A Unified Framework for Deep Learning Test Adequacy

Summary & Methodology Analysis

Deep learning test adequacy metrics have historically been released as independent research prototypes with disparate workflows, dependencies, and configuration mechanisms. This fragmentation makes these metrics difficult to reproduce, compare, and adopt in practice. The paper addresses this integration challenge by introducing ADEPT, a unified framework that consolidates these disparate testing mechanics into a single cohesive system. The methodology standardizes the execution of representative test adequacy techniques, which include neuron coverage metrics such as NC, TKNC, KMNC, NBC, and SNAC, along with surprise adequacy methods like LSA and DSA. Furthermore, the framework supports input distribution coverage, deep boundary coverage, and source- and model-level mutation scores including SLMS and MLMS. The architectural novelty of ADEPT lies in its template-based metric interface with extension points, YAML-based configuration management, preprocessing-cache reuse for intermediate artifacts, and structured result reporting. By reusing preprocessing caches for intermediate artifacts, the framework avoids redundant computational overhead during metric evaluation. The paper does not specify particular hardware requirements, runtime latencies, memory footprints, or dollar costs associated with running these evaluations. Additionally, the extracted facts do not list specific limitations or quantitative key results for the framework, leaving performance bounds and failure modes unspecified by the paper.