# Automating the Analysis of Negative Test Verdicts:
## 2 Materials and Methods
### Applied AI Algorithms  
To address the challenge of automating the analysis of test results for embedded systems, the Python language was selected due to its widespread availability, open-source nature, and rich collection of tools and libraries dedicated to artificial intelligence. This decision is primarily explained by the vast access to validated algorithms whose effectiveness has been confirmed in numerous scientific articles. In the context of exploration and evaluation of algorithms dedicated to anomaly detection, it is decided to use the Python Outlier Detection library (PyOD) [18]. This library stands out for its extensive implementations of various anomaly detection algorithms. Its application is particularly justified in the context of the ongoing project, enabling efficient analysis and identification of anomalies in test data. Based on benchmark results presented by [PERSON] et al. [19], it is decided to choose 7 unsupervised (ECOD, PCA, AvgKNN, IForest, INNE, AutoEncoder, ALAD) and 7 supervised(CatB+, LGB, MLP, SVM, XGB+, ResNet, FTTransformer) algorithms for the evaluation of the proposed AVESYS framework. The ECOD algorithm proposed by [PERSON] [20] is based on estimating the underlying distribution of the input data in a non-parametric way, calculating the empirical cumulative distribution for each dimension of the data. It then uses these distributions to estimate the tail probabilities for each dimension for each data point. Finally, ECOD calculates an outlier for each data point by aggregating the estimated tail probabilities across dimensions. The PCA algorithm is applied because of its efficacy in uncovering the underlying pattern and reduced computational complexity. The research uses the approach described by [PERSON] et al. [21]. The performance of the classic KNN (K-Nearest Neighbors) algorithm used for classification and regression tasks is investigated. The AvgKNN algorithm efficiently computes the weights using a Hilbert space-filling curve. It operates in two phases: the first phase provides an approximate solution with low time complexity, and the second phase refines the solution with exact results in a single scan [22]. Another algorithm designed for anomaly detection is Isolation Forest (IForest). It is developed based on the principle that anomalies are easier to isolate in a dataset. IForest builds a random forest of decision trees to effectively identify outliers. The algorithm works by randomly selecting features and creating paths in the trees to isolate individual data points. Anomalies that are rarer are expected to require fewer splits to isolate. The IForest algorithm assigns anomaly scores based on how quickly instances are isolated in the trees. Anomalies that are isolated faster receive lower scores, making it effective in identifying outliers in multidimensional datasets. This algorithm is particularly useful in scenarios where anomalies are rare and show different patterns to normal instances [23]. [PERSON] et al. [24] presented the INNE algorithm using an alternative isolation mechanism based on Nearest Neighbor Ensemble. Although INNE is based on nearest neighbors, it performs much faster than similar existing methods, especially in datasets with thousands of dimensions or millions of instances. This is because the proposed method has linear time complexity and constant spatial complexity. The autoencoder is chosen for this study because of its effective representation of complex data. The autoencoder consists of an encoder, which compresses the input data into a lower-dimensional representation, and a decoder, which is responsible for reconstructing the original input data. The aim of using an autoencoder is to minimize reconstruction error, encouraging the network to learn a meaningful representation of the data. The architecture of the autoencoder is symmetric, with the number of neurons in the encoder bottleneck layer typically representing the encoding of the input data in a lower dimension [25]. [PERSON] et al.