conf.researchr.org / Jin Song Dong

Not registered as user yet

Name:Jin Song Dong
Country:Singapore
Affiliation:National University of Singapore

Contributions

ICSE 2023 Author of DeepArc: Modularizing Neural Networks for the Model Maintenance within the Technical Track-track
ESEC/FSE 2022 Author of RegMiner: Mining Replicable Regression Dataset from Code Repositories within the Demonstrations-track
Author of Graph-Based Seed Object Synthesis for Search-Based Unit Testing within the ESEC/FSE 2021-track
ASE 2022 Author of Repairing Failure-inducing Inputs with Input Reflection within the Research Papers-track
Author of Are They Toeing the Line? Diagnosing Privacy Compliance Violations among Browser Extensions within the Research Papers-track
ISSTA 2022 Author of RegMiner: Towards Constructing a Large Regression Dataset from Code Evolution History within the Technical Papers-track
ICSE 2022 Author of gDefect4DL: A Dataset of General Real-World Deep Learning Program Defects within the DEMO - Demonstrations-track
ASE 2021 Author of A First Look at the Effect of Deep Learning inCoverage-guided Fuzzing within the Late Breaking Results-track
ESEC/FSE 2021 Author of Graph-Based Seed Object Synthesis for Search-Based Unit Testing within the Research Papers-track
ISSTA 2021 Author of Identifying Privacy Weaknesses from Multi-party Trigger-Action Integration Platforms within the Technical Papers-track
ICSE 2021 Committee Member in Program Committee within the Journal-First Papers-track
Author of Self-Checking Deep Neural Networks in Deployment within the Technical Track-track
ASE 2020 Author of Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction within the Research Papers-track
ISSTA 2020 Author of Recovering Fitness Gradients for Interprocedural Boolean Flags in Search-Based Testing within the Technical Papers-track
ICSE 2020 Author of White-box Fairness Testing through Adversarial Sampling within the Technical Papers-track
Author of Explaining Regressions via Alignment Slicing and Mending within the Journal First-track