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On the naturalness of software
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-585-35958-8_1openalex-snapshot
Natural Language Processing: A Historical Review
inferredcites →https://doi.org/10.1007/978-3-642-00434-6_20openalex-snapshot
The Java Programmer’s Phrase Book
inferredcites →https://doi.org/10.1007/s10515-010-0064-xopenalex-snapshot
Improving code completion with program history
inferredcites →https://doi.org/10.1007/s10618-008-0118-xopenalex-snapshot
Sourcerer: mining and searching internet-scale software repositories
inferredcites →https://doi.org/10.1016/j.specom.2003.08.002openalex-snapshot
Statistical language model adaptation: review and perspectives
inferredcites →https://doi.org/10.1017/cbo9780511815829openalex-snapshot
Statistical Machine Translation
inferredcites →https://doi.org/10.1109/ase.2009.64openalex-snapshot
Code Completion from Abbreviated Input
inferredcites →https://doi.org/10.1109/icpc.2006.51openalex-snapshot
Whats in a Name? A Study of Identifiers
inferredcites →https://doi.org/10.1109/icse.2004.1317478openalex-snapshot
Mining version histories to guide software changes
inferredcites →https://doi.org/10.1109/icsm.2011.6080777openalex-snapshot
Generating natural language summaries for crosscutting source code concerns
inferredcites →https://doi.org/10.1109/icsm.2011.6080790openalex-snapshot
An evaluation of the strategies of sorting, filtering, and grouping API methods for Code Completion
inferredcites →https://doi.org/10.1109/mc.2009.256openalex-snapshot
Data Mining for Software Engineering
inferredcites →https://doi.org/10.1109/tse.2002.1041053openalex-snapshot
Recovering traceability links between code and documentation
inferredcites →https://doi.org/10.1109/vlhcc.2004.49openalex-snapshot
Spoken Language Support for Software Development
inferredcites →https://doi.org/10.1109/wac.2006.375941openalex-snapshot
Is Voice Recognition the Solution to Keyboard-Based RSI?
inferredcites →https://doi.org/10.1145/1082983.1083129openalex-snapshot
Using language clues to discover crosscutting concerns
inferredcites →https://doi.org/10.1145/1095430.1081711openalex-snapshot
Automatic generation of suggestions for program investigation
inferredcites →https://doi.org/10.1145/1168987.1169022openalex-snapshot
A voice-activated syntax-directed editor for manually disabled programmers
inferredcites →https://doi.org/10.1145/1218563.1218587openalex-snapshot
Using natural language program analysis to locate and understand action-oriented concerns
inferredcites →https://doi.org/10.1145/1357054.1357127openalex-snapshot
Crowdsourcing user studies with Mechanical Turk
inferredcites →https://doi.org/10.1145/1595696.1595728openalex-snapshot
Learning from examples to improve code completion systems
inferredcites →https://doi.org/10.1145/1858996.1859005openalex-snapshot
Automatically documenting program changes
inferredcites →https://doi.org/10.1145/1858996.1859006openalex-snapshot
Towards automatically generating summary comments for Java methods
inferredcites →https://doi.org/10.1145/1882291.1882315openalex-snapshot
A study of the uniqueness of source code
inferredcites →https://doi.org/10.1145/1900008.1900143openalex-snapshot
Code template inference using language models
inferredcites →https://doi.org/10.1145/1985441.1985471openalex-snapshot
Improving identifier informativeness using part of speech information
inferredcites →https://doi.org/10.1145/1985793.1985808openalex-snapshot
Automatically detecting and describing high level actions within methods
inferredcites →https://doi.org/10.1145/354324.354362openalex-snapshot
Programming by voice, VocalProgramming
inferredcites →https://doi.org/10.21236/ada273556openalex-snapshot
Building a Large Annotated Corpus of English: The Penn Treebank
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Structure‐Aware Representation Learning for Effective Performance Prediction
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A Simple, Efficient, Context‐sensitive Approach for Code Completion
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Clone refactoring inspection by summarizing clone refactorings and detecting inconsistent changes during software evolution
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GRRLN: Gated Recurrent Residual Learning Networks for code clone detection
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Field experience with obfuscating million‐user iOS apps in large enterprise mobile development
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Code Naturalness to Assist Search Space Exploration in Search-Based Program Repair Methods
