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🏛️ The Software Engineering Crypt
cs.SE: Where Software Engineering papers rest without their code.
16351
Total Papers
13410
No Code
294
Twilight
2647
Has Code
16.2%
Survival Rate
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Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research
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CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Building
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SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection
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Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents
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Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency
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Leveraging LLMs for User Stories in AI Systems: UStAI Dataset
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Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks
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BLAZE: Cross-Language and Cross-Project Bug Localization via Dynamic Chunking and Hard Example Learning
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Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering
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Does GenAI Make Usability Testing Obsolete?
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Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Models
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Synthetic Test Data Generation Using Recurrent Neural Networks: A Position Paper
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Leveraging Large Language Models to Boost Dafny's Developers Productivity
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Enhancing LLM Code Generation with Ensembles: A Similarity-Based Selection Approach
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Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation
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The Kieker Observability Framework Version 2
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ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
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LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters
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Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs
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Knowledge-Enhanced Program Repair for Data Science Code
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Intention is All You Need: Refining Your Code from Your Intention
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Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
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