[PAST EVENT] Cody Watson, Computer Science - Oral Examination
LocationIntegrated Science Center (ISC), Room 0248
540 Landrum Dr
Williamsburg, VA 23185Map this location
Software evolves and therefore requires an evolving field of Software Engineering. The evolution of software can be seen on an individual project level through the software life cycle, as well as on a collective level, as we study the trends and uses of software in the real world. As the needs and requirements of users change, so must software evolve to reflect those changes. This cycle is never ending and has led to continuous and rapid development of software projects. More importantly, it has put a great responsibility on software engineers, causing them to adopt practices and tools that allow them to increase their efficiency. However, these tools suffer the same fate as software designed for the general population; they need to change in order to reflect the user's needs. Fortunately, the demand for this evolving software has given software engineers a plethora of data and artifacts to analyze. The challenge arises when attempting to identify and apply patterns learned from the vast amount of data.
In this oral presentation we explore and develop techniques, which take advantage of the vast amount of software data, to aid developers in software development tasks. Specifically, we exploit the tool of deep learning to automatically learn patterns discovered within previous software data and automatically apply those patterns to present day software development. We first develop an approach that simultaneously learns different representations of source code. We found that the use of multiple representations, such as Identifiers, ASTs, CFGs and byte code can lead to the identification of similar fragments of source code. Through the use of deep learning strategies, we automatically learn these different representations without the requirement of hand-crafted features. Secondly, we designed a novel approach for automating the generation of assert statements through seq2seq learning, with the goal of increasing the efficiency of software testing. Given the test method and the context of the assocaited focal method, we can automatically generate semantically and syntactically correct assert statements for a particular test method. Lastly, to better understand the current impact of deep learning in software engineering, we perform a systematic literature review of top tier conferences and journals. This leads to the generation of a research road map for future applications of deep learning in software engineering. Additionally, we discover and outline important relationships between deep learning models and software engineering tasks, to provide a resource where software engineers can find common practices and limitations when applying these models.
We exemplify that the techniques presented in this dissertation provide a meaningful advancement to the field of software engineering and the automation of software development tasks. We provide analytical evaluations and empirical evidence that substantiate the impact of our findings and usefulness of our approaches toward the software engineering community.
Cody Watson is a Ph.D. candidate at William & Mary, supervised by Dr. Denys Poshyvanyk. He received his Bachelor degree in Computer Science and Biology at Wofford College in 2015. His research focuses on deep learning applications in software engineering.