# Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

## TL;DR

This paper presents an AI-powered sensor system that predicts software behavior in industrial settings to improve decision-making.

## Contribution

A novel agent-based architecture using AI to predict software application metrics in industrial scenarios.

## Key findings

- The forecaster component using random forest regressor shows robust predictive capabilities.
- Visual representations and error analysis highlight forecasting accuracy and limitations.

## Abstract

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.