# Lost in Translation? Converting RegExes for Log Parsing into Dynatrace Pattern Language

arXiv: https://arxiv.org/abs/2506.19539 · HTML: https://mlsanity.com/paper/2506.19539/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://mlsanity.com/paper/2008.06448/) (2020)
- [Log Parsing with Prompt-based Few-shot Learning](https://mlsanity.com/paper/2302.07435/) (2023)
- [Sketch-Driven Regular Expression Generation from Natural Language and Examples](https://mlsanity.com/paper/1908.05848/) (2019)
- [System Log Parsing: A Survey](https://mlsanity.com/paper/2212.14277/) (2022)
- [Data Extraction via Semantic Regular Expression Synthesis](https://mlsanity.com/paper/2305.10401/) (2023)
- [InfeRE: Step-by-Step Regex Generation via Chain of Inference](https://mlsanity.com/paper/2308.04041/) (2023)
- [Why Aren't Regular Expressions a Lingua Franca? An Empirical Study on the Re-use and Portability of Regular Expressions](https://mlsanity.com/paper/2105.04397/) (2021)
- [Regexes are Hard: Decision-making, Difficulties, and Risks in Programming Regular Expressions](https://mlsanity.com/paper/2303.02555/) (2023)
- [Efficient Matching with Memoization for Regexes with Look-around and Atomic Grouping (Extended Version)](https://mlsanity.com/paper/2401.12639/) (2024)
- [Static Detection of DoS Vulnerabilities in Programs that use Regular Expressions (Extended Version)](https://mlsanity.com/paper/1701.04045/) (2017)

## Abstract (arXiv)

Log files provide valuable information for detecting and diagnosing problems in enterprise software applications and data centers. Several log analytics tools and platforms were developed to help filter and extract information from logs, typically using regular expressions (RegExes). Recent commercial log analytics platforms provide domain-specific languages specifically designed for log parsing, such as Grok or the Dynatrace Pattern Language (DPL). However, users who want to migrate to these platforms must manually convert their RegExes into the new pattern language, which is costly and error-prone. In this work, we present Reptile, which combines a rule-based approach for converting RegExes into DPL patterns with a best-effort approach for cases where a full conversion is impossible. Furthermore, it integrates GPT-4 to optimize the obtained DPL patterns. The evaluation with 946 RegExes collected from a large company shows that Reptile safely converted 73.7% of them. The evaluation of Reptile's pattern optimization with 23 real-world RegExes showed an F1-score and MCC above 0.91. These results are promising and have ample practical implications for companies that migrate to a modern log analytics platform, such as Dynatrace.