MAPO: Mining and Recommending API Usage Patterns

By Hao Zhong, Tao Xie, Lu Zhang, Jian Pei, Hong Mei. European Conference on Object-Oriented Programming, 2009

433 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/fed132d312a9f618329238ac6542d0148a0ff157

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Citations in the corpus

Mining Semantic Loop Idioms from Big Code (2016, neutral): “Another related area is API mining (Acharya et al. 2007; Nguyen et al. 2009; Zhong et al. 2009; Wang et al. 2013).”
Synthesizing API usage examples (2012, methodology): “, [12], [13]), to the best of our knowledge our approach is the first to leverage type information and statement ordering.”
Learning API Usages from Bytecode: A Statistical Approach (2015, background): “MAPO [45] mined frequent API call sequences and suggests associated code examples.”
Evaluating the evaluations of code recommender systems: A reality check (2016, background): “MAPO [29] is a miner and recommender for API usage patterns.”
Learning to rank code examples for code search engines (2017, methodology): “Then the package computes the page-rank of each code snippet E using (5) (Brin and Page 1998) The context similarity refers to the similarity between the context of the query and the candidate code snippets (Zhong et al. 2009).”
Bayesian specification learning for finding API usage errors (2017, background): “Most efforts in this area [6–8, 21, 25, 35, 42, 48, 55–57] focus on qualitative specifications, typically finite automata.”
Graph-Based Statistical Language Model for Code (2015, background): “, mining frequent pairs, subsequences [11], [28], [4], item sets [10], subgraphs [5], [12], associate rules [13].”
On the localness of software (2014, background): “[50, 51] aim to predict API calls, and Zhang et al.”
A statistical semantic language model for source code (2013, background): “MAPO [22] mines API patterns and suggests associated code examples.”
Portfolio: finding relevant functions and their usage (2011, neutral): “MAPO [38] F N W,Q T”
Parameter-free probabilistic API mining across GitHub (2015, methodology): “Choosing the number of clusters/cutoff is considered somewhat of a black art and can only be reliably inferred through computationally expensive training procedures on a validation set [44, 40].”
Aroma: code recommendation via structural code search (2018, background): “MAPO [Zhong et al. 2009] recommends code examples by mining and indexing associated API usage patterns.”
Learning natural coding conventions (2014, background): “API recommenders, code suggestion and completion systems aim to help during editing, when a user may not know the name of an API she needs [63], the parameters she should pass to the API [75], or that the API call she is making needs to preceded by another [29, 70, 71, 76].”
A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, methodology): “Deterministic pattern mining methods are used, e.g., mining frequent pairs, subsequences [60, 4, 61], item sets [13], subgraphs [52, 15], association rules [37].”
Exploring API Embedding for API Usages and Applications (2017, background): “As an evidence of that, researchers have been able to mine API usage patterns, which are frequently occurring API usages, from large code corpora [1], [2].”
Mining idioms from source code (2014, background): “rogrammers ourselves, we would nd this alien and more difcult to understand. Idioms dier signicantly from previous notions of textual patterns in software, such as code clones [43] and API patterns [55]. Unlike clones, idioms commonly recur across projects, even ones from dierent domains, and unlike”
Learning to Fuzz: Application-Independent Fuzz Testing with Probabilistic, Generative Models of Input Data (2016, background): “, to find anomalies that correspond to bugs [36], for code completion [12], to recommend API usages [50], for plagiarism detection [31, 46], and to find copy-paste bugs [29].”