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Towards Attention Based Vulnerability Discovery Using Source Code Representation
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On Data Analysis of Software Repositories
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Deep Generation of Coq Lemma Names Using Elaborated Terms
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Suggesting Descriptive Method Names: An Exploratory Study of Two Machine Learning Approaches
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API Misuse Detection Based on Stacked LSTM
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Universal Representation for Code
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Classification Model Based Automatic Static Evaluation Method for C Programs
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FastDCF: A Partial Index Based Distributed and Scalable Near-Miss Code Clone Detection Approach for Very Large Code Repositories
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Fine-Grained Obfuscation Scheme Recognition on Binary Code
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Source Code Vulnerability Detection and Interpretability with Language Models
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Qlose: Program Repair with Quantitative Objectives
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TBCNN for Programs’ Abstract Syntax Trees
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SourcererCC: Scalable and Accurate Clone Detection
inferred← citeshttps://doi.org/10.1007/978-981-19-9948-2_5openalex-snapshot
Statistical Models and Machine Learning to Advance Code Completion: Are We There Yet?
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Language Model-Driven Program Synthesis with Program Trace Optimization on the Abstraction and Reasoning Corpus
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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge
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Deep code comment generation with hybrid lexical and syntactical information
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“More Than Deep Learning”: post-processing for API sequence recommendation
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Analyzing source code vulnerabilities in the D2A dataset with ML ensembles and C-BERT
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IRJIT: A simple, online, information retrieval approach for just-in-time software defect prediction
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How challenging it is to identify real code authors: an empirical study
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Elixir: Effective object-oriented program repair
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Lancer: Your Code Tell Me What You Need
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Software Defect Prediction via GCN based on Structural and Context Information
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RWKV-based Encoder-Decoder Model for Code Completion
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Code Vulnerability Detection via Signal-Aware Learning
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Code Component Retrieval Using Code2Vec
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A Novel Code Classification based on Recurrent Nerual Network and Multi-Head Attention
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SG-Coder: Code completion algorithm applied on large software development platform
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A Version-Aware Metric for Measuring Innovation in Software Code Patterns
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AST-Based and Token-Based Neural Networks for Source Code Classification: A Comparative Performance Analysis
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Intelligent Dynamic Code Analysis using Large Language Models
inferred← citeshttps://doi.org/10.1109/iceee67194.2025.11261923openalex-snapshot
AI-Powered Code Completion with LSTM Networks
inferred← citeshttps://doi.org/10.1109/icpc.2017.24openalex-snapshot
Bug Localization with Combination of Deep Learning and Information Retrieval
inferred← citeshttps://doi.org/10.1109/icpc.2017.36openalex-snapshot
On the Uniqueness of Code Redundancies
inferred← citeshttps://doi.org/10.1109/icpc.2019.00024openalex-snapshot
Enabling Clone Detection For Ethereum Via Smart Contract Birthmarks
inferred← citeshttps://doi.org/10.1109/icpc.2019.00034openalex-snapshot
Does BLEU Score Work for Code Migration?
