2021/06/28 by Pradeep Dogga, Dogga, Pradeep, Karthik Narasimhan +9 · 2 citations
Computer Science · #Algorithmic program debugging #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Database #Debugging #Distributed #Exploit #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Programming language #Software Engineering Research #Software System Performance and Reliability #Testbed #Workflow #World Wide Web #and Cluster Computing (cs.DC) #cs.DC #cs.LG
paper · pdf · doi:10.48550/arxiv.2106.14347
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/06/28 · openalex publication_date 2021/06/28 · arxiv updated 2021/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
A major difficulty in debugging distributed systems lies in manually determining which of the many available debugging tools to use and how to query its logs. Our own study of a production debugging workflow confirms the magnitude of this burden. This paper explores whether a machine-learning model can assist developers in distributed systems debugging. We present Revelio, a debugging assistant which takes user reports and system logs as input, and outputs debugging queries that developers can use to find a bug's root cause. The key challenges lie in (1) combining inputs of different types (e.g., natural language reports and quantitative logs) and (2) generalizing to unseen faults. Revelio addresses these by employing deep neural networks to uniformly embed diverse input sources and potential queries into a high-dimensional vector space. In addition, it exploits observations from production systems to factorize query generation into two computationally and statistically simpler learning tasks. To evaluate Revelio, we built a testbed with multiple distributed applications and debugging tools. By injecting faults and training on logs and reports from 800 Mechanical Turkers, we show that Revelio includes the most helpful query in its predicted list of top-3 relevant queries 96% of the time. Our developer study confirms the utility of Revelio.