Mahsa Panahandeh

dblp:201/1655 · also Mahsa Sadat Panahandeh · DBLP profile ↗
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5ranked-venue papers
3as first author
4since 2021 · last 2026
0000-0002-6369-8982ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 5 · 3 first-author · 4 since 2021
YearPublicationVenuePosition
2026 B-Perf: Black-box Performance Antipattern Detection Using System-level Execution Tracing
abstract
Performance antipatterns capture recurring behaviours that degrade software efficiency. Black-box approaches aim to detect such issues without modifying the application. This paper presents B-Perf, a system-level black-box method that reconstructs execution, memory, and messaging behaviour from kernel-level traces. By analysing scheduling, allocation, and communication events, B-Perf derives workload-dependent behavioural trends and reports antipattern indicators grounded in resource usage and contention. To handle large trace volumes, the approach follows a pipeline of workload generation, event gathering, trace handling, and antipattern inference.
Morteza Noferesti, Mahsa Panahandeh, Naser Ezzati-Jivan
ICPE2
2026 CARE: Context Aware Root Cause Identification Using Distributed Traces and Profiling Metrics
abstract
Root cause localization in microservices is challenging due to intricate service dependencies and the high volume and heterogeneity of collected monitoring data, which add complexity to the analysis. Conventional methods often overlook nuanced propagation patterns and contextual interactions among services, and they are limited in leveraging multi-source observability data for comprehensive root cause identification. This study introduces CARE, a context-aware, spectrum-analysis-based approach that integrates multi-source observability data and employs network analysis to prioritize the contextual significance of components in propagating anomalies across individual services, service communities, and requests. CARE’s weighted spectrum analysis leverages these prioritized contexts to pinpoint underlying performance issues. Evaluations on 224 cases from the TrainTicket benchmark and a real-world Internet service provider’s production system demonstrate CARE’s substantial accuracy gains, with top-1 accuracy of 72%-89% and top-5 accuracy of 84%-99% for single root causes, outperforming baselines by 8%-41%. CARE also shows significant improvements in dual root cause identification, exceeding baseline performance by 18%-37%, all while maintaining efficient resource usage, establishing CARE as a robust and resource-effective solution for root cause localization in complex microservice environments.
Mahsa Panahandeh, Naser Ezzati-Jivan, Abdelwahab Hamou-Lhadj, James Miller 0001
IEEE Trans. Software Eng.1
2024 ServiceAnomaly: An anomaly detection approach in microservices using distributed traces and profiling metrics
Mahsa Panahandeh, Abdelwahab Hamou-Lhadj, Mohammad Hamdaqa, James Miller 0001
J. Syst. Softw.1
2021 MUPPIT: a method for using proper patterns in model transformations
Mahsa Panahandeh, Mohammad Hamdaqa, Bahman Zamani, Abdelwahab Hamou-Lhadj
Softw. Syst. Model.1
2018 Black-box tree test case generation through diversity
Ali Shahbazi, Mahsa Panahandeh, James Miller 0001
Autom. Softw. Eng.2