A&S Graduate Studies
This calendar presented by
A&S Graduate Studies
[PAST EVENT] Cody Watson, Computer Science - Dissertation Defense
July 17, 2020
9am - 11am
Abstract:
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 dissertation, 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 attempted to better understand the current impact of deep learning in software engineering by performing a systematic literature review of top tier conferences and journals. This review provides guidelines and common pitfalls for researchers to consider when implementing DL(Deep Learning) approaches in SE (Software Engineering). In addition, the review provides a research road map for areas within SE where DL could be applicable. Our next piece of work developed an approach that simultaneously learned different representations of source code for the task of clone detection. We found that the use of multiple representations, such as Identifiers, ASTs, CFGs and bytecode can lead to the identification of similar code fragments. Through the use of deep learning strategies, we automatically learned these different representations without the requirement of hand-crafted features. Lastly, we designed a novel approach for automating the generation of assert statements through seq2seqlearning, with the goal of increasing the efficiency of software testing. Given the test method and the context of the associated focal method, we automatically generated semantically and syntactically correct assert statements for a given, unseen test method. 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.
Bio:
Cody Watson ahs been working on his Ph.D. degree in the Department of Computer Science at William & Mary since Fall 2015. He is working with Dr. Denys Poshyvanyk in the fields of software engineering, deep learning, software evolution and software testing. Cody Watson received his M.S degree from William & Mary in 2017 and a B.S / B.A degree from Wofford College in 2015.
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 dissertation, 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 attempted to better understand the current impact of deep learning in software engineering by performing a systematic literature review of top tier conferences and journals. This review provides guidelines and common pitfalls for researchers to consider when implementing DL(Deep Learning) approaches in SE (Software Engineering). In addition, the review provides a research road map for areas within SE where DL could be applicable. Our next piece of work developed an approach that simultaneously learned different representations of source code for the task of clone detection. We found that the use of multiple representations, such as Identifiers, ASTs, CFGs and bytecode can lead to the identification of similar code fragments. Through the use of deep learning strategies, we automatically learned these different representations without the requirement of hand-crafted features. Lastly, we designed a novel approach for automating the generation of assert statements through seq2seqlearning, with the goal of increasing the efficiency of software testing. Given the test method and the context of the associated focal method, we automatically generated semantically and syntactically correct assert statements for a given, unseen test method. 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.
Bio:
Cody Watson ahs been working on his Ph.D. degree in the Department of Computer Science at William & Mary since Fall 2015. He is working with Dr. Denys Poshyvanyk in the fields of software engineering, deep learning, software evolution and software testing. Cody Watson received his M.S degree from William & Mary in 2017 and a B.S / B.A degree from Wofford College in 2015.