The field of software engineering is witnessing significant developments in code generation and software quality. Researchers are exploring innovative approaches to improve the accuracy and reliability of code generation models, such as large language models (LLMs). One notable direction is the use of semantic triangulation to reduce hallucinations in LLM-generated code, which has shown promising results in increasing the reliability of generated code. Another area of focus is the development of datasets and frameworks for software quality assessment, such as the Software Quality Dataset (SQuaD), which provides a comprehensive collection of software quality metrics. Additionally, there is a growing interest in applying graph neural networks (GNNs) to code smell refactoring and other software engineering tasks. Noteworthy papers in this area include: ExPairT-LLM, which presents an exact learning algorithm for code selection that outperforms state-of-the-art code selection algorithms. Reducing Hallucinations in LLM-Generated Code via Semantic Triangulation, which introduces semantic triangulation to increase the reliability of generated code. A Code Smell Refactoring Approach using GNNs, which proposes a graph-based deep learning approach for code smell refactoring. The Search for Constrained Random Generators, which presents a novel approach to the constrained random generation problem using deductive program synthesis. DataOps-driven CI/CD for analytics repositories, which proposes a qualitative design for a DataOps-aligned validation framework. From Code Smells to Best Practices, which systematically identifies code smells that lead to resource leaks in ML applications and derives best practices for mitigating them. A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code, which provides a causal perspective on measuring, explaining, and mitigating smell propensity in LLM-generated code.