MSTDP: a multi-scale temporal deep learning framework for just-in-time software defect prediction with cross-attention fusion

Just-In-Time Defect Prediction (JITDP), as an important means to improve software quality and reduce maintenance costs, has received widespread attention in recent years. However, existing methods generally neglect multi-scale temporal features during the development process, lack dynamic modeling of developer behavior and project lifecycle, and have limited robustness when facing concept drift. To address these limitations, this paper proposes a novel Multi-Scale Temporal Defect Prediction framework (MSTDP) that integrates commit behavior patterns at temporal granularities of hours, days, and weeks, combining code semantic information, developer behavioral features, and lifecycle-aware mechanisms to…

• FP
  Fundación para la Investigación e Innovación Biomédica del Hospital Universitario Infanta Sofía y Hospital Universitario del Henares

  Award: 2025XKT1437
• SA
  Steel and Iron Foundation of Hebei Province

  Award: H2023209049
• IN
  Institut Nordique De Recherche En Environnement Et En Santé Au Travail

  Award: 20241501076
• YI
  Youth Innovation Technology Project of Higher School in Shandong Province

  Award: 2024KJJ044