Forskningsoutput per år
Forskningsoutput per år
Personlig profil
Forskning
I am a WASP-funded Ph.D. student at the Computer Science Department, Lund University. Before my Ph.D. study, I had worked as a Software Engineer in the industry for about six years. My research aims to improve software testing of autonomous systems, where I have a particular focus on the autonomous driving domain and the critical scenario identification approaches. That is, to explore and identify critical scenarios that may lead to severe consequences in traffic and test the autonomous driving systems using those scenarios.
Fingeravtryck
Utforska forskningsämnen där Qunying Song är aktiv. Dessa ämnesetiketter kommer från personens arbeten. Tillsammans bildar de ett unikt fingeravtryck.
- 1 Liknande profiler
Nätverk
Externa samarbeten med länder/områden de senaste 5 åren
Forskningsoutput
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Industry-academia collaboration for realism in software engineering research: Insights and recommendations
Song, Q. & Runeson, P., 2023 apr., I: Information and Software Technology. 156, 107135.Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review
Öppen tillgång -
A Scenario Distribution Model for Effective and Efficient Testing of Autonomous Driving Systems
Song, Q., Runeson, P. & Persson, S., 2022 sep. 1, (Accepted/In press) Proceedings of the first Autonomous Software Testing Workshop. Association for Computing Machinery (ACM)Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review
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Critical scenario identification for realistic testing of autonomous driving systems
Song, Q., Tan, K., Runeson, P. & Persson, S., 2022 dec. 3, (E-pub ahead of print) I: Software Quality Journal.Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review
Öppen tillgång -
Critical Scenario Identification for Testing of Autonomous Driving Systems
Song, Q., 2022 mars 8, Lund: Lund University. 110 s.Forskningsoutput: Avhandling › Licentiatavhandling
Fil178 Nedladdningar (Pure) -
Exploring ML testing in practice - Lessons learned from an interactive rapid review with Axis Communications
Song, Q., Borg, M., Engström, E., Ardö, H. & Rico, S., 2022 maj 16, 2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE - Institute of Electrical and Electronics Engineers Inc.Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review
Fil9 Nedladdningar (Pure)
Projekt
- 1 Aktiva
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Software testing of autonomous systems
Song, Q., Runeson, P. & Engström, E.
2020/01/01 → …
Projekt: Avhandling