# Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps arXiv: https://arxiv.org/abs/2309.04142 ยท HTML: https://mlsanity.com/paper/2309.04142/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 48 of its 155 references that are in the map. - First posted on arXiv 8 Sep 2023. ## Builds on (most cited first, 30 of 48) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021) - [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020) - [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021) - [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023) - [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023) - [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018) - [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023) - [You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion](https://mlsanity.com/paper/2007.02220/) (2020) - [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022) - [Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPT](https://mlsanity.com/paper/2304.00385/) (2023) - [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021) - [Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues](https://mlsanity.com/paper/2307.12596/) (2023) - [Natural Attack for Pre-trained Models of Code](https://mlsanity.com/paper/2201.08698/) (2022) - [Automated Repair of Programs from Large Language Models](https://mlsanity.com/paper/2205.10583/) (2022) - [Repair Is Nearly Generation: Multilingual Program Repair with LLMs](https://mlsanity.com/paper/2208.11640/) (2022) - [On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot](https://mlsanity.com/paper/2302.00438/) (2023) - [CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models](https://mlsanity.com/paper/2206.00052/) (2022) - [Stealthy Backdoor Attack for Code Models](https://mlsanity.com/paper/2301.02496/) (2023) - [CC2Vec: Distributed Representations of Code Changes](https://mlsanity.com/paper/2003.05620/) (2020) - [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020) - [A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research](https://mlsanity.com/paper/2009.06520/) (2020) - [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023) - [Compressing Pre-trained Models of Code into 3 MB](https://mlsanity.com/paper/2208.07120/) (2022) - [Oreo: Detection of Clones in the Twilight Zone](https://mlsanity.com/paper/1806.05837/) (2018) - [CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models](https://mlsanity.com/paper/2302.04030/) (2023) - [MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models](https://mlsanity.com/paper/2303.13009/) (2023) ## Built on by (most cited first, 19 of 19) - [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024) - [Calibration and Correctness of Language Models for Code](https://mlsanity.com/paper/2402.02047/) (2024) - [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024) - [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024) - [Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey](https://mlsanity.com/paper/2310.17903/) (2023) - [Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI](https://mlsanity.com/paper/2411.08881/) (2024) - [On LLMs' Internal Representation of Code Correctness](https://mlsanity.com/paper/2512.07404/) (2025) - [LUNA: A Model-Based Universal Analysis Framework for Large Language Models](https://mlsanity.com/paper/2310.14211/) (2023) - [Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian](https://mlsanity.com/paper/2501.11264/) (2025) - [GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training](https://mlsanity.com/paper/2406.11439/) (2024) - [Morescient GAI for Software Engineering (Extended Version)](https://mlsanity.com/paper/2406.04710/) (2024) - [Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings](https://mlsanity.com/paper/2506.03013/) (2025) - [Open the Oyster: Empirical Evaluation and Improvement of Code Reasoning Confidence in LLMs](https://mlsanity.com/paper/2511.02197/) (2025) - [PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing](https://mlsanity.com/paper/2601.06910/) (2026) - [Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainability](https://mlsanity.com/paper/2602.07071/) (2026) - [Demystifying Faulty Code with LLM: Step-by-Step Reasoning for Explainable Fault Localization](https://mlsanity.com/paper/2403.10507/) (2024) - [Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives](https://mlsanity.com/paper/2503.13793/) (2025) - [AI and Agile Software Development: A Research Roadmap from the XP2025 Workshop](https://mlsanity.com/paper/2508.20563/) (2025) - [From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems](https://mlsanity.com/paper/2606.31589/) (2026) ## Abstract (arXiv) For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engineering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to AI4SE, particularly highlighting the need to realize trustworthy and synergistic AI4SE. Progressing, the paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted, suggesting a transition towards Software Engineering 2.0. Two strategic roadmaps are then laid out: one centered on realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE. While this paper may not serve as a conclusive guide, its intent is to catalyze further progress. The ultimate aspiration is to position AI4SE as a linchpin in redefining the horizons of software engineering, propelling us toward Software Engineering 2.0.