Intelligent Software Tooling for Improving Software Development
It addresses improving software development efficiency and quality for developers, but appears incremental as it builds on existing deep learning advancements in this domain.
The dissertation explores leveraging deep learning on unstructured software engineering artifacts to improve the software development process, aiming to enhance tools for code generation, bug detection, and other tasks.
Software has eaten the world with many of the necessities and quality of life services people use requiring software. Therefore, tools that improve the software development experience can have a significant impact on the world such as generating code and test cases, detecting bugs, question and answering, etc., The success of Deep Learning (DL) over the past decade has shown huge advancements in automation across many domains, including Software Development processes. One of the main reasons behind this success is the availability of large datasets such as open-source code available through GitHub or image datasets of mobile Graphical User Interfaces (GUIs) with RICO and ReDRAW to be trained on. Therefore, the central research question my dissertation explores is: In what ways can the software development process be improved through leveraging DL techniques on the vast amounts of unstructured software engineering artifacts?