{ "schema_version": "0.6.0", "generated_at": "2026-08-09", "site_url": "https://naser.github.io/research-publications/", "schema_url": "https://naser.github.io/research-publications/schemas/paper.schema.json", "vocabulary_url": "https://naser.github.io/research-publications/schemas/vocabularies.json", "bibliography_url": "https://naser.github.io/research-publications/schemas/crossref-bibliography.json", "creator": { "name": "Naser Ezzati-Jivan", "orcid": "0000-0003-1435-6297", "orcid_url": "https://orcid.org/0000-0003-1435-6297", "research_profile_url": "https://naser.github.io/tabs/research/" }, "record_count": 89, "full_text_reviewed_count": 57, "full_text_pending_count": 32, "batch": "expanded-catalog-release", "records": [ { "paper_id": "b-perf-black-box-performance-antipatterns", "title": "B-Perf: Black-box Performance Antipattern Detection Using System-level Execution Tracing", "year": 2026, "authors": [ "Morteza Noferesti", "Mahsa Panahandeh", "Naser Ezzati-Jivan" ], "author_details": [ { "name": "Morteza Noferesti", "orcid": "https://orcid.org/0009-0000-5507-1461", "profile_url": "https://dblp.org/pid/82/10462.html" }, { "name": "Mahsa Panahandeh", "orcid": "https://orcid.org/0000-0002-6369-8982", "profile_url": "https://dblp.org/pid/201/1655.html" }, { "name": "Naser Ezzati-Jivan", "orcid": "https://orcid.org/0000-0003-1435-6297", "profile_url": "https://naser.github.io/" } ], "publication": { "year": 2026, "venue": "ACM/SPEC International Conference on Performance Engineering (ICPE)", "type": "conference paper", "publication_date": "2026-05-03", "pages": "96-107", "publisher": "ACM", "isbn": [ "979-8-4007-2325-4" ], "online_date": "2026-05-03", "print_date": "2026-05-04", "volume": null, "issue": null, "article_number": null, "issn": [], "crossref_type": "proceedings-article" }, "identifiers": { "doi": "10.1145/3777884.3797014" }, "page": "papers/b-perf-black-box-performance-antipatterns/README.md", "json": "papers/b-perf-black-box-performance-antipatterns/paper.json", "bibtex": "papers/b-perf-black-box-performance-antipatterns/citation.bib", "citation_files": { "apa": "papers/b-perf-black-box-performance-antipatterns/citation.apa.txt", "ieee": "papers/b-perf-black-box-performance-antipatterns/citation.ieee.txt", "ris": "papers/b-perf-black-box-performance-antipatterns/citation.ris" }, "page_url": "https://naser.github.io/research-publications/papers/b-perf-black-box-performance-antipatterns/", "canonical_source_url": "https://doi.org/10.1145/3777884.3797014", "status": "published_with_public_full_text", "abstract_available": true, "scholar_eligibility": { "eligible": true, "basis": "complete-visible-author-abstract", "note": "The page displays the complete verified author abstract." }, "description": "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. We evaluate B-Perf on three representative antipatterns—One Lane Bridge, Empty Semi Trucks, and Excessive Dynamic Allocation—and apply it to traces from real multi-threaded applications. The results show that system-level events are often sufficient to expose bottlenecks linked to resource contention and system-level interactions. A key limitation is that kernel traces provide limited visibility into fine-grained in-process behaviour. When performance issues are driven by internal logic or function-level interactions, B-Perf may capture only indirect symptoms and may not reveal the full root cause. 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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.", "evidence_level": "full-text-reviewed", "evidence": { "source_basis": "full-text-reviewed", "coverage": "material paper sections", "summary_origin": "AI-assisted catalog editorial summary", "review_status": "catalog-reviewed; paper-author approval pending", "verified_on": "2026-08-09", "sources": [ { "note": "DBLP/DOI bibliographic record for 10.1109/tse.2025.3645143" }, { "note": "author identity matched to Naser Ezzati-Jivan in the local research catalog" }, { "note": "CARE accepted-author manuscript pp. 1-2: explicit accepted-manuscript statement, exact abstract, problem, context-aware observability motivation, and contribution" }, { "note": "CARE accepted-author manuscript pp. 3-10: service graphs, profiling metrics, trace clustering, graph ranking, and weighted spectrum analysis" }, { "note": "CARE accepted-author manuscript pp. 9-12: baselines, TrainTicket and China Mobile Zhejiang datasets, metrics, accuracy results, and double-root-cause evaluation" }, { "note": "CARE accepted-author manuscript pp. 11-12: HeMiRCA data mismatch and evaluation boundary" }, { "note": "Zenodo record 18021268 provides CARE_IEEETransaction_resubmit2.pdf as public full text; its 