Artificial Intelligence for Software Engineering: Advances, Applications, and Implications

Over the past decade, the fusion of software engineering (SE) and artificial intelligence (AI) has transformed software development. As software complexity grows, AI techniques like machine learning (ML), deep learning (DL), and large language models (LLMs) are streamlining SE phases, from design to maintenance. These innovations automate tasks, improve defect prediction, and optimize testing.

Breakthroughs in foundation models (e.g., GPT-4, Code Llama) have accelerated this trend, enabling tools like GitHub Copilot and Amazon CodeWhisperer to assist developers. AI-powered solutions, such as GitHub’s Copilot for code autocompletion and Facebook’s SapFix for automated bug fixes, demonstrate real-world impact. As AI becomes mainstream in SE, it enhances software quality and speeds up development. However, challenges like model interpretability and ethical concerns must be addressed to ensure responsible AI integration. The continued evolution of AI in SE promises both innovation and new complexities.

The integration of AI in SE offers immense potential for enhancing productivity and software quality. However, several challenges hinder its widespread adoption. The absence of standardized frameworks limits consistent AI application, while difficulties in interpreting and trusting AI models create barriers to their effective use in SE. Additionally, the lack of clear ethical guidelines raises concerns about responsible AI deployment in software development.

Emerging issues further complicate adoption, particularly the risks associated with AI-generated code. Vulnerabilities, copyright concerns, and accountability questions pose significant challenges, making it essential to establish rigorous evaluation mechanisms. Addressing these issues will be crucial to unlocking AI’s full potential in SE while ensuring reliability, security, and ethical compliance.

The key research goals include:
1. Developing Standardized Frameworks: establishing universal frameworks to guide the application of AI across SE activities, ensuring consistency and effectiveness in different phases of the software lifecycle.
2. Enhancing Model Interpretability: investigating methods to make AI models more transparent and explainable in the SE context, enabling developers to trust and effectively utilize AI-driven insights.
3. Establishing Ethical Standards: defining clear ethical guidelines for using AI in SE, focusing on fairness, accountability, and minimizing biases in AI-driven tools and processes.
4. Ensuring Security and Compliance: developing strategies to identify and mitigate AI-introduced software vulnerabilities (e.g., insecure code generation or AI hallucinations that produce faulty code) and clarify legal responsibilities, including copyright compliance and accountability for AI-generated artifacts.
5. Fostering Interdisciplinary Collaboration: promoting partnerships between AI researchers and software engineers to co-create innovative, practical solutions that balance AI capabilities with the realities of industrial SE practices.

This Research Topic explores the integration of advanced AI techniques into all facets of SE, emphasizing both the opportunities they provide and the challenges that must be overcome to implement them responsibly. We especially encourage works that highlight practical implementations of AI in real-world SE settings (including industrial case studies and applications), as well as studies that investigate emerging trends and underexplored paradigms such as the use of foundation models, autonomous agents, and multi-modal AI in software development. In parallel, contributors should address the critical AI risks and implications in SE—ranging from technical issues like AI-generated security vulnerabilities to broader concerns like legal and regulatory compliance (e.g., copyright and responsibility for AI-generated code).

Themes of interest include, but are not limited to:
- AI system integrity and quality in SE
- Data quality and bias in AI models for SE
- Robustness and resilience of AI-driven software systems
- Incident response and recovery for AI-augmented SE systems
- System monitoring and maintenance for AI in SE
- Secure deployment and integration of AI in DevOps pipelines
- Secure code generation and program synthesis using AI techniques
- Explainable AI methods in SE (interpreting AI recommendations in development)
- Vulnerability detection in AI-generated code
- AI-generated offensive/defense security code in SE
- Foundation models in SE (e.g., GPT-4, Code Llama for coding tasks and software design)
- Autonomous AI-driven software agents for development and maintenance tasks
- Multi-modal AI in SE (combining text, code, and other modalities in SE tools)
- Legal and regulatory implications of AI in SE (copyright, licensing, and compliance issues)

We welcome original research, case studies, surveys, theoretical frameworks, and perspective articles that connect these AI techniques (ML, DL, NLP, LLMs, etc.) to SE advancements. Manuscripts should demonstrate clear practical relevance—for example, by evaluating AI-based tools in industrial or opensource project settings—and ensure that ethical, security, and legal considerations are discussed alongside technical contributions. Through this Research Topic, we aim to shed light on how cutting-edge AI can be harnessed to address longstanding SE challenges, while also scrutinizing the risks and responsibilities that accompany this new era of intelligent SE.

Keywords: artificial intelligence, deep learning, machine learning, natural language processing, software engineering, explainable AI, software maintenance, software testing, software development, requirements elicitation

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.