Accurate translation of bug reports is critical for efficient collaboration in global software development. In this study, we conduct the first comprehensive evaluation of machine translation (MT) performance on bug reports, analyzing the capabilities of DeepL, AWS Translate, and ChatGPT using data from the Visual Studio Code GitHub repository, specifically focusing on reports labeled with the english-please tag. To thoroughly assess the accuracy and effectiveness of each system, we employ multiple machine translation metrics, including BLEU, BERTScore, COMET, METEOR, and ROUGE. Our findings indicate that DeepL consistently outperforms the other systems across most automatic metrics, demonstrating strong lexical and semantic alignment. AWS Translate performs competitively, particularly in METEOR, while ChatGPT lags in key metrics. This study underscores the importance of domain adaptation for translating technical texts and offers guidance for integrating automated translation into bug-triaging workflows. Moreover, our results establish a foundation for future research to refine machine translation solutions for specialized engineering contexts. The code and dataset for this paper are available at GitHub: https://github.com/av9ash/gitbugs/tree/main/multilingual.

Amazon's suite of ML services is constantly expanding. From having capabilities of building custom ML pipelines in SageMaker to a versatile set of AutoML services, options to deploy and tackle ML problems are limitless. Neural Machine Translation is a theoretically intense field and requires deep knowledge of LSTMs and Deep Learning frameworks such as TensorFlow and PyTorch. For this article we will explore AWS Translate, a Neural Machine Translation tool that supports 71 languages and lets you build applications with a simple API call. This article is a continuation of the Auto-ML on AWS series, check out the Rekognition and Comprehend articles for the first two parts.