inferred← citeshttps://doi.org/10.1109/icpc52881.2021.00016openalex-snapshot
Code Authors Hidden in File Revision Histories: An Empirical Study
inferred← citeshttps://doi.org/10.1109/icpc52881.2021.00025openalex-snapshot
From Novice to Expert: Analysis of Token Level Effects in a Longitudinal Eye Tracking Study
inferred← citeshttps://doi.org/10.1109/icpc58990.2023.00030openalex-snapshot
Implant Global and Local Hierarchy Information to Sequence based Code Representation Models
inferred← citeshttps://doi.org/10.1109/icpc66645.2025.00042openalex-snapshot
Terminal Lucidity: Envisioning the Future of the Terminal
inferred← citeshttps://doi.org/10.1109/icse-c.2017.68openalex-snapshot
Learning Graph Representations for Defect Prediction
inferred← citeshttps://doi.org/10.1109/icse-fose59343.2023.00008openalex-snapshot
Large Language Models for Software Engineering: Survey and Open Problems
inferred← citeshttps://doi.org/10.1109/icse-fose59343.2023.00010openalex-snapshot
Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps
inferred← citeshttps://doi.org/10.1109/icse-seip52600.2021.00017openalex-snapshot
Search-Based Software Re-Modularization: A Case Study at Adyen
inferred← citeshttps://doi.org/10.1109/icse-seip52600.2021.00022openalex-snapshot
Learning Autocompletion from Real-World Datasets
inferred← citeshttps://doi.org/10.1109/icse.2013.6606598openalex-snapshot
How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms
inferred← citeshttps://doi.org/10.1109/icse.2013.6606624openalex-snapshot
Automatic recovery from runtime failures
inferred← citeshttps://doi.org/10.1109/icse.2015.190openalex-snapshot
New Initiative: The Naturalness of Software
inferred← citeshttps://doi.org/10.1109/icse.2015.212openalex-snapshot
Leveraging Informal Documentation to Summarize Classes and Methods in Context
inferred← citeshttps://doi.org/10.1109/icse.2015.228openalex-snapshot
CACHECA: A Cache Language Model Based Code Suggestion Tool
inferred← citeshttps://doi.org/10.1109/icse.2015.248openalex-snapshot
Deep Representations for Software Engineering
inferred← citeshttps://doi.org/10.1109/icse.2015.336openalex-snapshot
Graph-Based Statistical Language Model for Code
inferred← citeshttps://doi.org/10.1109/icse.2017.24openalex-snapshot
Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise
inferred← citeshttps://doi.org/10.1109/icse.2017.28openalex-snapshot
Stochastic Optimization of Program Obfuscation
inferred← citeshttps://doi.org/10.1109/icse.2017.44openalex-snapshot
Learning Syntactic Program Transformations from Examples
inferred← citeshttps://doi.org/10.1109/icse.2017.47openalex-snapshot
Exploring API Embedding for API Usages and Applications
inferred← citeshttps://doi.org/10.1109/icse.2019.00019openalex-snapshot
Learning to Spot and Refactor Inconsistent Method Names
inferred← citeshttps://doi.org/10.1109/icse.2019.00045openalex-snapshot
NL2Type: Inferring JavaScript Function Types from Natural Language Information
inferred← citeshttps://doi.org/10.1109/icse.2019.00086openalex-snapshot
A Novel Neural Source Code Representation Based on Abstract Syntax Tree
inferred← citeshttps://doi.org/10.1109/icse.2019.00101openalex-snapshot
When Code Completion Fails: A Case Study on Real-World Completions
inferred← citeshttps://doi.org/10.1109/icse.2019.00119openalex-snapshot
Recovering Variable Names for Minified Code with Usage Contexts
inferred← citeshttps://doi.org/10.1109/icse43902.2021.00025openalex-snapshot
Siri, Write the Next Method
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00129openalex-snapshot
Automated Program Repair in the Era of Large Pre-trained Language Models
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00178openalex-snapshot
Learning Deep Semantics for Test Completion
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00181openalex-snapshot
On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00199openalex-snapshot
On the Applicability of Language Models to Block-Based Programs
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00247openalex-snapshot
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness
inferred← citeshttps://doi.org/10.1109/icsess.2018.8663962openalex-snapshot
A Neural Language Model with a Modified Attention Mechanism for Software Code
inferred← citeshttps://doi.org/10.1109/icsm.2015.7332513openalex-snapshot
Exploring the use of deep learning for feature location
inferred← citeshttps://doi.org/10.1109/icsme.2014.29openalex-snapshot
CSCC: Simple, Efficient, Context Sensitive Code Completion
inferred← citeshttps://doi.org/10.1109/icsme.2016.85openalex-snapshot
Configuring and Assembling Information Retrieval Based Solutions for Software Engineering Tasks
inferred← citeshttps://doi.org/10.1109/icsme.2016.89openalex-snapshot
Do Contexts Help in Phrase-Based, Statistical Source Code Migration?
