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Context-aware Retrieval-based Deep Commit Message Generation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4612-0667-5openalex-snapshot
Breakthroughs in Statistics
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
inferredcites →https://doi.org/10.1007/s10664-019-09730-9openalex-snapshot
Deep code comment generation with hybrid lexical and syntactical information
inferredcites →https://doi.org/10.1023/b:lida.0000048322.42751.caopenalex-snapshot
A Survey of Documentation Practice within Corrective Maintenance
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Measuring nominal scale agreement among many raters.
inferredcites →https://doi.org/10.1109/78.650093openalex-snapshot
Bidirectional recurrent neural networks
inferredcites →https://doi.org/10.1109/ase.2015.36openalex-snapshot
Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation
inferredcites →https://doi.org/10.1109/ase.2017.8115681openalex-snapshot
AnswerBot: Automated generation of answer summary to developers' technical questions
inferredcites →https://doi.org/10.1109/ase.2019.00026openalex-snapshot
Automatic Generation of Pull Request Descriptions
inferredcites →https://doi.org/10.1109/chase.2009.5071408openalex-snapshot
Why are software projects moving from centralized to decentralized version control systems?
inferredcites →https://doi.org/10.1109/compsac.2016.162openalex-snapshot
On Automatic Summarization of What and Why Information in Source Code Changes
inferredcites →https://doi.org/10.1109/esem.2017.56openalex-snapshot
Mining Version Control System for Automatically Generating Commit Comment
inferredcites →https://doi.org/10.1109/icpc.2017.12openalex-snapshot
Towards Automatic Generation of Short Summaries of Commits
inferredcites →https://doi.org/10.1109/icse.2019.00087openalex-snapshot
A Neural Model for Generating Natural Language Summaries of Program Subroutines
inferredcites →https://doi.org/10.1109/msr.2010.5463344openalex-snapshot
Can development work describe itself?
inferredcites →https://doi.org/10.1109/msr.2019.00056openalex-snapshot
Generating Commit Messages from Diffs using Pointer-Generator Network
inferredcites →https://doi.org/10.1109/scam.2014.14openalex-snapshot
On Automatically Generating Commit Messages via Summarization of Source Code Changes
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Perceptions, Expectations, and Challenges in Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2019.2937083openalex-snapshot
How does Machine Learning Change Software Development Practices?
inferredcites →https://doi.org/10.1109/tse.2020.2989002openalex-snapshot
Defining Smart Contract Defects on Ethereum
inferredcites →https://doi.org/10.1145/1858996.1859005openalex-snapshot
Automatically documenting program changes
inferredcites →https://doi.org/10.1145/3238147.3238190openalex-snapshot
Neural-machine-translation-based commit message generation: how far are we?
inferredcites →https://doi.org/10.1145/3377811.3380383openalex-snapshot
Retrieval-based neural source code summarization
inferredcites →https://doi.org/10.1162/089120103321337458openalex-snapshot
Word Reordering and a Dynamic Programming Beam Search Algorithm for Statistical Machine Translation
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A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
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R.A. Fisher and the making of maximum likelihood 1912-1922
inferredcites →https://doi.org/10.18653/v1/n19-1394openalex-snapshot
Recommendations for Datasets for Source Code Summarization
inferredcites →https://doi.org/10.18653/v1/w17-4713openalex-snapshot
Multi-Domain Neural Machine Translation through Unsupervised Adaptation
inferredcites →https://doi.org/10.18653/v1/w18-6513openalex-snapshot
Content Aware Source Code Change Description Generation
inferredcites →https://doi.org/10.24963/ijcai.2019/552openalex-snapshot
Commit Message Generation for Source Code Changes
inferredcites →https://doi.org/10.3115/v1/p14-5010openalex-snapshot
The Stanford CoreNLP Natural Language Processing Toolkit
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Current Trends in Tensorflow and the Future of Ml Software Documentation: One Documentation Does Not Fit All
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A large-scale empirical study of commit message generation: models, datasets and evaluation
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Repository-level code completion with adaptive segmentation and fused retrieval
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Learning to represent code changes
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Reading between the Lines: Context-Aware AI-based generation of software exploits
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Capturing the context-aware code change via dynamic control flow graph for commit message generation
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Research on paragraph-level functional structure recognition in scientific literature: a data augmentation method based on LLMs and lexical function
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
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Multilingual code refactoring detection based on deep learning
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An exploratory study of software artifacts on GitHub from the lens of documentation
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Using Large Language Models for multi-level commit message generation for large diffs
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Graph4Web: A relation-aware graph attention network for web service classification
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A survey on machine learning techniques applied to source code
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An empirical study of self-admitted technical debt in AI agents
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Retrieve-Guided Commit Message Generation with Semantic Similarity And Disparity
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00050openalex-snapshot
Delving into Commit-Issue Correlation to Enhance Commit Message Generation Models
