On the emulation of software faults by software fault injection

This paper proposes an approach to software faults diagnosis in complex fault tolerant systems, encompassing the phases of error detection, fault location, and system recovery. Errors are detected in the first phase, exploiting the operating system support. Faults are identified during the location phase, through a machine learning based approach. Then, the best recovery action is triggered once the fault is located. Feedback actions are also used during the location phase to improve detection quality over time. A real world application from the Air Traffic Control field has been used as case study for evaluating the proposed approach. Experimental results, achieved by means of fault injection, show that the diagnosis engine is able to diagnose faults with high accuracy and at a low overhead.

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A Recovery-Oriented Approach for Software Fault Diagnosis in Complex Critical Systems

International Journal of Adaptive Resilient and Autonomic Systems ◽

10.4018/jaras.2011010105 ◽

2011 ◽

Vol 2 (1) ◽

pp. 77-104 ◽

Cited By ~ 2

Author(s):

Gabriella Carrozza ◽

Roberto Natella

Keyword(s):

Error Detection ◽

Traffic Control ◽

Fault Location ◽

Fault Tolerant ◽

Fault Injection ◽

Critical Systems ◽

Software Faults ◽

Real World Application ◽

Complex Fault ◽

Detection Quality

This paper proposes an approach to software faults diagnosis in complex fault tolerant systems, encompassing the phases of error detection, fault location, and system recovery. Errors are detected in the first phase, exploiting the operating system support. Faults are identified during the location phase, through a machine learning based approach. Then, the best recovery action is triggered once the fault is located. Feedback actions are also used during the location phase to improve detection quality over time. A real world application from the Air Traffic Control field has been used as case study for evaluating the proposed approach. Experimental results, achieved by means of fault injection, show that the diagnosis engine is able to diagnose faults with high accuracy and at a low overhead.

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Introduction to Software Fault Injection

Innovative Technologies for Dependable OTS-Based Critical Systems ◽

10.1007/978-88-470-2772-5_1 ◽

2013 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Domenico Cotroneo ◽

Henrique Madeira

Keyword(s):

Fault Injection ◽

Software Fault

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Software Fault Prediction Using Deep Learning Algorithms

International Journal of Open Source Software and Processes ◽

10.4018/ijossp.2019100101 ◽

2019 ◽

Vol 10 (4) ◽

pp. 1-19

Author(s):

Osama Al Qasem ◽

Mohammed Akour

Keyword(s):

Neural Network ◽

Deep Learning ◽

Detection Rate ◽

Development Process ◽

Learning Algorithms ◽

Fault Prediction ◽

Software Faults ◽

Software Fault Prediction ◽

Development Lifecycle ◽

Software Fault

Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.

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