conf.researchr.org / Shiqing Ma
Shiqing Ma

Registered user since Wed 20 Dec 2017

Name:Shiqing Ma
Affiliation:Rutgers University
Personal website:https://www.cs.rutgers.edu/~sm2283/
Research interests:Software Engineering, Security, Machine Learning

Contributions

ISSTA 2023 Author of CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement within the Technical Papers-track
ICSE 2022 Author of FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons within the Technical Track-track
Author of To What Extent Do DNN-based Image Classification Models Make Unreliable Inferences? within the Journal-First Papers-track
ISSTA 2021 PC Member in Program Committee
Session Chair of Session 13 (time band 2) Testing Deep Learning Systems 4 (part of Technical Papers)
ICSE 2021 Author of AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair System within the Technical Track-track
ESEC/FSE 2020 Author of C2S: Translating Natural Language Comments to Formal Program Specifications within the Research Papers-track
Author of Correlations between Deep Neural Network Model Coverage Criteria and Model Quality within the Research Papers-track
ISSTA 2020 Author of FineLock: Automatically Refactoring Coarse-grained Locks into Fine-grained Locks within the Tool Demonstration-track
ICSE 2020 Author of TRADER: Trace Divergence Analysis and Embedding Regulation for Debugging Recurrent Neural Networks within the Technical Papers-track
Author of CPC: Automatically Classifying and Propagating Natural Language Comments via Program Analysis within the Technical Papers-track
PLDI 2019 Author of Programming Support for Autonomizing Software within the PLDI Research Papers-track
ICSE 2019 Author of SLF: Fuzzing without Valid Seed Inputs within the Technical Track-track
ESEC/FSE 2018 Author of MODE: Automated Neural Network Model Debugging via State Differential Analysis and Input Selection within the Research Papers-track
* ICSE 2018 * Author of Debugging with Intelligence via Probabilistic Inference within the Technical Papers -track