On the Use of Boundary Scan for Code Coverage of Critical Embedded Software

Quantitative Metrics for Performance Monitoring of Software Code Analysis Accredited Testing Laboratories

Sensors ◽

10.3390/s21113660 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3660

Author(s):

Wladmir Araujo Chapetta ◽

Jailton Santos das Neves ◽

Raphael Carlos Santos Machado

Keyword(s):

Proficiency Testing ◽

Performance Monitoring ◽

Embedded Software ◽

Interlaboratory Comparisons ◽

Code Analysis ◽

Code Coverage ◽

Software Analysis ◽

Quantitative Metrics ◽

Software Code ◽

Software Assessment

Modern sensors deployed in most Industry 4.0 applications are intelligent, meaning that they present sophisticated behavior, usually due to embedded software, and network connectivity capabilities. For that reason, the task of calibrating an intelligent sensor currently involves more than measuring physical quantities. As the behavior of modern sensors depends on embedded software, comprehensive assessments of such sensors necessarily demands the analysis of their embedded software. On the other hand, interlaboratory comparisons are comparative analyses of a body of labs involved in such assessments. While interlaboratory comparison is a well-established practice in fields related to physical, chemical and biological sciences, it is a recent challenge for software assessment. Establishing quantitative metrics to compare the performance of software analysis and testing accredited labs is no trivial task. Software is intangible and its requirements accommodate some ambiguity, inconsistency or information loss. Besides, software testing and analysis are highly human-dependent activities. In the present work, we investigate whether performing interlaboratory comparisons for software assessment by using quantitative performance measurement is feasible. The proposal was to evaluate the competence in software code analysis activities of each lab by using two quantitative metrics (code coverage and mutation score). Our results demonstrate the feasibility of establishing quantitative comparisons among software analysis and testing accredited laboratories. One of these rounds was registered as formal proficiency testing in the database—the first registered proficiency testing focused on code analysis.

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Research on Embedded Software Testing Based on Code Coverage and Variable Watching

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.1694 ◽

2012 ◽

Vol 263-266 ◽

pp. 1694-1699

Author(s):

Song Yang Du ◽

Hua Yang ◽

Ying Zhang ◽

Zhong Wei Chen

Keyword(s):

Embedded System ◽

Software Testing ◽

Real Time ◽

Embedded Software ◽

Code Coverage ◽

Testing Tools ◽

Normal Method ◽

The Embedded System ◽

Strict Requirement

The limitation of traditional testing tools makes it difficult to test the software of embedded system--the resource insufficiency of the embedded system as well as the strict requirement of real-time all contribute to the unavailability of the normal method for software testing. This document introduced a method about testing of embedded software, which based on code coverage and variable watching by using the software of Testbed and RTInsight. By using this method, it’s effective to validate the software testing of embedded system.

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CODE COVERAGE-BASED POWER ESTIMATION TECHNIQUES FOR MICROPROCESSORS

Journal of Circuits System and Computers ◽

10.1142/s0218126602000616 ◽

2002 ◽

Vol 11 (05) ◽

pp. 557-574 ◽

Cited By ~ 4

Author(s):

GANG QU ◽

NAOYUKI KAWABE ◽

KIMIYOSHI USAMI ◽

MIODRAG POTKONJAK

Keyword(s):

Power Dissipation ◽

High Efficiency ◽

Average Power ◽

Power Estimation ◽

Data Bank ◽

Embedded Software ◽

Average Error ◽

Machine Code ◽

Total Energy Consumption ◽

Code Coverage

We have developed a function-level power estimation methodology for predicting the power dissipation of embedded software. For a given microprocessor core, we empirically build the "power data bank", which stores the power information of the built-in library functions and basic instructions. To estimate the average power of an embedded software on this core, we first decompose the machine code into library functions and user-defined functions. We then use program profiling/tracing tools to get the execution information of the target software. Next, we evaluate the total energy consumption and execution time based on the "power data bank", and their ratio is taken as the average power. High efficiency is achieved because no power simulator is used once the "power data bank" is built. We apply this method to a commercial microprocessor core and get power estimates with an average error of 3%. Using this method, microprocessor vendors can provide users the "power data bank" without releasing details of the core to help users get early power estimates and eventually guide power optimization.

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