4,601 research outputs found
Sudden drop of fractal dimension of electromagnetic emissions recorded prior to significant earthquake
The variation of fractal dimension and entropy during a damage evolution
process, especially approaching critical failure, has been recently
investigated. A sudden drop of fractal dimension has been proposed as a
quantitative indicator of damage localization or a likely precursor of an
impending catastrophic failure. In this contribution, electromagnetic emissions
recorded prior to significant earthquake are analysed to investigate whether
they also present such sudden fractal dimension and entropy drops as the main
catastrophic event is approaching. The pre-earthquake electromagnetic time
series analysis results reveal a good agreement to the theoretically expected
ones indicating that the critical fracture is approaching
Test Case Purification for Improving Fault Localization
Finding and fixing bugs are time-consuming activities in software
development. Spectrum-based fault localization aims to identify the faulty
position in source code based on the execution trace of test cases. Failing
test cases and their assertions form test oracles for the failing behavior of
the system under analysis. In this paper, we propose a novel concept of
spectrum driven test case purification for improving fault localization. The
goal of test case purification is to separate existing test cases into small
fractions (called purified test cases) and to enhance the test oracles to
further localize faults. Combining with an original fault localization
technique (e.g., Tarantula), test case purification results in better ranking
the program statements. Our experiments on 1800 faults in six open-source Java
programs show that test case purification can effectively improve existing
fault localization techniques
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Assessing the Effectiveness of Defect Prediction-based Test Suites at Localizing Faults
Debugging a software program constitutes a significant and laborious task for programmers, often consuming a substantial amount of time. The need to identify faulty lines of code further compounds this challenge, leading to decreased overall productivity. Consequently, the development of automated tools for fault detection becomes imperative to streamline the debugging process and enhance programmer productivity.
In recent years, the field of automatic test generation has witnessed remarkable advancements, significantly improving the efficacy of automatic tests in detecting faults. The localization of faults can be further optimized through the utilization of such sophisticated tools.
This dissertation aims to conduct an experimental study that assembles specialized automatic test generation tools designed to detect faults by estimating the likelihood of code being faulty. These tools will be compared against each other to discern their relative performance and effectiveness. Additionally, the study will comprehensively compare developer-generated tests with automatically generated tests to evaluate their respective aptitude for fault detection. Through this investigation, we seek to identify the most effective automated test generation tool while providing valuable insights into the relative merits of developer-generated and automatically generated tests for fault detection
A kernel density estimate-based approach to component goodness modeling
Intermittent fault localization approaches account for the fact that faulty components may fail intermittently by considering a parameter (known as goodness) that quantifies the probability that faulty components may still exhibit correct behavior. Current, state-of-the-art approaches (1) assume that this goodness probability is context independent and (2) do not provide means for integrating past diagnosis experience in the diagnostic mechanism. In this paper, we present a novel approach, coined Non-linear Feedback-based Goodness Estimate (NFGE), that uses kernel density estimations (KDE) to address such limitations. We evaluated the approach with both synthetic and real data, yielding lower estimation errors, thus increasing the diagnosis performance
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI
We demonstrate a unified approach to rigorous design of safety-critical
autonomous systems using the VerifAI toolkit for formal analysis of AI-based
systems. VerifAI provides an integrated toolchain for tasks spanning the design
process, including modeling, falsification, debugging, and ML component
retraining. We evaluate all of these applications in an industrial case study
on an experimental autonomous aircraft taxiing system developed by Boeing,
which uses a neural network to track the centerline of a runway. We define
runway scenarios using the Scenic probabilistic programming language, and use
them to drive tests in the X-Plane flight simulator. We first perform
falsification, automatically finding environment conditions causing the system
to violate its specification by deviating significantly from the centerline (or
even leaving the runway entirely). Next, we use counterexample analysis to
identify distinct failure cases, and confirm their root causes with specialized
testing. Finally, we use the results of falsification and debugging to retrain
the network, eliminating several failure cases and improving the overall
performance of the closed-loop system.Comment: Full version of a CAV 2020 pape
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