Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques

• URL: http://arxiv.org/abs/2207.00622v2
• Date: Sat, 1 Jun 2024 21:57:06 GMT
• Title: Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques
• Authors: Zurab Khasidashvili,
• Abstract summary: We describe how it provides high quality debug hints for reducing the debug effort. As a case study, we used these techniques for root-causing failures of the Power Management (PM) design feature Package-C8. We propose an approach for mining the root-causing experience and results for reuse, to accelerate future debug activities and reduce dependency on validation experts.
• Score: 1.6317061277457001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a root-causing procedure for accelerating system-level debug using rule-based techniques. We describe the procedure and how it provides high quality debug hints for reducing the debug effort. This includes the heuristics for engineering features from logs of many tests, and the data analytics techniques for generating powerful debug hints. As a case study, we used these techniques for root-causing failures of the Power Management (PM) design feature Package-C8 and showed their effectiveness. Furthermore, we propose an approach for mining the root-causing experience and results for reuse, to accelerate future debug activities and reduce dependency on validation experts. We believe that these techniques are beneficial also for other validation activities at different levels of abstraction, for complex hardware, software and firmware systems, both pre-silicon and post-silicon.

Related papers

• Technical Lag as Latent Technical Debt: A Rapid Review [0.0]
  Technical lag accumulates when software systems fail to keep pace with technological advancements, leading to a deterioration in software quality.<n>This paper aims to consolidate existing research on technical lag, clarify definitions, explore its detection and quantification methods, examine underlying causes and consequences, review current management practices, and lay out a vision as an indicator of passively accumulated technical debt.
  arXiv  Detail & Related papers  (2026-01-16T16:27:32Z)
• Design and Implementation of Data Acquisition and Analysis System for Programming Debugging Process Based On VS Code Plug-In [1.1521859713894205]
  This paper implements a data acquisition and analysis system for programming debug process based on VS Code plug-in.<n>The system supports a variety of programming languages, integrates debug tasks and data acquisition functions.<n>It uploads the data to the platform database to realize the whole process monitoring and feedback, and improves the teaching effect.
  arXiv  Detail & Related papers  (2025-11-08T03:18:34Z)
• Executable Knowledge Graphs for Replicating AI Research [65.41207324831583]
  Executable Knowledge Graphs (xKG) is a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature.<n>Code will released at https://github.com/zjunlp/xKG.
  arXiv  Detail & Related papers  (2025-10-20T17:53:23Z)
• From Benchmark Data To Applicable Program Repair: An Experience Report [1.6913109767046948]
  This paper describes our approach to automated program repair.<n>We combine various techniques from the literature to achieve this.<n>Experiments show that our approach performs better than other techniques on standard benchmarks.<n>On closer inspection, none of these techniques work on realistic defects that we see in industry.
  arXiv  Detail & Related papers  (2025-08-22T03:59:27Z)
• The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs [0.0]
  We introduce the Decay Index (DDI), a mathematical framework that quantifies when debug becomes ineffective and predicts intervention points.<n>DDI reveals a fundamental limitation in current AI debug and provides the first quantitative framework for optimising iterative code generation strategies.
  arXiv  Detail & Related papers  (2025-06-23T08:40:45Z)
• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement [69.51496713076253]
  In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL.
  arXiv  Detail & Related papers  (2024-02-05T22:15:55Z)
• Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach [49.42371633618761]
  We propose an RL-based strategy to find approximate versions of an application that balance accuracy degradation and power and computation time reduction. Our experimental results show a good trade-off between accuracy degradation and decreased power and computation time for some benchmarks.
  arXiv  Detail & Related papers  (2023-12-29T09:10:40Z)
• Embedding in Recommender Systems: A Survey [67.67966158305603]
  A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Targeted collapse regularized autoencoder for anomaly detection: black hole at the center [3.924781781769534]
  Autoencoders can generalize beyond the normal class and achieve a small reconstruction error on some anomalous samples. We propose a remarkably straightforward alternative: instead of adding neural network components, involved computations, and cumbersome training, we complement the reconstruction loss with a computationally light term. This mitigates the black-box nature of autoencoder-based anomaly detection algorithms and offers an avenue for further investigation of advantages, fail cases, and potential new directions.
  arXiv  Detail & Related papers  (2023-06-22T01:33:47Z)
• Learning to Adapt to Unseen Abnormal Activities under Weak Supervision [43.40900198498228]
  We present a meta-learning framework for weakly supervised anomaly detection in videos. Our framework learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available.
  arXiv  Detail & Related papers  (2022-03-25T12:15:44Z)
• Feature Recommendation for Structural Equation Model Discovery in Process Mining [0.0]
  We propose a method for finding the set of (aggregated) features with a possible effect on the problem. We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs.
  arXiv  Detail & Related papers  (2021-08-13T12:23:01Z)
• Cockpit: A Practical Debugging Tool for Training Deep Neural Networks [27.96164890143314]
  We present a collection of instruments that enable a closer look into the inner workings of a learning machine. These instruments leverage novel higher-order information about the gradient distribution and curvature.
  arXiv  Detail & Related papers  (2021-02-12T16:28:49Z)
• DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks [88.62288327934499]
  We propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2020-11-03T07:49:15Z)
• Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems [6.4332733596587115]
  We introduce a novel deep learning-based approach for fault detection and test it on a real data set, namely, the Kaggle platform for a partial discharge detection task. Our solution adopts a Long-Short Term Memory architecture with attention mechanism to extract time series features, and uses a 1D-Convolutional Neural Network structure to exploit frequency information of the signal for prediction.
  arXiv  Detail & Related papers  (2020-09-15T02:01:47Z)

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.