At the End of Synthesis: Narrowing Program Candidates. - David Shriver; Sebastian G. Elbaum; Kathryn T. Stolee (ICSE-NIER, 2017)

Toward the development of richer properties for recommender systems. - David Shriver (ICSE (Companion Volume), 2018)

Evaluating Recommender System Stability with Influence-Guided Fuzzing. - David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer; David S. Rosenblum (AAAI, 2019)

Refactoring Neural Networks for Verification. - David Shriver; Dong Xu 021; Sebastian G. Elbaum; Matthew B. Dwyer (CoRR, 2019)

Systematic Generation of Diverse Benchmarks for DNN Verification. - Dong Xu 021; David Shriver; Matthew B. Dwyer; Sebastian G. Elbaum (CAV (1), 2020)

DNNV: A Framework for Deep Neural Network Verification. - David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (CAV (1), 2021)

Reducing DNN Properties to Enable Falsification with Adversarial Attacks. - David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (ICSE, 2021)

Artifact: Reducing DNN Properties to Enable Falsification with Adversarial Attacks. - David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (ICSE (Companion Volume), 2021)

Distribution Models for Falsification and Verification of DNNs. - Felipe Toledo; David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (ASE, 2021)

Reducing DNN Properties to Enable Falsification with Adversarial Attacks. - David Shriver; Sebastian G. Elbaum; Matthew Dwyer 001 (Zenodo, 2021)

Artifact for DNNV: A Framework for Deep Neural Network Verification. - David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (Zenodo, 2021)

Artifact for DNNV: A Framework for Deep Neural Network Verification. - David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (Zenodo, 2021)

Distribution Models for Falsification and Verification of DNNs. - Felipe Toledo; David Shriver; Sebastian G. Elbaum; Matthew Dwyer 001 (Zenodo, 2021)

Distribution Models for Falsification and Verification of DNNs. - Felipe Toledo; David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (Zenodo, 2021)

Distribution Models for Falsification and Verification of DNNs. - Felipe Toledo; David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (Zenodo, 2021)

Deeper Notions of Correctness in Image-Based DNNs: Lifting Properties from Pixel to Entities. - Felipe Toledo; David Shriver; Sebastian G. Elbaum; Matthew B. Dwyer (ESEC/SIGSOFT FSE, 2023)

DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles. - Meriel von Stein; David Shriver; Sebastian G. Elbaum (IEEE Trans. Software Eng., 2023)

The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability. - Stephen Casper; Jieun Yun; Joonhyuk Baek; Yeseong Jung; Minhwan Kim; Kiwan Kwon; Saerom Park; Hayden Moore; David Shriver; Marissa Connor; Keltin Grimes; Angus Nicolson; et al. (CoRR, 2024)

Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing. - Keltin Grimes; Marco Christiani; David Shriver; Marissa Catherine Connor (ICLR, 2025)

A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practice. - Eric Heim; Oren Wright; David Shriver (CoRR, 2025)