Enhancing code review for improved code quality with language model-driven approaches

Code review is essential for maintaining software development standards, yet achieving effective reviews and issue resolution remains challenging. This thesis introduces RefineCode, an application tool to find actionable code reviews and provide similar code reviews as references within an organization, aiding developers in resolving issues effectively. To this end, we collected 9,500 code reviews from five private projects in an industrial setting and empirically evaluated various classification methods for identifying actionable code reviews. RefineCode automatically recommends relevant solutions from Stack Overflow based on textual similarity and entity linking between code reviews and Stack Overflow issues. Additionally, it integrates a chatbot feature, leveraging large language models to propose potential solutions for actionable code reviews. These features empower developers to make informed decisions, enhancing code quality by guiding issue resolution without reinforcing misunderstandings.