1,580,357-byte file and MD5 e6e888ef140bee9ece7d26c74fce559b exactly match the locally inspected 21-page manuscript" }, { "note": "Crossref and the manuscript identify the canonical published DOI as 10.1109/TSE.2025.3645143" } ] }, "versions": [ { "id": "published-version", "label": "Published version", "relation": "version-of-record", "status": "published", "url": "https://doi.org/10.1109/TSE.2025.3645143", "pdf_url": null, "canonical_for_citation": true }, { "id": "accepted-author-manuscript", "label": "Accepted-author manuscript", "relation": "accepted-manuscript", "status": "public_full_text", "url": "https://zenodo.org/records/18021268", "pdf_url": "https://zenodo.org/records/18021268/files/CARE_IEEETransaction_resubmit2.pdf?download=1", "canonical_for_citation": false } ], "tags": [ "system-tracing", "microservices", "root-cause-analysis", "latency-analysis" ], "keywords": [ "distributed traces", "profiling metrics", "context-aware RCA", "microservice diagnosis", "TrainTicket", "spectrum-based fault localization", "PageRank", "China Mobile Zhejiang" ] }, { "paper_id": "dtracomp-distributed-trace-comparison", "title": "DTraComp: Comparing distributed execution traces for understanding intermittent latency sources", "year": 2026, "authors": [ "Maryam Ekhlasi", "Fatemeh Faraji Daneshgar", "Michel Dagenais", "Maxime Lamothe", "Naser Ezzati-Jivan", "Matthew Khouzam" ], "author_details": [ { "name": "Maryam Ekhlasi", "orcid": null, "profile_url": null }, { "name": "Fatemeh Faraji Daneshgar", "orcid": null, "profile_url": null }, { "name": "Michel Dagenais", "orcid": null, "profile_url": null }, { "name": "Maxime Lamothe", "orcid": null, "profile_url": null }, { "name": "Naser Ezzati-Jivan", "orcid": "https://orcid.org/0000-0003-1435-6297", "profile_url": "https://naser.github.io/" }, { "name": "Matthew Khouzam", "orcid": null, "profile_url": null } ], "publication": { "year": 2026, "venue": "Journal of Systems and Software", "type": "journal article", "publication_date": "2026-11", "online_date": null, "print_date": "2026-11", "volume": "241", "issue": null, "pages": "112990", "article_number": "112990", "publisher": "Elsevier BV", "issn": [ "0164-1212" ], "isbn": [], "crossref_type": "journal-article" }, "identifiers": { "doi": "10.1016/j.jss.2026.112990" }, "page": "papers/dtracomp-distributed-trace-comparison/README.md", "json": "papers/dtracomp-distributed-trace-comparison/paper.json", "bibtex": "papers/dtracomp-distributed-trace-comparison/citation.bib", "citation_files": { "apa": "papers/dtracomp-distributed-trace-comparison/citation.apa.txt", "ieee": "papers/dtracomp-distributed-trace-comparison/citation.ieee.txt", "ris": "papers/dtracomp-distributed-trace-comparison/citation.ris" }, "page_url": "https://naser.github.io/research-publications/papers/dtracomp-distributed-trace-comparison/", "canonical_source_url": "https://doi.org/10.1016/j.jss.2026.112990", "status": "published_metadata_record", "abstract_available": false, "scholar_eligibility": { "eligible": false, "basis": "not-eligible", "note": "The page is a discovery record; it does not claim Google Scholar article-host eligibility." }, "description": "This work investigates DTraComp: Comparing distributed execution traces for understanding intermittent latency sources in the context of software performance and systems analysis.", "evidence_level": "metadata-or-abstract-reviewed", "evidence": { "source_basis": "metadata-only", "coverage": "bibliographic metadata", "summary_origin": "AI-assisted catalog editorial summary", "review_status": "catalog-reviewed; paper-author approval pending", "verified_on": "2026-08-09", "sources": [ { "note": "DBLP/DOI bibliographic record for 10.1016/j.jss.2026.112990" }, { "note": "author identity matched to Naser Ezzati-Jivan in the local research catalog" }, { "note": "full-text summary pending source review" } ] }, "versions": [ { "id": "published-version", "label": "Published version", "relation": "version-of-record", "status": "published", "url": "https://doi.org/10.1016/j.jss.2026.112990", "pdf_url": null, "canonical_for_citation": true } ], "tags": [ "system-tracing", "trace-analysis", "latency-analysis", "root-cause-analysis" ], "keywords": [ "distributed execution traces", "trace comparison", "intermittent latency", "latency sources" ] }, { "paper_id": "lmat-adaptive-tracing-language-models", "title": "LMAT: An adaptive tracing approach based on efficient system behavior analysis using language models", "year": 2026, "authors": [ "Kasra Darvishi", "Morteza Noferesti", "Yuvraj Sehgal", "Naser Ezzati-Jivan" ], "author_details": [ { "name": "Kasra Darvishi", "orcid": "https://orcid.org/0000-0001-7755-7227", "profile_url": "https://dblp.org/pid/313/9799.html" }, { "name": "Morteza