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Found 5 papers, 2 papers with code
Evaluation of large language models for assessing code maintainability
Our results show that, controlling for the number of logical lines of codes (LLOC), cross-entropy computed by LLMs is indeed a predictor of maintainability on a class level (the higher the cross-entropy the lower the maintainability).
Badgers: generating data quality deficits with Python
Generating context specific data quality deficits is necessary to experimentally assess data quality of data-driven (artificial intelligence (AI) or machine learning (ML)) applications.
Building AI Innovation Labs together with Companies
Currently, a lot of companies are thinking about whether and how AI and the usage of data will impact their business model and what potential use cases could look like.
Software Engineering for AI-Based Systems: A Survey
Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems
Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment.
