FastFlip: Compositional Error Injection Analysis
- URL: http://arxiv.org/abs/2403.13989v2
- Date: Tue, 26 Mar 2024 17:56:57 GMT
- Title: FastFlip: Compositional Error Injection Analysis
- Authors: Keyur Joshi, Rahul Singh, Tommaso Bassetto, Sarita Adve, Darko Marinov, Sasa Misailovic,
- Abstract summary: We present FastFlip, a combination of empirical error injection and symbolic SDC propagation analyses.
FastFlip speeds up the analysis of incrementally modified programs by $3.2times$ (geomean)
- Score: 6.285347477114202
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Instruction-level error injection analyses aim to find instructions where errors often lead to unacceptable outcomes like Silent Data Corruptions (SDCs). These analyses require significant time, which is especially problematic if developers wish to regularly analyze software that evolves over time. We present FastFlip, a combination of empirical error injection and symbolic SDC propagation analyses that enables fast, compositional error injection analysis of evolving programs. FastFlip calculates how SDCs propagate across program sections and correctly accounts for unexpected side effects that can occur due to errors. Using FastFlip, we analyze five benchmarks, plus two modified versions of each benchmark. FastFlip speeds up the analysis of incrementally modified programs by $3.2\times$ (geomean). FastFlip selects a set of instructions to protect against SDCs that minimizes the runtime cost of protection while protecting against a developer-specified target fraction of all SDC-causing errors.
Related papers
- The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs [17.497629884237647]
BugLens is a post-refinement framework that significantly improves static analysis precision.
It raises precision from 0.10 (raw) and 0.50 (semi-automated refinement) to 0.72, substantially reducing false positives.
Our results suggest that a structured LLM-based workflow can meaningfully enhance the effectiveness of static analysis tools.
arXiv Detail & Related papers (2025-04-16T02:17:06Z) - SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model [52.47816604709358]
Video anomaly detection (VAD) aims to identify unexpected events in videos and has wide applications in safety-critical domains.
vision-language models (VLMs) have demonstrated strong multimodal reasoning capabilities, offering new opportunities for anomaly detection.
We propose SlowFastVAD, a hybrid framework that integrates a fast anomaly detector with a slow anomaly detector.
arXiv Detail & Related papers (2025-04-14T15:30:03Z) - ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.
This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
arXiv Detail & Related papers (2025-02-17T12:38:57Z) - Rethinking Early Stopping: Refine, Then Calibrate [49.966899634962374]
We show that calibration error and refinement error are not minimized simultaneously during training.
We introduce a new metric for early stopping and hyper parameter tuning that makes it possible to minimize refinement error during training.
Our method integrates seamlessly with any architecture and consistently improves performance across diverse classification tasks.
arXiv Detail & Related papers (2025-01-31T15:03:54Z) - Leveraging Slither and Interval Analysis to build a Static Analysis Tool [0.0]
This paper presents our progress toward finding defects that are sometimes not detected or completely detected by state-of-the-art analysis tools.
We developed a working solution built on top of Slither that uses interval analysis to evaluate the contract state during the execution of each instruction.
arXiv Detail & Related papers (2024-10-31T09:28:09Z) - REDO: Execution-Free Runtime Error Detection for COding Agents [3.9903610503301072]
Execution-free Error Detection for COding Agents (REDO) is a method that integrates runtime errors with static analysis tools.
We demonstrate that REDO outperforms current state-of-the-art methods by achieving a 11.0% higher accuracy and a 9.1% higher weighted F1 score.
arXiv Detail & Related papers (2024-10-10T18:06:29Z) - A Coin Has Two Sides: A Novel Detector-Corrector Framework for Chinese Spelling Correction [79.52464132360618]
Chinese Spelling Correction (CSC) stands as a foundational Natural Language Processing (NLP) task.
We introduce a novel approach based on error detector-corrector framework.
Our detector is designed to yield two error detection results, each characterized by high precision and recall.
arXiv Detail & Related papers (2024-09-06T09:26:45Z) - VDebugger: Harnessing Execution Feedback for Debugging Visual Programs [103.61860743476933]
We introduce V Debugger, a critic-refiner framework trained to localize and debug visual programs by tracking execution step by step.
V Debugger identifies and corrects program errors leveraging detailed execution feedback, improving interpretability and accuracy.
Evaluations on six datasets demonstrate V Debugger's effectiveness, showing performance improvements of up to 3.2% in downstream task accuracy.
arXiv Detail & Related papers (2024-06-19T11:09:16Z) - Geometry-Aware Instrumental Variable Regression [56.16884466478886]
We propose a transport-based IV estimator that takes into account the geometry of the data manifold through data-derivative information.
We provide a simple plug-and-play implementation of our method that performs on par with related estimators in standard settings.
arXiv Detail & Related papers (2024-05-19T17:49:33Z) - Supporting Error Chains in Static Analysis for Precise Evaluation
Results and Enhanced Usability [2.8557828838739527]
Static analyses tend to report where a vulnerability manifests rather than the fix location.
This can cause presumed false positives or imprecise results.
We designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location.
arXiv Detail & Related papers (2024-03-12T16:46:29Z) - Attacking Large Language Models with Projected Gradient Descent [49.19426387912186]
Projected Gradient Descent (PGD) for adversarial prompts is up to one order of magnitude faster than state-of-the-art discrete optimization.
Our PGD for LLMs is up to one order of magnitude faster than state-of-the-art discrete optimization to achieve the same devastating attack results.
arXiv Detail & Related papers (2024-02-14T13:13:26Z) - BEC: Bit-Level Static Analysis for Reliability against Soft Errors [0.26107298043931204]
We propose a bit-level error coalescing (BEC) static program analysis to understand and improve program reliability against soft errors.
BEC analysis tracks each bit corruption in the register file and classifies the effect of the corruption by its semantics at compile time.
The proposed method is generic and not limited to a specific computer architecture.
arXiv Detail & Related papers (2024-01-11T09:03:47Z) - Bridging Precision and Confidence: A Train-Time Loss for Calibrating
Object Detection [58.789823426981044]
We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions.
Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
arXiv Detail & Related papers (2023-03-25T08:56:21Z) - DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified
Robustness [58.23214712926585]
We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection.
Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables.
We are the first to offer certified robustness in the realm of static detection of malware executables.
arXiv Detail & Related papers (2023-03-20T17:25:22Z) - Fast and Accurate Error Simulation for CNNs against Soft Errors [64.54260986994163]
We present a framework for the reliability analysis of Conal Neural Networks (CNNs) via an error simulation engine.
These error models are defined based on the corruption patterns of the output of the CNN operators induced by faults.
We show that our methodology achieves about 99% accuracy of the fault effects w.r.t. SASSIFI, and a speedup ranging from 44x up to 63x w.r.t.FI, that only implements a limited set of error models.
arXiv Detail & Related papers (2022-06-04T19:45:02Z)
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.