English Please: Evaluating Machine Translation for Multilingual Bug Reports

• URL: http://arxiv.org/abs/2502.14338v2
• Date: Tue, 04 Mar 2025 23:24:09 GMT
• Title: English Please: Evaluating Machine Translation for Multilingual Bug Reports
• Authors: Avinash Patil, Aryan Jadon,
• Abstract summary: This study is the first comprehensive evaluation of machine translation (MT) performance on bug reports.<n>We employ multiple machine translation metrics, including BLEU, BERTScore, COMET, METEOR, and ROUGE.<n>DeepL consistently outperforms the other systems, demonstrating strong lexical and semantic alignment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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