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Exploring the applicability of Generative AI and LLM on Software Performance and Self-Adaptive Systems

a. Project Description

Generative AI has attracted a lot of attention recently from the research community. Its ability to generate complex solution from similar examples has made it an interesting solution for problems that require a certain degree of creativity. Runtime adaptation to maintain software quality and performance may require similarly creative solutions. In addition, software quality assurance is a multidimensional problem, which necessitates a proper human-system interface to facilitate the work of system administrators. In this sense, runtime adaptation design can also benefit from the use of large language models (LLM), known for their ability to understand and generate natural language. The objective of this project is to explore the ability of LLMs and generative AI to produce self-adaptive strategies for complex distributed systems at runtime. The developed models will need to extract functional and non-functional requirements and produce automatically deployable adaptation strategies, using infrastructure-as-code (IaC) following proper software performance engineering principles. Explainability and justifiability are of utmost importance for the produced strategies.

b. Tasks and responsibilities

The hired student will work towards the review of relevant technologies and its applications on Software Performance so far, as well as on the use of current technologies for the generation of self-adaptive systems. The student will develop the theoretical foundation as well as practical experience on the use of generative AI and LLM tools and methods on Software Performance. The student will aim to publish in top-tier journals, including IEEE Transactions on Software Engineering, Elsevier Journal of Systems and Software, and conferences, such as ICPE, SEAMS, ACSOS, ICSE, and others. The student will also be responsible for supervising and mentoring MSc and BSc students working on the project. The position is open for Winter, Summer or Fall 2024.

c. Required Skills

The student will be asked to demonstrate adequate understanding or expertise in the following topics through relevant courses (on undergraduate or graduate level) or through relevant publications in international conferences or journals. The student should consider applying if they have the expert-level skills and at least 50% of the good-level skills.

d. Application process

Upon contacting the professor to inquire for the position, the student is also asked to submit the following documents: