# Automating the Analysis of Negative Test Verdicts:
## 1 Introduction
To the increase in testing and maintenance tasks must be added the effort and time required to debug and resolve failed tests [11]. One critical aspect of this process is the analysis of negative test verdicts, which has traditionally required significant human intervention and expertise. The traditional approach to analyzing negative test results required testers to meticulously review test results, identify issues and determine root causes. This process, while effective, was time-consuming, resource-intensive and vulnerable to human error. Moreover, as the complexity of systems and software increased, the volume of test data grew exponentially, making manual analysis increasingly impractical. In this work, we introduce a concept that enables the application of artificial intelligence algorithms to assist embedded system engineers in the process of validating test results. It supports the process of analyzing negative results and identifies cases where the test environment, rather than a software defect, is the cause of test failure. With the advent of artificial intelligence algorithms, the prospect of automating this crucial aspect of testing is now within reach, promising not only to streamline the testing process, but also to improve its overall reliability. The aim of our work is to evaluate the applicability of artificial intelligence algorithms in the process of testing embedded software in a real industrial project. We identify the limitations of the proposed solution and the direction of further work on the implementation of neural networks in the investigated process. Implementing advanced AI methods and algorithms in mechanical engineering diagnostics offers significant potential for improving maintenance, planning, and process optimization. Tools for testing embedded software at the unit test and integration test levels are usually closely integrated with development tools. They enable not only the automatic execution of designed test cases, but often also the automatic generation of test cases based on code or software architecture (UML diagrams). Test execution reports can also be automatically generated [12]. Machine learning has emerged as a powerful technique for autonomously learning correct system behavior that meets specified requirements [13]. With the increasing complexity of embedded systems, not all possible interactions can be manually defined as test cases. It is crucial to note that additional input data, when introduced automatically, must align with component behavior patterns to ensure the test corresponds to the system's actual behavior. [PERSON] proposes employing deep learning to obtain new, realistic test data and identify new embedded system behavior patterns [14]. Augmented intelligence applied in embedded system testing serves as a robust tool acting as a competent and comprehensive expert system. Considering input data regarding the embedded system and its hardware components, the most suitable set of tests can be suggested to ensure full compliance with product requirements and alert testers to inconsistencies or anomalies in the testing system. Regarding the analysis of negative test results, it could be mentioned that [PERSON] [15] introduces a system called a Measurement Intrusion Detection System (MIDS) for detecting anomalies in industrial control systems. It utilizes supervised machine learning to classify normal and abnormal activity in the system. Choosing the right anomaly detection algorithm is essential for real-time applications. Feature selection is critical to reducing computation time and improving model accuracy. [PERSON] [15] compares a few learning algorithms (KNN, DTC, Random Forest) in the anomaly detection task. Evaluation criteria include classification accuracy and processing time for training and prediction. The Random Forest algorithm demonstrates the best anomaly detection performance and consumes the least time in model generation, while the Decision Tree algorithm has the lowest prediction computation time.