Source-Code-to-Object-Code Traceability Analysis for Avionics Software: Don’t Trust Your Compiler

Brauer, Jörg; Dahlweid, Markus; Pankrath, Tobias; Peleskã, Jan

doi:10.1007/978-3-319-24255-2_31

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Compiler-enforced memory sectioning has a limited quality with regard to elements it can identify. As pointed out by Brauer et al [Brauer et al, 2015], representation of the software behaviour in runtime memory differs to what we have in the source code. More specifically, as we've shown in our previous work [Kajtazović et al, 2020], that compilers may, in the course of the compilation process, create various housekeeping data and managing code which are part of the software runtime environment for our RTOS, applications.…”

Section: Problem Statementmentioning

confidence: 86%

“…Ensuring traceability between the source and object code in context of functional safety is motivated by the fact that both representations of software may differ, since compilers may put additional branches in code, generate parts of the language runtime or link additional libraries. Although this has many other implications, traceability analysis is considered merely as a measure to demonstrate that code coverage criteria for module (unit) testing is met [Brauer et al, 2015].…”

Section: Related Workmentioning

confidence: 99%

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A Compiler and Language Support for Designing Mixed-Criticality Applications

Kajtazovic¹,

Hödl²,

Botler³

2021

jucs

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Coexistence of software components and functions of different criticality in a single computing platform has challenged the safety community for the past two decades. Despite efforts that have been made so far, dealing with mixed-criticality has still left some room for improvements. One particular concern here is that partitioning of hardware and software resources with regard to criticality (safety related, non-safety related) has direct implications on how safety measures need to be realised. For example, a self-test that must meet certain diagnostic coverage for the microcontroller core by inspecting its instructions, needs to cover only those instructions which are able to affect a safety function. Available software mechanisms and tools are to a certain extent still unable to deal with such a fine-grained selection of resources. In this work, we introduce a compiler extension and language support which enable accurate selection of data based on their criticality. The compiler extension serves to establish detailed traceability between the software code and its representation in runtime memory. With the language support, the individual data elements can be classified based on the desired safety integrity level. As a result, safety measures that operate on data (e.g. Abraham test for SRAM can achieve better coverage. The method has been evaluated and applied to industrial safety controllers. We provide here relevant performance figures and discuss possible applications of the method in other fields.

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Section: Problem Statementmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

A Compiler and Language Support for Designing Mixed-Criticality Applications

Kajtazovic¹,

Hödl²,

Botler³

2021

jucs

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show abstract

“…The software test quality criteria are typically expressed at the source-code level, to make it easier for developers to understand what is covered and what is not. There has also been some work on test coverage at the object-code level [13,17], but it is not widely studied. Our resiliency analysis examines error models at the object code level and aims to find assembly instructions that are vulnerable to SDCs (SDC-PCs).…”

Section: Input Qualitymentioning

confidence: 99%

Minotaur

Mahmoud

Venkatagiri

Ahmed

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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With the end of conventional CMOS scaling, efficient resiliency solutions are needed to address the increased likelihood of hardware errors. Silent data corruptions (SDCs) are especially harmful because they can create unacceptable output without the user's knowledge. Several resiliency analysis techniques have been proposed to identify SDC-causing instructions, but they remain too slow for practical use and/or sacrifice accuracy to improve analysis speed. We develop Minotaur, a novel toolkit to improve the speed and accuracy of resiliency analysis. The key insight behind Minotaur is that modern resiliency analysis has many conceptual similarities to software testing; therefore, adapting techniques from the rich software testing literature can lead to principled and significant improvements in resiliency analysis. Minotaur identifies and adapts four concepts from software testing: 1) it introduces the concept of input quality criteria for resiliency analysis and identifies PC coverage as a simple but effective criterion; 2) it creates (fast) minimized inputs from (slow) standard benchmark inputs, using the input quality criteria to assess the goodness of the created input; 3) it adapts the concept of test case prioritization to prioritize error injections and invoke early termination for a given instruction to speed up error-injection campaigns; and 4) it further adapts test case or input prioritization to accelerate SDC discovery across multiple inputs. We evaluate Minotaur by applying it to Approxilyzer, a state-of-the-art resiliency analysis tool. Minotaur's first three techniques speed up Approxilyzer's resiliency analysis by

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Source-Code-to-Object-Code Traceability Analysis for Airborne Software: A Case for Tool Support

Brauer¹

2023

Lecture Notes in Computer Science

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Source-Code-to-Object-Code Traceability Analysis for Avionics Software: Don’t Trust Your Compiler

Cited by 5 publications

References 11 publications

A Compiler and Language Support for Designing Mixed-Criticality Applications

A Compiler and Language Support for Designing Mixed-Criticality Applications

Minotaur

Source-Code-to-Object-Code Traceability Analysis for Airborne Software: A Case for Tool Support

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