Language Feature Studies
Programming languages are often designed by people by utilizing their existing expertise and experience. New languages often add features based on features seen in other (often popular) programming languages. I believe that programming language design can be more data-driven.
To that end, we utilize mining software repositories techniques to analyze hundreds of thousands of open-source projects so we can get a better handle on how developers actually use programming language features.
Such data can then be leveraged to help provide more data-driven inspirations for new programming language designs, or to help provide concrete evidence for the support for features that are assumed to be popular in existing languages.
Related Publications
- NEW EMSE: How Do Developers Use Type Inference: An Exploratory Study in Kotlin Samuel W. Flint, Ali M. Keshk, , Hamid Bagheri. October 28, 2024.
- ICSE:
Data-Driven Evidence-Based Syntactic Sugar Design
David OBrien,
,
Tien N. Nguyen,
Hridesh Rajan. April 17, 2024.Acceptance rate: 234/1049 (22.31%)
- MSR:
Method Chaining Redux: An Empirical Study of Method Chaining in Java, Kotlin, and Python
Ali M. Keshk,
. May 15, 2023.Acceptance rate: 43/115 (37.39%)
- An Empirical Study on the Classification of Python Language Features Using Eye-Tracking Jigyasa Chauhan. December 1, 2022. A master's thesis at University of Nebraska-Lincoln.
- ESEC/FSE:
An Exploratory Study on the Predominant Programming Paradigms in Python Code
,
Jigyasa Chauhan. November 16, 2022.Acceptance rate: 99/449 (22.05%)
- ICSE:
Mining Billions of AST Nodes to Study Actual and Potential Usage of Java Language Features
,
Hridesh Rajan,
Hoan Anh Nguyen,
Tien N. Nguyen. June 3, 2014.Acceptance rate: 99/495 (20.00%)
Collaborators
Students
UNL, Master'sGraduated: December 2022
