ru
ru
Source
FSE
DATE OF PUBLICATION
06/23/2025
Authors
Karina Romanova Sergey Senichev Lina Veltman Ivan Nasonov Andrey Kuznetsov Ilya Makarov
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SODAOpt: Socio-Demographic and Textual Adaptive Fusion for Optimizing Developer Task Assignment

LLMs, Software Engineering, Task Assignment

Abstract

Modern software engineering faces challenges in task assignmentdue to over-reliance on static identifiers, which ignore contextualdata like documentation, commit logs, and developer profiles. Thislimits adaptability in resource allocation and bug triage.We proposeSODAOpt, a transformer-based framework that integrates textualand socio-demographic embeddings to enhance task matching. Ourapproach employs adaptive fusion layers to unify heterogeneousdata modalities and a composite loss function balancing contrastivelearning with assignment optimization. Evaluations on real-worlddatasets demonstrate SODAOpt’s superiority in precision and crossprojectdiversity, validated by tailored metrics (SP@K, CPR, CNDCG@K).

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