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Knowledge-Based Verification of Concatenative Programming Patterns Inspired by Natural Language for

Salvatore Gaglio1,2, Giuseppe Lo Re1, Gloria Martorella1

  • 1Department of Engineering, University of Palermo, Viale delle Scienze, Ed.6, 90128 Palermo, Italy.

Sensors (Basel, Switzerland)
|December 30, 2020
PubMed
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This summary is machine-generated.

This study introduces a new method for verifying natural language-inspired applications on resource-constrained devices. It enables runtime verification and testing of software interacting with physical environments.

Area of Science:

  • Computer Science
  • Software Engineering
  • Embedded Systems

Background:

  • Resource-constrained devices require efficient programming paradigms.
  • Interacting with physical environments necessitates robust verification methods.
  • Traditional verification approaches often struggle with distributed, resource-limited systems.

Purpose of the Study:

  • To propose a methodology for verifying applications using natural language-inspired programming patterns.
  • To enable runtime verification on resource-constrained interconnected devices.
  • To reduce reliance on complex, synchronized ontologies for mapping physical concepts to code.

Main Methods:

  • Utilizing natural language programming patterns to map physical concepts directly to executable code.
Keywords:
concatenative languagesdistributed programmingembedded systemsforthinternet of thingssymbolic programmingwireless sensor networks

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  • Employing a rule-based system for automated oracle and test case generation.
  • Performing runtime verification on-board target devices, including syntactic, semantic, and physical effect checks.
  • Leveraging sensor and actuator rules to verify code execution impact on the physical environment.

    • Main Results:

    • Successful runtime verification of software under test (SUT) on target hardware.
    • Automated generation of test cases and oracles for comprehensive testing.
    • Verification of code execution effects on both hardware and the physical environment.
    • Identification of potential software issues through repeated execution testing.

    Conclusions:

    • The proposed methodology effectively verifies applications on resource-constrained devices using natural language-inspired patterns.
    • This approach simplifies development by avoiding complex ontology management.
    • It enhances the reliability of software interacting with physical systems.