VLDB 2026 Research / reviewers in the wild / expert
Isami Akasaka
dblp:409/8084
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Debugging and program repair · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Debugging and program repair
root cause analysis |
0.9 | 1 | 2025 | LogSage: An LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation · ASE 2025 |
Methods — techniques the papers use, named apart from their topics
retrieval-augmented generation · 0.9log preprocessing · 0.9large language model · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | LogSage: An LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial ValidationabstractContinuous Integration and Deployment (CI/CD) pipelines are critical to modern software engineering, yet diagnosing and resolving their failures remains complex and labor-intensive. We present LogSage, the first end-to-end LLM-powered framework for root cause analysis (RCA) and automated remediation of CI/CD failures. LogSage employs a token-efficient log preprocessing pipeline to filter noise and extract critical errors, then performs structured diagnostic prompting for accurate RCA. For solution generation, it leverages retrieval-augmented generation (RAG) to reuse historical fixes and invokes automation fixes via LLM tool-calling.On a newly curated benchmark of 367 GitHub CI/CD failures, LogSage achieves over 98% precision, near-perfect recall, and an F1 improvement of more than 38% points in the RCA stage, compared with recent LLM-based baselines. In a yearlong industrial deployment at ByteDance, it processed over 1.07M executions, with end-to-end precision exceeding 80%. These results demonstrate that LogSage provides a scalable and practical solution for automating CI/CD failure management in real-world DevOps workflows. Weiyuan Xu, Juntao Luo, Kaixin Sui, Qijun Ma, Isami Akasaka, Xiaoxue Shi |
ASE | 7 |