# The Future of AI-Driven Software Engineering

arXiv: https://arxiv.org/abs/2406.07737 · HTML: https://mlsanity.com/paper/2406.07737/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 118 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 30 of 38)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://mlsanity.com/paper/2310.03003/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [InferFix: End-to-End Program Repair with LLMs](https://mlsanity.com/paper/2303.07263/) (2023)
- [On Protecting the Data Privacy of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2403.05156/) (2024)
- [No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation](https://mlsanity.com/paper/2305.04207/) (2023)
- [Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning](https://mlsanity.com/paper/2207.08281/) (2022)
- [Metamorphic Testing: A New Approach for Generating Next Test Cases](https://mlsanity.com/paper/2002.12543/) (2020)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [Automated Unit Test Improvement using Large Language Models at Meta](https://mlsanity.com/paper/2402.09171/) (2024)
- [Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning](https://mlsanity.com/paper/2304.11384/) (2023)
- [Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT](https://mlsanity.com/paper/2304.10778/) (2023)
- [RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation](https://mlsanity.com/paper/2402.16667/) (2024)
- [Using Large Language Models to Generate JUnit Tests: An Empirical Study](https://mlsanity.com/paper/2305.00418/) (2023)
- [Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing](https://mlsanity.com/paper/2308.16557/) (2023)
- [Pynguin: Automated Unit Test Generation for Python](https://mlsanity.com/paper/2202.05218/) (2022)
- [Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback](https://mlsanity.com/paper/2403.16792/) (2024)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [TOGLL: Correct and Strong Test Oracle Generation with LLMs](https://mlsanity.com/paper/2405.03786/) (2024)
- [Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation](https://mlsanity.com/paper/2402.00905/) (2024)
- [Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning](https://mlsanity.com/paper/2310.18338/) (2023)
- [Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools](https://mlsanity.com/paper/2312.10622/) (2023)
- [EvoSpex: An Evolutionary Algorithm for Learning Postconditions](https://mlsanity.com/paper/2102.13569/) (2021)
- [APIzation: Generating Reusable APIs from StackOverflow Code Snippets](https://mlsanity.com/paper/2109.01138/) (2021)

## Built on by (most cited first, 23 of 23)

- [Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarios](https://mlsanity.com/paper/2503.12374/) (2025)
- [LLMs: A Game-Changer for Software Engineers?](https://mlsanity.com/paper/2411.00932/) (2024)
- [Generative AI and Empirical Software Engineering: A Paradigm Shift](https://mlsanity.com/paper/2502.08108/) (2025)
- [LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loops](https://mlsanity.com/paper/2603.23613/) (2026)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale](https://mlsanity.com/paper/2504.09283/) (2025)
- [Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents](https://mlsanity.com/paper/2601.20106/) (2026)
- [Understanding LLM-Driven Test Oracle Generation](https://mlsanity.com/paper/2601.05542/) (2026)
- [Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges](https://mlsanity.com/paper/2601.10220/) (2026)
- [LLMORPH: Automated Metamorphic Testing of Large Language Models](https://mlsanity.com/paper/2603.23611/) (2026)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering](https://mlsanity.com/paper/2602.00496/) (2026)
- [Lore: Repurposing Git Commit Messages as a Structured Knowledge Protocol for AI Coding Agents](https://mlsanity.com/paper/2603.15566/) (2026)
- [MR-Coupler: Automated Metamorphic Test Generation via Functional Coupling Analysis](https://mlsanity.com/paper/2604.10126/) (2026)
- [SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents](https://mlsanity.com/paper/2604.17699/) (2026)
- [Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models](https://mlsanity.com/paper/2607.16708/) (2026)
- [Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents](https://mlsanity.com/paper/2506.20062/) (2025)
- [Toward Explaining Large Language Models in Software Engineering Tasks](https://mlsanity.com/paper/2512.20328/) (2025)
- [From Untestable to Testable: Metamorphic Testing in the Age of LLMs](https://mlsanity.com/paper/2603.24774/) (2026)
- [Vibe-driven model-based engineering](https://mlsanity.com/paper/2604.10645/) (2026)
- [Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment](https://mlsanity.com/paper/2606.11672/) (2026)
- [AI-driven Software Development: A Pragmatic Path to Agentic Development Processes](https://mlsanity.com/paper/2606.15283/) (2026)
- [Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications](https://mlsanity.com/paper/2607.10674/) (2026)

## Abstract (arXiv)

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.