一句话：提出AVIATE，利用多翻译器产生的翻译变体与跨制品共识双词项增强IR值，显著提升双语项目可追溯性恢复。
基于信息检索（IR）的可追溯性恢复依赖文本相似度，但双语软件项目中同一概念存在多语言表述（如“属性”与“attribute”），导致术语不一致，降低恢复性能。机器翻译虽可缓解，却会引入与项目实际用词不一致的同义词。AVIATE利用不同翻译器生成的翻译变体，提取在不同类型制品翻译变体中同时出现的词对（consensual biterms），先用其丰富制品文本，再增强IR相似度值。在17个涉及英语及另外4种语言的双语项目上实验，与基于机器翻译的IR方法（当前最优）相比，Average Precision平均提升16.67（31.43%），Mean Average Precision提升8.38（11.22%），表明其能有效应对多语言可追溯性恢复挑战。
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., "属性" 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 "属性" 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.CCS CONCEPTS• Software and its engineering → Software development process management.
@inproceedings{SunRKGMRS024,
title = {AVIATE: Exploiting Translation Variants of Artifacts to Improve IR-based Traceability Recovery in Bilingual Software Projects},
author = {Kexin Sun and Yiding Ren and Hongyu Kuang and Hui Gao and Xiaoxing Ma and Guoping Rong and Dong Shao and He Zhang},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695023}
}