A First Look at the Lifecycle of DL-Specific Self-Admitted Technical Debt

The rapid adoption of Deep Learning (DL)-enabled systems has revolutionized software development, driving innovation across various domains. However, these systems also introduce unique challenges, particularly in maintaining software quality and performance. Among these challenges, Self-Admitted Technical Debt (SATD) has emerged as a growing concern, significantly impacting the maintainability and overall quality of ML and DL-enabled systems. Despite its critical implications, the lifecycle of DL-specific SATD, how developers introduce, acknowledge, and address it over time-remains underexplored. This study presents a preliminary analysis of the persistence and lifecycle of DL-specific SATD in DL-enabled systems. The purpose of this project is to uncover the patterns of SATD introduction, recognition, and durability during the development life cycle, providing information on how to manage these issues. Using mining software repository techniques, we examined 40 ML projects, focusing on 185 DL-specific SATD instances. The analysis tracked the introduction and persistence of SATD instances through project commit histories to assess their lifecycle and developer actions. The findings indicate that DL-specific SATD is predominantly introduced during the early and middle stages of project development. Training and Hardware phases showed the longest SATD durations, highlighting critical areas where debt accumulates and persists. Additionally, developers introduce DL-specific SATD more frequently during feature implementation and bug fixes. This study emphasizes the need for targeted DL-specific SATD management strategies in DL-enabled systems to mitigate its impact. By understanding the temporal characteristics and evolution of DL-specific SATD, developers can prioritize interventions at critical stages to improve the maintainability and quality of the system.