Detecting Semantic Bugs in Autopilot Software by Classifying Anomalous Variables

Huang, Hu; Guyer, Samuel Z.; Rife, Jason

doi:10.2514/1.i010767

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Bug monitors we have implemented to date in to date by Huang et al 20,21 can be described in general terms by the block diagram of Figure 2. The primary automation software and the bug-monitoring algorithm are two separate programs, each coded separately and linked at compile time.…”

Section: General Descriptionmentioning

confidence: 99%

Applying sensor integrity concepts to detect intermittent bugs in aviation software

2019

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This paper assesses potential benefits of online integrity monitoring for aviation software. Today, aviation software safety is assessed entirely via verification and validation conducted before deployment, largely through exhaustive testing. Although software anomalies (or bugs) do occur, system fault trees do not assign fault probabilities to software components. Moreover, online signal monitoring is not generally applied to detect bugs. As software complexity increases, and as interactions between software and hardware system components become more complex, it is prudent to consider whether exhaustive preservice software testing is sufficient to maintain system safety, and whether safety analyses used for hardware components (eg, fault trees and monitoring) might be applied to software components. The contribution of this paper is to propose an architecture for online bug monitoring and quantify its potential.The proposed concept has significant potential impact for low-cost, autonomous unmanned aircraft systems, where cost drivers prohibit exhaustive preservice verification.

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Section: General Descriptionmentioning

confidence: 99%

Applying sensor integrity concepts to detect intermittent bugs in aviation software

2019

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“…To better understand the characteristics of faults, researchers in recent years have tried to explore the characteristics of faults from real systems [22,[24][25][26][27]. Generally, software faults can classified by the following three dimensions: root cause, impact, and software component.…”

Section: Introductionmentioning

confidence: 99%

Using Algebra Graph Representation to Detect Pairwise-Constraint Software Faults

et al. 2020

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Automatic fault detection, localization, and repairing have always been a research hot spot. Because software faults can appear anywhere in a software, it is impossible to automatically repair all types of faults. One possible solution is to detect, localize, and fix a specific fault each time according to the fault signature pattern. Along this direction, we propose an approach that detects pairwise-constraint software faults using algebra graph representation. The approach takes a program and a pairwise-constraint as inputs and generates a fault report for programmers. It firstly generates a constraint control flow graph (CFG) that is labeled using pairwise constraint and then translates the constraint CFG as a path expression using algebra graph representation, which is an abstract path model for the program. Finally, it employs a detection algorithm to detect whether the program contains pairwise-constraint faults. We perform case studies to validate the effectiveness of our approach. The preliminary results show that the approach can detect pairwise-constraint software faults before software testing.

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Vulnerabilities Space and the Superiority of Hackers

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et al. 2021

2021 International Conference Automatics and Informatics (ICAI)

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Detecting Semantic Bugs in Autopilot Software by Classifying Anomalous Variables

Cited by 6 publications

References 9 publications

Applying sensor integrity concepts to detect intermittent bugs in aviation software

Applying sensor integrity concepts to detect intermittent bugs in aviation software

Using Algebra Graph Representation to Detect Pairwise-Constraint Software Faults

Vulnerabilities Space and the Superiority of Hackers

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