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KHAN Zanis Ali

Main Referenced Co-authors
BIANCULLI, Domenico  (3)
BRIAND, Lionel  (3)
SHIN, Donghwan  (2)
Shin, Donghwan (1)
Main Referenced Keywords
Anomaly Detection (1); Anomaly detection (1); Computer Science - Software Engineering (1); Log Analysis (1); Log Parsing (1);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab) (2)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation (1)
Main Referenced Disciplines
Computer science (4)

Publications (total 4)

The most downloaded
1721 downloads
KHAN, Z. A., SHIN, D., BIANCULLI, D., & BRIAND, L. (2022). Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 1095-1106). New York, NY, United States: ACM. doi:10.1145/3510003.3510101 https://hdl.handle.net/10993/50072

The most cited

67 citations (OpenAlex)

KHAN, Z. A., SHIN, D., BIANCULLI, D., & BRIAND, L. (2022). Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 1095-1106). New York, NY, United States: ACM. doi:10.1145/3510003.3510101 https://hdl.handle.net/10993/50072

KHAN, Z. A., Shin, D., BIANCULLI, D., & BRIAND, L. (2024). Impact of Log Parsing on Deep Learning-Based Anomaly Detection. Empirical Software Engineering, 29, 139:1--139:33. doi:10.1007/s10664-024-10533-w
Peer Reviewed verified by ORBi

KHAN, Z. A. (2023). On log parsing and log-based anomaly detection: an empirical evaluation [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/60158

KHAN, Z. A., SHIN, D., BIANCULLI, D., & BRIAND, L. (2022). Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 1095-1106). New York, NY, United States: ACM. doi:10.1145/3510003.3510101
Peer reviewed

SHIN, D., KHAN, Z. A., BIANCULLI, D., & BRIAND, L. (2021). A Theoretical Framework for Understanding the Relationship Between Log Parsing and Anomaly Detection. In Proceedings of the 21st International Conference on Runtime Verification (pp. 277-287). Springer. doi:10.1007/978-3-030-88494-9_16
Peer reviewed

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