{"total":33,"rows":[{"id":"2308.10620","title":"Large Language Models for Software Engineering: A Systematic Literature Review","created":"2023-08-21","cited_by_count":399,"area":null},{"id":"2203.09095","title":"Automating Code Review Activities by Large-Scale Pre-training","created":"2022-03-17","cited_by_count":98,"area":null},{"id":"2401.00288","title":"Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit","created":"2023-12-30","cited_by_count":22,"area":null},{"id":"2309.04142","title":"Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps","created":"2023-09-08","cited_by_count":19,"area":null},{"id":"2308.12415","title":"Benchmarking Causal Study to Interpret Large Language Models for Source Code","created":"2023-08-23","cited_by_count":14,"area":null},{"id":"2302.03788","title":"Toward a Theory of Causation for Interpreting Neural Code Models","created":"2023-02-07","cited_by_count":12,"area":null},{"id":"2402.13640","title":"Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures","created":"2024-02-21","cited_by_count":11,"area":null},{"id":"2407.08983","title":"Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations","created":"2024-07-12","cited_by_count":11,"area":null},{"id":"2308.03873","title":"Evaluating and Explaining Large Language Models for Code Using Syntactic Structures","created":"2023-08-07","cited_by_count":9,"area":null},{"id":"2310.17903","title":"Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey","created":"2023-10-27","cited_by_count":9,"area":null},{"id":"2302.04048","title":"Automating Code-Related Tasks Through Transformers: The Impact of Pre-training","created":"2023-02-08","cited_by_count":8,"area":null},{"id":"2403.03322","title":"Deep Configuration Performance Learning: A Systematic Survey and Taxonomy","created":"2024-03-05","cited_by_count":8,"area":null},{"id":"2211.09425","title":"Machine Learning for Software Engineering: A Tertiary Study","created":"2022-11-17","cited_by_count":7,"area":null},{"id":"2305.04106","title":"On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code","created":"2023-05-06","cited_by_count":7,"area":null},{"id":"2401.10359","title":"Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting","created":"2024-01-18","cited_by_count":6,"area":null},{"id":"2412.18989","title":"How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study","created":"2024-12-25","cited_by_count":6,"area":null},{"id":"2205.13022","title":"Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora","created":"2022-05-25","cited_by_count":4,"area":null},{"id":"2404.04839","title":"AI for DevSecOps: A Landscape and Future Opportunities","created":"2024-04-07","cited_by_count":4,"area":null},{"id":"2508.02611","title":"Meta-RAG on Large Codebases Using Code Summarization","created":"2025-08-04","cited_by_count":4,"area":null},{"id":"2301.10516","title":"What are the Machine Learning best practices reported by practitioners on Stack Exchange?","created":"2023-01-25","cited_by_count":3,"area":null},{"id":"2506.22776","title":"Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generation","created":"2025-06-28","cited_by_count":3,"area":null},{"id":"2112.11858","title":"End to End Software Engineering Research","created":"2021-12-22","cited_by_count":2,"area":null},{"id":"2209.01566","title":"Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables","created":"2022-09-04","cited_by_count":2,"area":null},{"id":"2502.12490","title":"UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation","created":"2025-02-18","cited_by_count":2,"area":null},{"id":"2112.11226","title":"Energy-bounded Learning for Robust Models of Code","created":"2021-12-20","cited_by_count":1,"area":null},{"id":"2307.02443","title":"An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code","created":"2023-07-05","cited_by_count":1,"area":null},{"id":"2308.08943","title":"Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?","created":"2023-08-17","cited_by_count":1,"area":null},{"id":"2402.05645","title":"Investigating Reproducibility in Deep Learning-Based Software Fault Prediction","created":"2024-02-08","cited_by_count":1,"area":null},{"id":"2406.13877","title":"A Systematic Literature Review on the Use of Machine Learning in Software Engineering","created":"2024-06-19","cited_by_count":0,"area":null},{"id":"2411.19304","title":"Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education","created":"2024-11-28","cited_by_count":0,"area":null},{"id":"2503.13793","title":"Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives","created":"2025-03-18","cited_by_count":0,"area":null},{"id":"2601.21070","title":"Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering","created":"2026-01-28","cited_by_count":0,"area":null},{"id":"2604.15385","title":"Prompt-Driven Code Summarization: A Systematic Literature Review","created":"2026-04-16","cited_by_count":0,"area":null}],"next_cursor":null}