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description You are expected to investigate how software development organizations can leverage internal process metrics and external usage or stakeholder data to improve software quality and delivery
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retrainable pipelines, model-wrapping services, LLM prompting logic, data pre/post-processing components, and explainability hooks. The research will examine how design patterns, principles, and quality
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on developing methods for the verification and validation of systems that embed machine learning or generative models, addressing challenges such as non-determinism, data drift, and explainability. The project
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monitoring strategies, and how these aspects affect the co-evolution of code and data pipelines. The research aims to develop empirical insights and practices that enable more sustainable, observable, and
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recommendations. -Contact information for two references (reference letters are not required at this point). -An appendix with current publications (if applicable). -A link to your thesis (latest/highest degree
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from quantitative and qualitative research and from tool development for the collection and analysis of software development data. We also value experience from empirical research in close collaboration