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From Commit Message Generation to History-Aware Commit Message Completion
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LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM
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Contextual Code Retrieval for Commit Message Generation: A Preliminary Study
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Construction of Intelligent Sign Language Corpus of Tourist Attractions under the Background of Big Data Information Retrieval
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Optimizing Cross Language Information Retrieval and Context Translation Using BERT Model and Deep Learning Techniques
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Towards Generating the Rationale for Code Changes
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LLMs as Evaluators: A Novel Approach to Commit Message Quality Assessment
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CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back
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Developer-Intent Driven Code Comment Generation
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Did We Miss Something Important? Studying and Exploring Variable-Aware Log Abstraction
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An Empirical Study on Commit Message Generation Using LLMs via In-Context Learning
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Summarize Me: The Future of Issue Thread Interpretation
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AI-Powered Commit Explorer (APCE)
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SECMG: Style Enhancement Commit Message Generation
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Enhancing AI-based Generation of Software Exploits with Contextual Information
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Commit Classification via Diff-Code GCN based on System Dependency Graph
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Slice-Based Code Change Representation Learning
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Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Tasks
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Enhancing Commit Message Generation in Software Repositories: A RAG-Based Approach
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Technical Debt Prediction
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Automatic Commit Message Generation: A Critical Review and Directions for Future Work
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Just-In-Time TODO-Missed Commits Detection
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Automated Commit Message Generation With Large Language Models: An Empirical Study and Beyond
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DockerFill: Automatically Completing Dockerfile Code With Syntax-Aware Multi-Task Learning
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On the Significance of Category Prediction for Code-Comment Synchronization
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Combining Code Context and Fine-grained Code Difference for Commit Message Generation
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Automatic Comment Generation via Multi-Pass Deliberation
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Retrieving API Knowledge from Tutorials and Stack Overflow Based on Natural Language Queries
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Toward Automatically Completing GitHub Workflows
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COME: Commit Message Generation with Modification Embedding
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CCT5: A Code-Change-Oriented Pre-trained Model
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LoGenText-Plus : Improving Neural Machine Translation Based Logging Texts Generation with Syntactic Templates
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Learning to Represent Patches
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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation
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Only diff Is Not Enough: Generating Commit Messages Leveraging Reasoning and Action of Large Language Model
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ESGen: Commit Message Generation Based on Edit Sequence of Code Change
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Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study
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Commit Message Generation via ChatGPT: How Far Are We?
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CCAF: Learning Code Change via AdapterFusion
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Is It Hard to Generate Holistic Commit Message?
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Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation
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From Bytes to Semantics: LLM-Agent-Driven Commit Message Generation for Non-Code Assets
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Readability of AI-Generated Pull Request Descriptions Across Pull Request Types
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Delving into History: Retrieve Less but Augment More on Commit Message Generation
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ProofFusion: Improving Neural Theorem Proving via Adaptive Retrieval-Augmented Reasoning
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Towards an Understanding of Context Utilization in Code Intelligence
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ChangeWiki: Repository-Level Change Summarization Augmented with Architecture Semantics
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RACE: Retrieval-augmented Commit Message Generation
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CommitMessageGen-Agent: A Dependable Multi-Agent Approach for Reproducible Commit Message Generation
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Learning program semantics via exploring program structures with deep learning
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Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation
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