Publications
A selection of representative work across programming languages, data-driven software engineering, and trustworthy AI.
For the complete and current list, see my lab's publications and DBLP.
Book
- Hridesh Rajan, An Experiential Introduction to Principles of Programming Languages. MIT Press, Cambridge, MA, 304 pp., May 2022.
Programming languages and modular reasoning
- Hridesh Rajan and Gary T. Leavens, Ptolemy: A Language with Quantified, Typed Events. ECOOP 2008, 22nd European Conference on Object-Oriented Programming. Reconciles separation of crosscutting concerns with modular reasoning.
Data-driven software engineering
- Robert Dyer, Hoan Anh Nguyen, Hridesh Rajan, and Tien N. Nguyen, Boa: A Language and Infrastructure for Analyzing Ultra-Large-Scale Software Repositories. ICSE 2013, 35th International Conference on Software Engineering. The first cyberinfrastructure for big-data-driven discovery in software engineering.
Software engineering of AI-enabled systems and trustworthy AI
- Md Johirul Islam, Giang Nguyen, Rangeet Pan, and Hridesh Rajan, A Comprehensive Study on Deep Learning Bug Characteristics. ESEC/FSE 2019. The first rigorous taxonomy of defects and repairs in neural-network code.
- Rangeet Pan and Hridesh Rajan, On Decomposing a Deep Neural Network into Modules. ESEC/FSE 2020. Started the sub-field of decomposition and modularity for deep neural networks.
- Mohammad Wardat, Wei Le, and Hridesh Rajan, DeepLocalize: Fault Localization for Deep Neural Networks. ICSE 2021. The first approach for bug localization in deep learning models.
- Shibbir Ahmed, Hongyang Gao, and Hridesh Rajan, Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment. ICSE 2024.
- Ruchira Manke, Mohammad Wardat, Foutse Khomh, and Hridesh Rajan, Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning. ICSE 2025.
Author orderings and venues follow the citations on my lab site; please refer there for the authoritative record.