一句话：提出融合文本与社会人口统计嵌入的 transformer 框架 SODAOpt，用自适应融合与复合损失优化开发者任务分配与缺陷分诊。
现代软件工程中的任务分配过度依赖静态标识符，忽略了文档、提交日志与开发者画像等上下文数据，限制了资源分配与缺陷分诊的适应性。本文提出 SODAOpt，一个基于 transformer 的框架，将文本嵌入与社会人口统计嵌入相结合以增强任务匹配。方法上，SODAOpt 采用自适应融合层统一异构数据模态，并设计兼顾对比学习与分配优化的复合损失函数进行训练。评估在真实世界数据集上进行，并使用 SP@K、CPR、C-NDCG@K 等定制指标加以验证；结果表明 SODAOpt 在精度与跨项目多样性方面均优于现有方法。
Modern software engineering faces challenges in task assignment due to over-reliance on static identifiers, which ignore contextual data like documentation, commit logs, and developer profiles. This limits adaptability in resource allocation and bug triage. We propose SODAOpt, a transformer-based framework that integrates textual and socio-demographic embeddings to enhance task matching. Our approach employs adaptive fusion layers to unify heterogeneous data modalities and a composite loss function balancing contrastive learning with assignment optimization. Evaluations on real-world datasets demonstrate SODAOpt's superiority in precision and cross-project diversity, validated by tailored metrics (SP@K, CPR, C-NDCG@K).
@inproceedings{paperbot9151,
title = {SODAOpt: Socio-Demographic and Textual Adaptive Fusion for Optimizing Developer Task Assignment},
author = {Karina Romanova and Sergey Senichev and Lina Veltman and Ivan Nasonov and Andrey Kuznetsov and Ilya Makarov},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3731437}
}