Meaningful variable names for decompiled code

Jaffe, Alan; Lacomis, Jeremy; Schwartz, Edward J.; Goues, Claire Le; Vasilescu, Bogdan

doi:10.1145/3196321.3196330

Cited by 32 publications

(10 citation statements)

References 33 publications

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“…Several approaches use neural machine translation, in which an encoder LM is paired to a decoder LM. Examples include recovering names from minified Javascript code [10,84], or from decompiled C code [50]. Other applications include program repair [20], learning code changes [82], or generating source code comments [47].…”

Section: Language Modeling and Vocabulary In Sementioning

confidence: 99%

Big code != big vocabulary

Karampatsis

Babii

Robbes

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

134

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Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new software development tools, such as tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work; and 3) showing that such models outperform the state of the art on three distinct code corpora (Java, C, Python). To our knowledge, these are the largest NLMs for code that have been reported. All datasets, code, and trained models used in this work are publicly available. CCS CONCEPTS • Software and its engineering → Software maintenance tools.

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Section: Language Modeling and Vocabulary In Sementioning

confidence: 99%

Big code != big vocabulary

Karampatsis

Babii

Robbes

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

134

View full text Add to dashboard Cite

show abstract

“…Atlas generates models trained on large-scale datasets of source code to accurately predict assert statements within test methods. We take advantage of the deep learning strategy of NMT, which has become an important tool for supporting software-related tasks such as bug-fixing [7,12,20,36], code changes [35], code migration [21,22], code summarization [18,19,39], pseudo-code generation [23], code deobfuscation [13,38] and mutation analysis [37]. To the best of our knowledge, this is the first empirical step toward evaluating an NMT-based approach for the automatic generation of assert statements.…”

Section: Introductionmentioning

confidence: 99%

On learning meaningful assert statements for unit test cases

Watson

Tufano

Moran

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

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Software testing is an essential part of the software lifecycle and requires a substantial amount of time and eort. It has been estimated that software developers spend close to 50% of their time on testing the code they write. For these reasons, a long standing goal within the research community is to (partially) automate software testing. While several techniques and tools have been proposed to automatically generate test methods, recent work has criticized the quality and usefulness of the assert statements they generate. Therefore, we employ a Neural Machine Translation (NMT) based approach called A (AuTomatic Learning of Assert Statements) to automatically generate meaningful assert statements for test methods. Given a test method and a focal method (i.e., the main method under test), A can predict a meaningful assert statement to assess the correctness of the focal method. We applied A to thousands of test methods from GitHub projects and it was able to predict the exact assert statement manually written by developers in 31% of the cases when only considering the top-1 predicted assert. When considering the top-5 predicted assert statements, A is able to predict exact matches in 50% of the cases. These promising results hint to the potential usefulness of our approach as (i) a complement to automatic test case generation techniques, and (ii) a code completion support for developers, who can benet from the recommended assert statements while writing test code.

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Section: Language Modeling and Vocabulary In Sementioning

confidence: 99%

Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code

Karampatsis,

Babii,

Robbes

et al. 2020

Preprint

View full text Add to dashboard Cite

Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new software development tools, such as tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work; and 3) showing that such models outperform the state of the art on three distinct code corpora (Java, C, Python). To our knowledge, these are the largest NLMs for code that have been reported. All datasets, code, and trained models used in this work are publicly available. CCS CONCEPTS• Software and its engineering → Software maintenance tools.

show abstract

Meaningful variable names for decompiled code

Cited by 32 publications

References 33 publications

Big code != big vocabulary

Big code != big vocabulary

On learning meaningful assert statements for unit test cases

Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code

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