Robert Dyer // UNL

My current research interests are in software engineering and programming languages. I apply data analytics techniques (mining software repositories) to study how developers use programming languages, to help gain insight into features they use and how they use them. I believe future programming languages can be better designed by leveraging data.

I am also working on ARG-V and PAClab for automatically generating benchmark programs for program analysis researchers.

I am a lead researcher and engineer for the Boa language and infrastructure. Boa is a virtual laboratory for data-intensive research on open-source software development. Boa provides better abstractions for easily and efficiently mining ultra-large-scale software repositories.

Recent and Select Publications

ICSE: Boa: A Language and Infrastructure for Analyzing Ultra-Large-Scale Software Repositories Robert Dyer, Hoan Anh Nguyen, Hridesh Rajan, Tien N. Nguyen. May 23, 2013.
Acceptance rate: 85/461 (18.43%)
MSR: Escaping the Time Pit: Pitfalls and Guidelines for Using Time-Based Git Data Distinguished Paper Award Samuel W. Flint, Jigyasa Chauhan, Robert Dyer. May 17, 2021.
Acceptance rate: 48/140 (34.29%)
MODULARITY: Modular Reasoning in the Presence of Event Subtyping Best Paper Award Mehdi Bagherzadeh, Robert Dyer, Rex D. Fernando, José Sánchez, Hridesh Rajan. March 18, 2015.
Acceptance rate: 12/25 (48.00%)
ICSE: Data-Driven Evidence-Based Syntactic Sugar Design David OBrien, Robert Dyer, Tien N. Nguyen, Hridesh Rajan. April 17, 2024.
Acceptance rate: 234/1049 (22.31%)
ICSE: Boidae: Your Personal Mining Platform Brian Sigurdson, Samuel W. Flint, Robert Dyer. April 17, 2024.

Current Students

Samuel W. Flint
Ph.D.
He is interested in the empirical evaluation of programming languages and language constructs using large datasets, as well as through methods like eye-tracking to better understand how developers comprehend code as they read or write.
Salomé Perez-Rosero
Co-advised with Witawas Srisa-an
Ph.D.
Her research interests are at the intersection of software engineering and artificial learning, where she uses advanced repository mining, heuristics to cluster intents of units of software, and ML/DL techniques to study source code patterns that characterize software bugs, vulnerabilities and code fixes.