A Novel Automation Framework for Embedded System Validation Using Domain-Specific Language

In the realm of embedded system testing, automating the validation process remains a critical challenge due to the complexity of mixed-signal environments. This paper introduces a domain-specific language (DSL) designed to streamline hardware-in-the-loop testing by enabling rapid signal validation without extensive coding expertise. By leveraging optimized dynamic time warping (DTW) algorithms, the proposed framework accelerates the detection of signal anomalies and enhances validation accuracy. The experimental results demonstrate a 68% reduction in test development time and 4.12times faster discrepancy identification with 93.5% maintained classification accuracy. This approach paves the way for more efficient, scalable, and reliable automation in embedded system testing.

Authors

Publication Details

Journal
International Journal of Intelligent Systems and Data Science
Published
2026-10-31
DOI
https://doi.org/10.67231/msp2my77
Primary Topic
Software Testing and Debugging Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Novel Automation Framework for Embedded System Validation Using Domain-Specific Language

Elvis Mondal
International Journal of Intelligent Systems and Data Science
Software Testing and Debugging Techniques
article

A Novel Automation Framework for Embedded System Validation Using Domain-Specific Language

Elvis Mondal
article en

Abstract

In the realm of embedded system testing, automating the validation process remains a critical challenge due to the complexity of mixed-signal environments. This paper introduces a domain-specific language (DSL) designed to streamline hardware-in-the-loop testing by enabling rapid signal validation without extensive coding expertise. By leveraging optimized dynamic time warping (DTW) algorithms, the proposed framework accelerates the detection of signal anomalies and enhances validation accuracy. The experimental results demonstrate a 68% reduction in test development time and 4.12times faster discrepancy identification with 93.5% maintained classification accuracy. This approach paves the way for more efficient, scalable, and reliable automation in embedded system testing.

International Journal of Intelligent Systems and Data ScienceVol. 1(6)
Openalex Percentile: Top 3%
Software Testing and Debugging Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon