Every Maintenance Has Its Exemplar: The Future of Software Maintenance through Migration

2026-02-15

AI Summary

Software maintenance remains heavily reliant on manual effort, resulting in high costs, low efficiency, and susceptibility to errors. This work proposes the first systematic research framework for transfer-based software maintenance, drawing inspiration from transfer learning. The framework establishes a comprehensive lifecycle model encompassing task identification, source system selection, cross-system data matching and adaptation, and validation of transferred outcomes. It explicitly delineates the core objectives and key challenges at each stage, integrating techniques from software engineering such as knowledge transfer, cross-project data alignment, and context-aware adaptation. By doing so, the framework introduces a novel paradigm for automating software maintenance and lays a solid theoretical foundation for the future development of supporting tools and methodologies.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Socio-technical web-based systems

Abstract

Maintenance is a critical stage in the software lifecycle, ensuring that post-release systems remain reliable, efficient, and adaptable. However, manual software maintenance is labor-intensive, time-consuming, and error-prone, which highlights the urgent need for automation. Learning from maintenance activities conducted on other software systems offers an effective way to improve efficiency. In particular, recent research has demonstrated that migration-based approaches transfer knowledge, artifacts, or solutions from one system to another and show strong potential in tasks such as API evolution adaptation, software testing, and migrating patches for fault correction. This makes migration-based maintenance a valuable research direction for advancing automated maintenance. This paper takes a step further by presenting the first systematic research agenda on migration-based approaches to software maintenance. We characterize the migration-based maintenance lifecycle through four key stages: \ding{182} identifying a maintenance task that can be addressed through migration, \ding{183} selecting suitable migration sources for the target project,\ding{184} matching relevant data across systems and adapting the migrated data to the target context, and \ding{185} validating the correctness of the migration. We also analyze the challenges that may arise at each stage. Our goal is to encourage the community to explore migration-based approaches more thoroughly and to tackle the key challenges that must be solved to advance automated software maintenance.

Problem

• software maintenance
• migration-based maintenance
• automated maintenance
• knowledge transfer
• software evolution

Innovation

• migration-based maintenance
• software maintenance automation
• knowledge transfer
• cross-system adaptation
• maintenance lifecycle