Software projects are continually evolving, as developers incorporate changes to refactor code, support new functionality, and fix bugs. To uphold software quality amidst constant changes and also facilitate prompt implementation of critical changes, it is desirable to have automated tools for supporting and driving software evolution. In this thesis, we explore tasks and data and design machine learning approaches which leverage natural language to serve this purpose.

When developers make code changes, they sometimes fail to update the accompanying natural language comments documenting various aspects of the code, which can lead to confusion and vulnerability to bugs. We present our work on alerting developers of inconsistent comments upon code changes and suggesting updates by learning to correlate comments and code.

When a bug is reported, developers engage in a dialogue to collaboratively understand it and ultimately resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend, which delays its implementation through the necessary repository changes. To guide developers in more easily absorbing information relevant towards making these changes and consequently expedite bug resolution, we investigate generating a concise natural language description of the solution by synthesizing relevant content as it emerges in the discussion. We benchmark models for generating solution descriptions and design a classifier for determining when sufficient context for generating an informative description becomes available. We investigate approaches for real-time generation, entailing separately trained and jointly trained classification and generation models. Furthermore, we also study techniques for deriving natural language context from bug report discussions and generated solution descriptions to guide models in generating suggested bug-resolving code changes.