Artificial Intelligence (AI) has rapidly advanced, significantly impacting software engineering through AI-driven tools like ChatGPT and Copilot. These tools, which have garnered substantial commercial interest, rely heavily on the performance of their underlying models, assessed via benchmarks. However, the current focus on performance scores has often overshadowed the quality and rigor of these benchmarks, as emphasized by the absence of studies on this topic. This thesis addresses this gap by reviewing and improving benchmarking practices in the field of AI for software engineering (AI4SE).

First, a categorized overview and analysis of nearly a hundred prominent AI4SE benchmarks from the past decade are provided. Based on this analysis, several challenges and future directions are identified and discussed, including quality control, programming and natural language diversity, task diversity, purpose alignment, and evaluation metrics. Lastly, a significant contribution of this work is the introduction of HumanEvalPro, an enhanced version of the original HumanEval benchmark. HumanEvalPro incorporates more rigorous test cases and edge cases, providing a more accurate and challenging assessment of model performance. The findings demonstrate substantial drops in pass@1 scores for various large language models, highlighting the necessity for well-maintained and comprehensive benchmarks.

This thesis aims to set a new standard for AI4SE benchmarks, providing a foundation for future research and development in this rapidly evolving field.

Given the fundamental profit gained by renewable energy assets in climate control, existing control algorithms are urged to be improved to match power supply and demand optimally. This paper explores various designed cases that lead toward an enhanced definition of a control algorithm with optimized behaviour. The core of improvement is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy.
Based on several thousands of simulations of energy communities in the UK, the proposed smart control algorithm has demonstrated a robust performance and gained notable additional profit in theoretical and practical scenarios using probable data.