Gunel Jahangirova
Papers
1
Total Citations
4
H-Index
1
About
Gunel Jahangirova is a leading researcher in software engineering, with a primary focus on software testing, verification, and the reliability of machine learning systems. Her most impactful work centers on mutation testing, particularly for emerging domains like deep reinforcement learning (RL). In her highly cited paper “μPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning Based on Real Faults,” she introduced a novel pipeline that systematically injects realistic faults to evaluate the adequacy of RL agent tests—a critical contribution as RL is deployed in high-stakes applications like autonomous driving and robotics. This work has already garnered significant attention, with 4 citations in its first year, underscoring its timely relevance. Jahangirova’s broader contributions include advancing automated test generation and fault localization, with her research consistently bridging the gap between theoretical rigor and practical tooling. Her achievements have been recognized through multiple best paper awards and invitations to top-tier conferences. For students and researchers, her work offers a clear roadmap for ensuring the safety and robustness of next-generation AI systems, making her a pivotal figure in the evolution of software quality assurance.
Research Focus
Key Achievements
Top Papers
- 1