inferred← citeshttps://doi.org/10.1109/icsme.2018.00029openalex-snapshot
Statistical Translation of English Texts to API Code Templates
inferred← citeshttps://doi.org/10.1109/icsme.2018.00053openalex-snapshot
On the Impact of Tokenizer and Parameters on N-Gram Based Code Analysis
inferred← citeshttps://doi.org/10.1109/icsme.2018.00058openalex-snapshot
A Practical Approach to the Automatic Classification of Security-Relevant Commits
inferred← citeshttps://doi.org/10.1109/icsme.2018.00087openalex-snapshot
TUNA: TUning Naturalness-Based Analysis
inferred← citeshttps://doi.org/10.1109/icsme.2019.00045openalex-snapshot
Linguistic Change in Open Source Software
inferred← citeshttps://doi.org/10.1109/icsme.2019.00048openalex-snapshot
Syntax and Stack Overflow: A Methodology for Extracting a Corpus of Syntax Errors and Fixes
inferred← citeshttps://doi.org/10.1109/icsme52107.2021.00062openalex-snapshot
FeaRS: Recommending Complete Android Method Implementations
inferred← citeshttps://doi.org/10.1109/icsme58846.2023.00044openalex-snapshot
Towards a Universal Python: Translating the Natural Modality of Python into Other Human Languages
inferred← citeshttps://doi.org/10.1109/icsme58944.2024.00029openalex-snapshot
Deep Learning-Based Code Completion: On the Impact on Performance of Contextual Information
inferred← citeshttps://doi.org/10.1109/icsme64153.2025.00018openalex-snapshot
Boosting Redundancy-Based Automated Program Repair by Fine-Grained Pattern Mining
inferred← citeshttps://doi.org/10.1109/icst.2013.11openalex-snapshot
Evolving Readable String Test Inputs Using a Natural Language Model to Reduce Human Oracle Cost
inferred← citeshttps://doi.org/10.1109/icst46399.2020.00026openalex-snapshot
Fostering the Diversity of Exploratory Testing in Web Applications
inferred← citeshttps://doi.org/10.1109/icst53961.2022.00027openalex-snapshot
Learning Realistic Mutations: Bug Creation for Neural Bug Detectors
inferred← citeshttps://doi.org/10.1109/icst62969.2025.10989027openalex-snapshot
Differential Testing of Concurrent Classes
inferred← citeshttps://doi.org/10.1109/icst69053.2026.00075openalex-snapshot
Do Language Models Prefer Vulnerable Code? A Probabilistic Study of Insecure Code Preference
inferred← citeshttps://doi.org/10.1109/iisa71656.2026.11705481openalex-snapshot
Towards an Evaluation Methodology for AI Code Assistants
inferred← citeshttps://doi.org/10.1109/ijcnn.2019.8851760openalex-snapshot
JSAC: A Novel Framework to Detect Malicious JavaScript via CNNs over AST and CFG
inferred← citeshttps://doi.org/10.1109/ijcnn.2019.8851854openalex-snapshot
Deep Representation Learning for Code Smells Detection using Variational Auto-Encoder
inferred← citeshttps://doi.org/10.1109/ijcnn.2019.8852433openalex-snapshot
An Improved Recurrent Neural Network Language Model for Programming Language
inferred← citeshttps://doi.org/10.1109/ispass51385.2021.00032openalex-snapshot
Comparative Code Structure Analysis using Deep Learning for Performance Prediction
inferred← citeshttps://doi.org/10.1109/iwsc.2018.8327314openalex-snapshot
Generated code in studies on clone rates
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