Qunying Song

According to our database, Qunying Song authored at least 20 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry.

Qunying Song, Ali Nouri, Håkan Sivencrona, Federica Sarro

CoRR, May, 2026

AI Act high-risk AI compliance challenge and industry impact: A multiple case study.

Matthias Wagner, Qunying Song, Markus Borg, Emelie Engström, Michal Lysek

Inf. Softw. Technol., 2026

2025

Synthetic versus real: an analysis of critical scenarios for autonomous vehicle testing.

Qunying Song, Avner Bensoussan, Mohammad Reza Mousavi

Autom. Softw. Eng., November, 2025

Primary Studies.

Qunying Song, He Ye, Mark Harman, Federica Sarro

Dataset, November, 2025

Generative AI for Testing of Autonomous Driving Systems: A Survey.

Qunying Song, He Ye, Mark Harman, Federica Sarro

CoRR, August, 2025

AI Alignment for Ethical Compliance and Risk Mitigation in Industrial Applications.

Rushali Gupta, Qunying Song, Matthias Wagner, Emelie Engström, Emma Söderberg, Markus Borg, Per Runeson

Proceedings of the Product-Focused Software Process Improvement, 2025

2024

TTV Data Extraction.

Patricia Lago, Per Runeson, Qunying Song, Roberto Verdecchia

Dataset, August, 2024

Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios.

Qunying Song, Emelie Engström, Per Runeson

ACM Trans. Softw. Eng. Methodol., May, 2024

NLP-TestGen.

Qunying Song, Rune Anderberg, Henrik Olsson, Per Runeson

Dataset, January, 2024

Critical Scenario Identification for Testing of Realistic Autonomous Driving Systems.

Qunying Song

PhD thesis, 2024

An Empirically Grounded Path Forward for Scenario-Based Testing of Autonomous Driving Systems.

Qunying Song, Emelie Engström, Per Runeson

Proceedings of the Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, 2024

Generating Executable Test Scenarios from Autonomous Vehicle Disengagements using Natural Language Processing.

Qunying Song, Rune Anderberg, Henrik Olsson, Per Runeson

Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 2024

Threats to Validity in Software Engineering - hypocritical paper section or essential analysis?

Patricia Lago, Per Runeson, Qunying Song, Roberto Verdecchia

Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2024

2023

Threats to validity in software engineering research: A critical reflection.

Roberto Verdecchia, Emelie Engström, Patricia Lago, Per Runeson, Qunying Song

Inf. Softw. Technol., December, 2023

Critical scenario identification for realistic testing of autonomous driving systems.

Qunying Song, Kaige Tan, Per Runeson, Stefan Persson

Softw. Qual. J., June, 2023

Industry-academia collaboration for realism in software engineering research: Insights and recommendations.

Qunying Song, Per Runeson

Inf. Softw. Technol., April, 2023

2022

A Scenario Distribution Model for Effective and Efficient Testing of Autonomous Driving Systems.

Qunying Song, Per Runeson, Stefan Persson

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022

Exploring ML testing in practice: lessons learned from an interactive rapid review with axis communications.

Qunying Song, Markus Borg, Emelie Engström, Håkan Ardö, Sergio Rico

Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, 2022

2021

Concepts in Testing of Autonomous Systems: Academic Literature and Industry Practice.

Qunying Song, Emelie Engström, Per Runeson

Proceedings of the 1st IEEE/ACM Workshop on AI Engineering - Software Engineering for AI, 2021

An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software.

Qunying Song, Kaige Tan, Per Runeson, Stefan Persson

Proceedings of the 2021 IEEE International Conference on Artificial Intelligence Testing, 2021