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Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A survey on large language models for recommendation
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What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?
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Large Language Models for Software Engineering: Survey and Open Problems
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Automated Program Repair in the Era of Large Pre-trained Language Models
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RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
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On the Evaluation of Commit Message Generation Models: An Experimental Study
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Automatic Commit Message Generation: A Critical Review and Directions for Future Work
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Automated Commit Message Generation With Large Language Models: An Empirical Study and Beyond
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eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI
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What makes a good commit message?
inferredcites →https://doi.org/10.1145/3540250.3549101openalex-snapshot
Less training, more repairing please: revisiting automated program repair via zero-shot learning
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CommentFinder: a simpler, faster, more accurate code review comments recommendation
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SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics
inferredcites →https://doi.org/10.1145/3597503.3623316openalex-snapshot
CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models
inferredcites →https://doi.org/10.1145/3597503.3639125openalex-snapshot
GrammarT5: Grammar-Integrated Pretrained Encoder-Decoder Neural Model for Code
inferredcites →https://doi.org/10.1145/3597503.3639183openalex-snapshot
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
inferredcites →https://doi.org/10.1145/3597503.3639226openalex-snapshot
Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code
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CCT5: A Code-Change-Oriented Pre-trained Model
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A Survey on Evaluation of Large Language Models
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Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
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Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPT
inferredcites →https://doi.org/10.1145/3650212.3680359openalex-snapshot
ThinkRepair: Self-Directed Automated Program Repair
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A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
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Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
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Self-Collaboration Code Generation via ChatGPT
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Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering
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Large Language Models for Software Engineering: A Systematic Literature Review
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Is It Hard to Generate Holistic Commit Message?
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Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
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Findings of the Association for Computational Linguistics: ACL 2025
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Individual Comparisons by Ranking Methods
inferredcites →https://doi.org/10.48550/arxiv.2003.05620openalex-snapshot
CC2Vec: Distributed Representations of Code Changes
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The reproducible package of empirical study
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Ontology Population Using LLMs: Which Factors Matter?
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Zero-shot interpretable biomedical literature appraisal with generative large language models
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Model-Agnostic Empirical Evaluation of Test-Driven Prompt Engineering on Improving Accuracy and Efficiency in Large Language Models Python Code Generation
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Deconstructing Prompt Strategies: Evaluating Generative AI for Latency, Relevance, and Depth in Domain-Specific Tasks
inferred← citeshttps://doi.org/10.1109/fllm67465.2025.11391174openalex-snapshot
Balancing Cost and Quality in LLM-based Test Case Generation by Prompt Engineering
inferred← citeshttps://doi.org/10.1109/fllm67465.2025.11391183openalex-snapshot
Toward Green Code: Prompting Small Language Models for Energy-Efficient Code Generation
inferred← citeshttps://doi.org/10.1109/naecon70028.2026.11675033openalex-snapshot
Experimental Prompt Engineering Using Structured Language Refinement for Attack Path Analysis
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Condor: A Code Discriminator Integrating General Semantics With Code Details
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Boosting LLMs for Mutation Generation
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From Greedy Steps to Global Optimization: Learning Sequential Test Suite Generation
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Prompts Matter: On the Impact of Code Smell Remediation with Large Language Models
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Whose voice counts? The role of large language models in public commenting
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Evaluating large language models as clinical laboratory test recommenders in primary and emergency care: a crucial step in clinical decision making
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GPT-4o and OpenAI o1 Performance on the 2024 Spanish Competitive Medical Specialty Access Examination: Cross-Sectional Quantitative Evaluation Study
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