# AVIATE: Exploiting Translation Variants of Artifacts to Improve IR-based Traceability Recovery in Bilingual Software Projects

arXiv: https://arxiv.org/abs/2409.19304 · HTML: https://mlsanity.com/paper/2409.19304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2024.

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

- [Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity](https://mlsanity.com/paper/2509.05585/) (2025)

## Abstract (arXiv)

Traceability plays a vital role in facilitating various software development activities by establishing the traces between different types of artifacts (e.g., issues and commits in software repositories). Among the explorations for automated traceability recovery, the IR (Information Retrieval)-based approaches leverage textual similarity to measure the likelihood of traces between artifacts and show advantages in many scenarios. However, the globalization of software development has introduced new challenges, such as the possible multilingualism on the same concept (e.g., "ShuXing" vs. "attribute") in the artifact texts, thus significantly hampering the performance of IR-based approaches. Existing research has shown that machine translation can help address the term inconsistency in bilingual projects. However, the translation can also bring in synonymous terms that are not consistent with those in the bilingual projects (e.g., another translation of "ShuXing" as "property"). Therefore, we propose an enhancement strategy called AVIATE that exploits translation variants from different translators by utilizing the word pairs that appear simultaneously across the translation variants from different kinds artifacts (a.k.a. consensual biterms). We use these biterms to first enrich the artifact texts, and then to enhance the calculated IR values for improving IR-based traceability recovery for bilingual software projects. The experiments on 17 bilingual projects (involving English and 4 other languages) demonstrate that AVIATE significantly outperformed the IR-based approach with machine translation (the state-of-the-art in this field) with an average increase of 16.67 in Average Precision (31.43%) and 8.38 (11.22%) in Mean Average Precision, indicating its effectiveness in addressing the challenges of multilingual traceability recovery.