Noferesti", "orcid": "https://orcid.org/0009-0000-5507-1461", "profile_url": "https://dblp.org/pid/82/10462.html" }, { "name": "Yuvraj Sehgal", "orcid": "https://orcid.org/0009-0002-1980-604X", "profile_url": "https://dblp.org/pid/434/8410.html" }, { "name": "Naser Ezzati-Jivan", "orcid": "https://orcid.org/0000-0003-1435-6297", "profile_url": "https://naser.github.io/" } ], "publication": { "year": 2026, "venue": "Journal of Systems and Software", "type": "journal article", "publication_date": "2026-08", "online_date": null, "print_date": "2026-08", "volume": "238", "issue": null, "pages": "112890", "article_number": "112890", "publisher": "Elsevier BV", "issn": [ "0164-1212" ], "isbn": [], "crossref_type": "journal-article" }, "identifiers": { "doi": "10.1016/j.jss.2026.112890" }, "page": "papers/lmat-adaptive-tracing-language-models/README.md", "json": "papers/lmat-adaptive-tracing-language-models/paper.json", "bibtex": "papers/lmat-adaptive-tracing-language-models/citation.bib", "citation_files": { "apa": "papers/lmat-adaptive-tracing-language-models/citation.apa.txt", "ieee": "papers/lmat-adaptive-tracing-language-models/citation.ieee.txt", "ris": "papers/lmat-adaptive-tracing-language-models/citation.ris" }, "page_url": "https://naser.github.io/research-publications/papers/lmat-adaptive-tracing-language-models/", "canonical_source_url": "https://doi.org/10.1016/j.jss.2026.112890", "status": "published_with_public_full_text", "abstract_available": true, "scholar_eligibility": { "eligible": true, "basis": "complete-visible-author-abstract", "note": "The page displays the complete verified author abstract." }, "description": "We introduce LMAT, a Language Model-based Adaptive Tracing framework designed for host-level observability that provides granular monitoring without excessive overhead. LMAT leverages a multi-task architecture to jointly predict kernel event sequences and classify event durations, thereby capturing both control-flow and temporal dynamics. By continuously comparing live trace data against model predictions, LMAT automatically signals deviations, dynamically adjusting trace granularity only when needed. This approach significantly reduces trace volume, along with associated energy and storage costs, achieving a 70.6% reduction in our experiments. Additionally, LMAT utilizes prediction discrepancies to drive an efficient root-cause classifier, mapping detected anomalies directly to their potential fault sources and providing actionable feedback for operations teams. We evaluate LMAT on two architecturally distinct single-host environments—an Apache2 web-server stack and the Sock Shop containerized microservice benchmark—using kernel traces that include standard workloads, duration-centric noise scenarios, and controlled CPU, disk, memory, and network stress injections. On the Apache workload, LMAT demonstrates up to 97.7% accuracy in anomaly detection and root-cause identification, surpassing state-of-the-art methods relying solely on event sequences. On Sock Shop, the same design remains effective for host-local change detection, while root-cause attribution in the microservice setting remains more challenging. A deployment-oriented overhead study shows that under a stable load, asynchronous LMAT inference introduces no measurable additional tail-latency overhead beyond tracing, while maintaining consistent throughput. Our findings illustrate that LMAT is a practical approach for adaptive tracing in the evaluated single-host environments, improving detection quality while keeping deployment overhead negligible.", "evidence_level": "full-text-reviewed", "evidence": { "source_basis": "full-text-reviewed", "coverage": "material paper sections", "summary_origin": "AI-assisted catalog editorial summary", "review_status": "catalog-reviewed; paper-author approval pending", "verified_on": "2026-08-09", "sources": [ { "note": "DBLP/DOI bibliographic record for 10.1016/j.jss.2026.112890" }, { "note": "author identity matched to Naser Ezzati-Jivan in the local research catalog" }, { "note": "Crossref DOI record verified 2026-08-09: the version of record is licensed CC BY 4.0 from 2026-04-08" }, { "note": "LMAT proof PDF pp. 1-4: exact abstract, adaptive tracing motivation, four contributions, system loop, and language-model/event-duration design" }, { "note": "LMAT proof PDF pp. 5-8: LTTng event representation, duration modeling, multi-task loss, error vectors, HDBSCAN prototypes, and adaptive control" }, { "note": "LMAT proof PDF pp. 8-11: Apache and Sock Shop datasets, environments, fault injection, tracing protocol, and event subsets" }, { "note": "LMAT proof PDF pp. 12-16: