Bugram: Bug detection with n-gram language models

By Song Wang, Devin Chollak, Dana Movshovitz-Attias, Lin Tan. 2016

146 citations recorded by the source corpus.

Subfields: Information Systems, Software, Natural Language Processing, Program Analysis & Verification

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Citations in the corpus

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021, background): “• code-code (clone detection [65, 84, 46, 80, 9, 89, 86], defect detection [91, 55, 51, 42, 78, 79], cloze test [15], code completion [57, 1, 56, 66, 67, 8, 27, 7], code repair [69, 4, 23, 26, 71, 73], and code-to-code translation [48, 37, 10, 41]) • text-code (natural language code search [32, 20, ”
CODIT: Code Editing With Tree-Based Neural Models (2018, methodology): “Applying ML to source code has received increasing attention in recent years [26] across many applications such as code completion [11], [31], bug prediction [57], [58], [59], clone detection [60], code search [61], etc.”
Finding Likely Errors with Bayesian Specifications (2017, background): “Such a specification assigns quantitative likelihood values to observed program behaviors, with higher likelihood representing greater confidence in a behavior’s correctness.”
TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search (2020, methodology): “Assuming such sequence with its length T , the language model denes p(y1:T ) as its occurrence probability which is usually computed based on the conditional probability from a window of n predecessor words, known as ngram [30], as shown in Equation 1.”
Improving bug detection via context-based code representation learning and attention-based neural networks (2019, methodology): “For example, the Bugram [Wang et al. 2016a] uses n-gram models to rank the methods and then picks the top-ranked methods as buggy methods.”
Neural Attribution for Semantic Bug-Localization in Student Programs (2019, background): “Bugram [28] is a language model based bug-detection technique.”
Integrating Tree Path in Transformer for Code Representation (2021, background): “Early research works learned language model on raw text data for code [Dam et al., 2016, Wang et al., 2016, Allamanis et al., 2016, Iyer et al., 2016], providing evidence for the naturalness assumption [Hindle et al., 2016].”
Bayesian specification learning for finding API usage errors (2017, methodology): “Some recent efforts [40, 41, 52] have also used n-gram models to learn specifications on source code structure.”