# Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANA

arXiv: https://arxiv.org/abs/2205.08029 · HTML: https://mlsanity.com/paper/2205.08029/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)

## Built on by (most cited first, 1 of 1)

- [On Enhancing Root Cause Analysis with SQL Summaries for Failures in Database Workload Replays at SAP HANA](https://mlsanity.com/paper/2412.13679/) (2024)

## Abstract (arXiv)

Capturing customer workloads of database systems to replay these workloads during internal testing can be beneficial for software quality assurance. However, we experienced that such replays can produce a large amount of false positive alerts that make the results unreliable or time consuming to analyze. Therefore, we design a machine learning based approach that attributes root causes to the alerts. This provides several benefits for quality assurance and allows for example to classify whether an alert is true positive or false positive. Our approach considerably reduces manual effort and improves the overall quality assurance for the database system SAP HANA. We discuss the problem, the design and result of our approach, and we present practical limitations that may require further research.