Machine Learning for Software Engineering (AISE lab)

Broad Topic Categories

AI-enabled Software Engineering (AISE)
Large language models (LLMs) for Code (opportunities and challenges)
Intelligent development tools
Enhanced developer productivity

Research Description

In recent years, Machine Learning and AI technologies have made remarkable strides—particularly with the emergence of Large Language Models (LLMs) such as GitHub Copilot, Cursor.ai, ChatGPT, JetBrains AI Assistant, Google Gemini, and many others. These models have found successful applications across various domains, including Software Engineering, where software repositories provide a rich archive of valuable data: source code, execution traces, version histories, mailing lists, and bug reports. This wealth of information reflects the evolution and status of software projects and has been leveraged by LLMs to build powerful tools that boost developer productivity, efficiency, and velocity.

Most recently, Transformer-based LLMs and other deep neural networks have been employed to tackle key challenges in Software Engineering, such as code generation, automated program repair, code summarization, structural code representations, and defect prediction.

At the AISE Lab, our research explores several topics at the intersection of AI and Software Engineering, including:

Intelligent systems for software creation with large language models: Exploring how generative technologies can support and transform key aspects of software development workflows across diverse contexts and coding tasks, including code generation, summarization, refactoring, and bug fixing.

Trust, Transparency, and Model Behavior: Investigating challenges around explainability, hallucination, memorization, and aligned behavior in learning-based systems as they integrate into developer-facing tools.

Evaluating Generative Capabilities in Practice: Developing strategies to assess evolving generative systems across tasks, time, and toolchains, with a focus on actionable insights and practical relevance.

Hybrid Intelligence in Development Environments: Designing future collaboration paradigms between human developers and adaptive, assistive systems, ranging from conversational interfaces to agentic behavior.

Learning from software histories at scale: Using large-scale project data to uncover patterns, enable automation, and shape more intelligent development support systems, including issue report management, triage, and automated documentation.

Industrial Collaborators/Funding

Amazon Research Award (personal grant) on “Understanding and Regulating Memorization in Large Language Models for Code”
Two fully-funded PhD positions funded by JetBrains Research through the AI4SE ICAI lab. Maliheh is leading two tracks; namely, LLM adaptation for coding tasks (track 2) and Interactive and Aligned IDEs in the LLM Era (track 3).

Awards

ACM Distinguished Paper Award at MSR 2025 conference for our work on “How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning”
ACM Distinguished Paper Award at AIWare 2024 conference for our work on “A Transformer-Based Approach for Smart Invocation of Automatic Code Completion”
Best Tool Award at NLBSE for code comment classification (2023)
Best Attack Award for extracting training data from LLMs at the SatML conference (2023).
Best Tool Award at NLBSE for issue report management (2022).

Thesis and Publications lists

You can find relevant AISE BSc and MSc theses in the TU Delft repository. Additionally, you can find our recent publications here.

CS4570: Machine Learning for Software Engineering Offered in both CS and DSAIT MSc programs.

AISE Team (Lab Manager: Maliheh Izadi)

PhD students

MSc Students

2025: Nadine Kuo (intern at JetBrains Research)
2025: Venelina Pocheva (intern at NXP)
2025: Yash Mundhra (intern at ASML)

Research Assistants

2024-2025: Roham Koohestani

Alumni

2025: Razvan Popescu (MSc student, next a PhD candidate at TU Delft)
2024: Andrei Ionesco (interned at JetBrains Research, next an intern at Microsoft)
2023-2024: Aral de Moor (BSc student and scientific developer, next machine learning engineer at JetBrains Research)
2024: Fabio Salerno (Visiting MSc student), next software engineer at Stema, Italy
2022-2024: Tim van Dam (MSc student, interned at JetBrains Research)
2022-2024: Frank van der Heijden (MSc student, interned at JetBrains Research)
2023-2024: Philippe de Bekker (MSc student, next software engineer at Booking.com)
2023-2024: Remco Schrijver (MSc student, interned at JetBrains Research, next software engineer at Booking.com)
2022-2023: Ali Al-kaswan (MSc student, now a PhD candidate at TU Delft)

Contact

If you have questions or are interested in joining the AISE lab, please reach out to Maliheh Izadi.