Further information

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Background

The increasing spread of AI-supported development tools such as GitHub Copilot or ChatGPT is fundamentally changing the way software is created. The term "vibe coding" refers to a development paradigm in which developers largely adopt AI-generated code without fully understanding or reviewing it. While there are short-term productivity gains, the question arises as to what long-term quality risks are associated with this, especially with regard to technical debt. Traditional quality metrics were developed for human-written code and may be of limited suitability for AI-generated code.

Task definition

The target of the work is to systematically investigate technical debt in the context of AI-supported software development. First, existing concepts of technical debt and classical quality metrics are to be analyzed and tested for their suitability for AI-generated code. Possible focal points include:

• Systematic literature analysis on quality properties of AI-generated code and known quality risks
• Investigation of the suitability of classic metrics (e.g. complexity, coupling, duplication) for evaluating AI-generated code
• Empirical analysis and comparison of AI-generated and human-written code based on defined quality criteria
• Derivation of extended or adapted quality criteria specifically for AI-supported development

Own ideas and focal points can be introduced in consultation.