# Revelio: ML-Generated Debugging Queries for Distributed Systems

arXiv: https://arxiv.org/abs/2106.14347 · HTML: https://mlsanity.com/paper/2106.14347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 95 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System](https://mlsanity.com/paper/1802.08979/) (2018)
- [Program Synthesis using Natural Language](https://mlsanity.com/paper/1509.00413/) (2015)
- [Deep API Learning](https://mlsanity.com/paper/1605.08535/) (2016)
- [Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge](https://mlsanity.com/paper/1608.03000/) (2016)
- [Programming Protocol-Independent Packet Processors](https://mlsanity.com/paper/1312.1719/) (2013)
- [DeepTriage: Automated Transfer Assistance for Incidents in Cloud Services](https://mlsanity.com/paper/2012.03665/) (2020)
- [MeDICINE: Rapid Prototyping of Production-Ready Network Services in Multi-PoP Environments](https://mlsanity.com/paper/1606.05995/) (2016)

## Abstract (arXiv)

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.