Citation :

Kodanda Rami Reddy, 2024. "Optimization of Router Testing Procedures Using Advanced Machine Learning Techniques" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 1: 101-112.

Abstract :

The term machine learning refers to the capability of a program or application to learn and optimize its performance as a consequence of its exposure to a multitude of external conditions. The development of ML technologies has reached the level where they can be introduced to address logical operations and tasks that are typically carried out by highly skilled technical personnel in the repetitive and mundane day-to-day product testing phase of the manufacturing process. Various ML applications already exist for PHY layer technologies and products. This paper discusses the use of machine learning technologies in developing automated testing schemes for testing router software. In particular, we discuss an application where a specific machine learning technology is used to analyze and evaluate patterns of call flows resulting from the execution of the software under test. Not only the presence or absence of specific call flow patterns is of interest, but in addition, the system can categorize call flow patterns into behavioral subgroups. Such subgroups reflect the responses of the system to specific stimuli. Both sensible responses and out-of-the-ordinary responses are of interest, as they may hold the key to faults or other out-of-spec behavior that deserves additional attention.

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Keywords :

Optimization of Router Testing, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM), Computer Science, Data Science, Vehicle, Vehicle Reliability.