Using web corpus statistics for program analysis

37 citations recorded by the source corpus.

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Neural Code Comprehension: A Learnable Representation of Code Semantics (2018, methodology): “[34] use PDGs to compute n-gram models for program analysis, and Wang et al.”
Learning to Fuzz: Application-Independent Fuzz Testing with Probabilistic, Generative Models of Input Data (2016, methodology): “These models are useful for code completion [9, 22, 37, 38, 43], plagiarism detection [24], and to infer appropriate identifier names [7, 42].”
Divide-and-Conquer Approach for Multi-phase Statistical Migration for Source Code (T) (2015, methodology): “To verify semantic correctness, the approach in [16] may cause inaccuracy in our result.”
Bugram: Bug detection with n-gram language models (2016, methodology): “[17] proposed to use n-gram token sequences and tf-idf-style measures to detect code clone and related bugs.”
Learning API Usages from Bytecode: A Statistical Approach (2015, methodology): “[17] learns n-gram language model on program dependence graph and uses the model for finding plagiarized code pairs.”
Probabilistic model for code with decision trees (2016, methodology): “Further, several approaches address the limitation of the n-gram model by providing better semantic program abstractions using program dependence graphs [13] or various extensions including topic models [22] and modeling local and global context [34].”