ADEPT: A Unified Framework for Deep Learning Test Adequacy

• URL: http://arxiv.org/abs/2608.12144v1
• Date: Wed, 12 Aug 2026 15:03:28 GMT
• Title: ADEPT: A Unified Framework for Deep Learning Test Adequacy
• Abstract summary: In this paper, we present the engineering details of ADEPT, a framework that integrates representative adequacy techniques.<n>ADEPT provides a template-based metric interface with well-defined extension points for integrating new adequacy metrics.<n>ADEPT is designed for researchers and practitioners who wish to reproduce and apply adequacy metrics without spending days or weeks implementing missing tooling.
• Score: 0.4899818550820574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.g., neuron activation behavior, latent feature coverage, decision-boundary exploration, etc. However, these metrics are typically released as independent research prototypes with substantially different installation and preprocessing requirements, execution workflows, and configuration mechanisms. These complications make them quite difficult to reproduce, compare, and adopt in research work and practical deployment alike. In this paper, we present the engineering details of ADEPT, a framework that integrates representative adequacy techniques, including neuron-coverage-based metrics, surprise adequacy, input distribution coverage, boundary coverage, and source- and model-level mutation score, under a consistent execution workflow. ADEPT provides a template-based metric interface with well-defined extension points for integrating new adequacy metrics. Furthermore, it provides YAML-based configuration management, preprocessing-cache reuse, and structured result reporting, making it easy to use in any research and development workflows. ADEPT is designed for researchers and practitioners who wish to reproduce and apply adequacy metrics without spending days or weeks implementing missing tooling or configuring disparate research prototypes. A demo video is available at https://aub.ie/ADEPT_video.

Related papers

• CIPHER: A Decoupled Exploration-Selection Framework for Test-Time Scaling of Data Science Agents [1.4057557845067372]
  CIPHER is an automated data science agent that leverages test-time scaling through the generation and selection of multiple initial states for concurrent execution.<n>We demonstrate that CIPHER exceeds state-of-the-art performance in matched-model comparisons.<n>Our empirical study characterizes the design space of the Decoupled Exploration-Selection framework.
  arXiv  Detail & Related papers  (2026-07-15T22:01:21Z)
• TraceLLM: Leveraging Large Language Models with Prompt Engineering for Enhanced Requirements Traceability [4.517933493143603]
  This paper introduces TraceLLM, a framework for enhancing requirements traceability through prompt engineering and demonstration selection.<n>We assess prompt generalization and robustness using eight state-of-the-art LLMs on four benchmark datasets.
  arXiv  Detail & Related papers  (2026-02-01T14:29:13Z)
• EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence [17.644658293987955]
  Embodied AI agents are capable of robust spatial perception, effective task planning, and adaptive execution in physical environments.<n>Current large language models (LLMs) and multimodal LLMs (MLLMs) for embodied tasks suffer from key limitations.<n>We propose EmbodiedBrain, a novel vision-language foundation model available in both 7B and 32B parameter sizes.
  arXiv  Detail & Related papers  (2025-10-23T14:05:55Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
  We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
  arXiv  Detail & Related papers  (2025-08-12T09:45:19Z)
• Comprehensive Evaluation of Prototype Neural Networks [0.6967006044904097]
  Prototype models are an important method for explainable artificial intelligence (XAI) and interpretable machine learning.<n>In this paper, we perform an in-depth analysis of a set of prominent prototype models including ProtoPNet, ProtoPool and PIPNet.<n>We propose several new metrics to further complement the analysis of model interpretability.
  arXiv  Detail & Related papers  (2025-07-09T13:08:21Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy [8.927002750209295]
  Large Language Models (LLMs) and Multi-Agent LLMs (MALLMs) introduce non-determinism unlike traditional or machine learning software.<n>This paper presents a taxonomy for LLM test case design, informed by research literature and our experience.
  arXiv  Detail & Related papers  (2025-03-01T13:15:56Z)
• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks.<n>We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.