (e.g. frequency-based) for mining and synthesizing API patterns [34, 192], which are out-of-scope of this review. Also out-of-scopeis work that combinesnatural lang”
Learning API Usages from Bytecode: A Statistical Approach (2015, methodology): “[7] R. P. L. Buse and W. Weimer.”
Aroma: code recommendation via structural code search (2018, methodology): “The work by Buse and Weimer [2012] synthesizes API usage examples through data flow analysis, clustering and pattern abstraction.”
Autofolding for Source Code Summarization (2014, methodology): “Work in this area includes synthesis of API usage examples [37], extraction of API usage patterns [38], [39], and generation of natural language summaries for source code [12], [13].”
Graph-Based Statistical Language Model for Code (2015, unknown): “There exist deterministic approaches to improve code completion/suggestion and code search by using recent editing history [29], [30], cloned code [31], developers’ editing history [32], API usages, examples, and documentation [33], [34], [35], [36], [37], [38], structural context [39], parameter fi”
Parameter-free probabilistic API mining across GitHub (2015, methodology): “Buse and Weimer [10] generate API usage documentation by extracting and synthesizing code examples from API method call graphs annotated with control ﬂow information.”
Intelligent Code Completion with Bayesian Networks (2015, methodology): “Buse and Weimer use data-ﬂow analysis to extract single type usages [Buse and Weimer 2012].”
Parameter-Free Probabilistic API Mining at GitHub Scale (2015, methodology): “Buse and Weimer [10] generate API usage documentation by extracting and synthesizing code examples from API method call graphs annotated with control flow information.”
Learning to rank code examples for code search engines (2017, background): “Similar to earlier studies on learningto-rank or code search [12][34], we ask assessors to assign a relevance label to each pair of query and candidate answer.”
Emergent, crowd-scale programming practice in the IDE (2014, methodology): “While other projects have crowdsourced documentation for existing library functions [28, 8], mined code to enable query-based searching for patterns or examples [26, 33], or embedded example-finding tools into an IDE [5, 7, 15, 29], Codex augments traditional data mining techniques with crowds, pres”
A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, methodology): “Several approaches use the data structures such as pairs, sets, trees, and graphs to model abstractions in code and then detect patterns in API usages and examples [41, 47, 44, 14, 39].”