change-detection, root-cause, trace-reduction, latency, throughput, and storage-cost results" }, { "note": "LMAT proof PDF pp. 17-18: generalization boundaries, threats to validity, limitations, and future work" }, { "note": "LMAT proof PDF p. 3: implementation https://github.com/kasra-darvishi/adaptive_tracer and dataset https://zenodo.org/records/10437041" }, { "note": "Locally reviewed 19-page Elsevier proof PDF SHA-256: CA95BE05F9B4E91CD697477AEEAEB002A9DE4D0E6D992050EDC6E4C603E421EA; the proof is evidence only and is not redistributed" } ] }, "versions": [ { "id": "published-version", "label": "Published version", "relation": "version-of-record", "status": "published", "url": "https://doi.org/10.1016/j.jss.2026.112890", "pdf_url": null, "canonical_for_citation": true } ], "tags": [ "kernel-tracing", "system-tracing", "llm-assisted-analysis", "observability", "anomaly-detection", "root-cause-analysis", "trace-reduction" ], "keywords": [ "adaptive tracing", "LTTng", "kernel events", "system-call sequences", "event-duration modeling", "language models", "LSTM", "BERT", "multi-task learning", "HDBSCAN", "change detection", "root-cause analysis", "Apache", "Sock Shop", "OpenTelemetry", "Prometheus", "stress-ng", "trace reduction", "trace overhead" ] }, { "paper_id": "observability-runtime-governance-agentic-ai-systems", "title": "Observability and Runtime Governance for Agentic AI Systems", "year": 2026, "authors": [ "Naser Ezzati-Jivan", "Maryam Ekhlasi" ], "author_details": [ { "name": "Naser Ezzati-Jivan", "orcid": "https://orcid.org/0000-0003-1435-6297", "profile_url": "https://naser.github.io/" }, { "name": "Maryam Ekhlasi", "orcid": "https://orcid.org/0009-0007-9147-869X", "profile_url": "https://dblp.org/pid/409/3420.html" } ], "publication": { "year": 2026, "venue": "Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering (FSE)", "type": "conference paper", "publication_date": "2026-07-05", "online_date": "2026-07-17", "print_date": "2026-07-05", "volume": null, "issue": null, "pages": "64-65", "article_number": null, "publisher": "ACM", "issn": [], "isbn": [], "crossref_type": "proceedings-article" }, "identifiers": { "doi": "10.1145/3803437.3804904" }, "page": "papers/observability-runtime-governance-agentic-ai-systems/README.md", "json": "papers/observability-runtime-governance-agentic-ai-systems/paper.json", "bibtex": "papers/observability-runtime-governance-agentic-ai-systems/citation.bib", "citation_files": { "apa": "papers/observability-runtime-governance-agentic-ai-systems/citation.apa.txt", "ieee": "papers/observability-runtime-governance-agentic-ai-systems/citation.ieee.txt", "ris": "papers/observability-runtime-governance-agentic-ai-systems/citation.ris" }, "page_url": "https://naser.github.io/research-publications/papers/observability-runtime-governance-agentic-ai-systems/", "canonical_source_url": "https://doi.org/10.1145/3803437.3804904", "status": "published_with_public_abstract", "abstract_available": true, "scholar_eligibility": { "eligible": true, "basis": "complete-visible-author-abstract", "note": "The page displays the complete verified author abstract." }, "description": "Agentic AI systems increasingly plan, call tools, coordinate with other agents, and act on external software services. Their failures are difficult to diagnose because control flow is generated at run-time, execution is stochastic, and incorrect behavior often appears as goal drift, unsafe tool use, or false success reports rather than as crashes. This tutorial presents a software engineering workflow for AgentOps that connects high-level intent, model-level decisions, tool calls, memory accesses, and low-level system effects. 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For instance, by examining the internal behaviors of different fast or slow web requests more closely and by clustering and comparing their internal executions, one can determine what causes some requests to run slowly or behave in unexpected ways. In this paper, we propose a method of extracting the internal behavior of web requests as well as introduce a pipeline that detects performance issues in web requests and provides insights into their root causes. First, low-level and fine-grained information regarding each request is gathered by tracing both the user space and the kernel space. Second, further information is extracted and fed into an outlier detector. Finally, these outliers are then clustered by their behavior, and each group is analyzed separately. Experiments revealed that this pipeline is indeed able to detect slow web requests and provide additional insights into their true root causes. 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