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Related Concept Videos

Quality Assurance01:19

Quality Assurance

434
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
434
Quality Control01:05

Quality Control

619
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
619
Quantitative Analysis01:12

Quantitative Analysis

835
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
835
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

11.1K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
11.1K
Development of Analytical Methods01:21

Development of Analytical Methods

1.0K
An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
1.0K
Data Validation01:15

Data Validation

351
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
351

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When Measurements Meet Blockchain: On Behalf of an Inter-NMI Network.

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

Wladmir Araujo Chapetta1, Jailton Santos das Neves2, Raphael Carlos Santos Machado1,2

  • 1Division of Metrology in Information Technologies and Telecommunications, Brazilian National Institute of Metrology, Quality, and Technology (Inmetro), Av. Nossa Sra. Das Graças 50, Duque de Caxias, Rio de Janeiro 25.250-020, Brazil.

Sensors (Basel, Switzerland)
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Interlaboratory comparisons for software assessment are feasible using quantitative metrics like code coverage and mutation score. This study demonstrates the viability of comparing software analysis labs, establishing a new benchmark for proficiency testing.

Keywords:
accredited laboratoriesinterlaboratory comparisonsproficiency testingsoftware product evaluation

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Area of Science:

  • Metrology
  • Software Engineering
  • Information Technology

Background:

  • Modern Industry 4.0 sensors possess intelligent, software-driven behaviors requiring comprehensive software analysis.
  • Traditional interlaboratory comparisons, common in physical sciences, face challenges when applied to intangible software assessments.
  • Software's intangible nature and human-dependent analysis processes complicate the establishment of objective performance metrics for accredited labs.

Purpose of the Study:

  • To investigate the feasibility of using quantitative performance measurements for interlaboratory comparisons in software assessment.
  • To evaluate the competence of software code analysis laboratories through objective metrics.
  • To establish a foundation for formal proficiency testing in software analysis.

Main Methods:

  • Utilized two quantitative metrics: code coverage and mutation score, to assess software code analysis competence.
  • Conducted interlaboratory comparisons among accredited software analysis and testing laboratories.
  • Registered a proficiency testing round focused on code analysis, marking a first in the field.

Main Results:

  • Demonstrated the feasibility of establishing quantitative comparisons among software analysis and testing accredited laboratories.
  • Successfully applied code coverage and mutation score as effective metrics for evaluating lab performance.
  • Pioneered the first formal proficiency testing registration for code analysis.

Conclusions:

  • Quantitative performance measurements are feasible and effective for interlaboratory comparisons in software assessment.
  • The study validates the use of code coverage and mutation score for assessing software analysis competence.
  • This work establishes a precedent for formal proficiency testing in software code analysis, enhancing quality assurance in Industry 4.0 applications.