Abstract: In some implementations, a device may receive information associated with a software log corpus. The device may identify alphanumeric blocks in the software log corpus. The device may encode the blocks to generate numeric encoded blocks. The device may generate a set of input sequences and a set of target sequences based on the encoded blocks and a statistical block length associated with the blocks, wherein the set of target sequences are shifted versions of the set of input sequences. The device may generate a training dataset for embedding computation based on combining the set of input sequences and the set of target sequences into a tuple, partitioning the tuple into batches, and shuffling the batches to obtain the training dataset. The device may generate a set of dense embedding tensors using the training dataset and the encoded blocks.

Abstract: In some implementations, a device may receive training data associated with a set of training command logs and a set of training log masks. The device may generate at least one artificial intelligence model for communications system testing. The device may receive a command log, the command log associated with a first log mask. The device may execute the at least one artificial intelligence model to identify a second log mask for a second set of tests. The device may output information associated with the second log mask for the second set of tests.

Abstract: A device may receive software logs identifying raw data and may convert the raw data to a text format, to generate text data. The device may extract pre-log data from the text data and may remove files with less than a threshold quantity of lines from the text data to generate modified text data. The device may extract UE data from the modified text data and may decode RRC messages in the modified text data to generate decoded RRC messages. The device may extract marker data from the modified text data and may remove files associated with timestamps and test cases from the modified text data to generate further modified text data. The device may extract test case data from the further modified text data and may generate final data that includes the pre-log data, the UE data, the decoded RRC messages, the marker data, and the test case data.

Abstract: A device may receive software logs identifying raw data and may convert the raw data to a text format, to generate text data. The device may extract pre-log data from the text data and may remove files with less than a threshold quantity of lines from the text data to generate modified text data. The device may extract UE data from the modified text data and may decode RRC messages in the modified text data to generate decoded RRC messages. The device may extract marker data from the modified text data and may remove files associated with timestamps and test cases from the modified text data to generate further modified text data. The device may extract test case data from the further modified text data and may generate final data that includes the pre-log data, the UE data, the decoded RRC messages, the marker data, and the test case data.

Abstract: In some implementations, a device may obtain a training corpus, from a set of pre-processed log data, associated with an alphanumeric format. The device may encode the training corpus to obtain encoded data using a set of tokens. The device may calculate a sequence length based on a statistical parameter associated with the training corpus. The device may generate a set of input sequences and a set of target sequences based on the encoded data, where each input sequence and each target sequence has a length equal to the sequence length. The device may generate a training data set based on combining the set of input sequences and the set of target sequences. The device may train a deep neural network (DNN) using the training data set and based on one or more hyperparameters to obtain a set of embedding tensors associated with an embedding layer of the DNN.

Abstract: A device may receive software logs identifying raw data and may convert the raw data to a text format, to generate text data. The device may extract pre-log data from the text data and may remove files with less than a threshold quantity of lines from the text data to generate modified text data. The device may extract UE data from the modified text data and may decode RRC messages in the modified text data to generate decoded RRC messages. The device may extract marker data from the modified text data and may remove files associated with timestamps and test cases from the modified text data to generate further modified text data. The device may extract test case data from the further modified text data and may generate final data that includes the pre-log data, the UE data, the decoded RRC messages, the marker data, and the test case data.

Abstract: An anomaly detection system may train a first model associated with detecting anomalies involving a log source based on first historical logs associated with the log source and may train a second model associated with the log source based on second historical logs associated with the log source and target data that is associated with the second historical logs. The anomaly detection system may cause the first model to process the second historical logs to generate training anomaly data of the log source. The anomaly detection system may train a third model associated with the log source based on the training anomaly data and the target data. The anomaly detection system may configure, based on outputs from the second model and the third model, an anomaly detection model to detect the anomalies in input data that is provided to the first model and the second model.