Abstract: Systems and methods for providing an archive of precedents are provided. The archive of precedents includes determining precedent data; parsing a new-event XML file for string data, wherein the string data is related to a new event; generating a vector embedding from the string data; determining a similar embedding in a vector space, wherein the similar embedding is a vector embedding of an instance of precedent data; parsing XML files of the precedent and new event and using parsed data to generate an old-to-new value map; retrieving the corresponding XML file and the corresponding precedent-event document associated with the vector embedding of the instance of precedent data; and providing, as input to a natural language processing model, the list of text strings recovered from the editable form of the document of the precedent event and the old-to-new value map.

Abstract: Systems and methods for anomaly detection in software-defined networks from observed host metrics are disclosed. A method may include: (1) training a random forest model comprising a plurality of trees with historical metrics from a software defined network, the software defined network comprising a plurality of hosts; (2) receiving metrics for a plurality of features from the hosts in the software defined network; (3) providing the metrics to the trained random forest model; (4) receiving, from the trained random forest model, a prediction of an anomalous hosts for one of the hosts; (5) identifying a subset of the plurality of trees that contributed to the prediction; (6) generating feature scores for the feature from the subset of trees; (7) generating an anomaly score for the feature based on the feature scores and an explanation; and (8) executing an automated action in response to the anomaly score.

Abstract: Systems and methods for providing an archive of precedents are provided. The archive of precedents includes determining precedent data; parsing a new-event XML file for string data, wherein the string data is related to a new event; generating a vector embedding from the string data; determining a similar embedding in a vector space, wherein the similar embedding is a vector embedding of an instance of precedent data; parsing XML files of the precedent and new event and using parsed data to generate an old-to-new value map; retrieving the corresponding XML file and the corresponding precedent-event document associated with the vector embedding of the instance of precedent data; and providing, as input to a natural language processing model, the list of text strings recovered from the editable form of the document of the precedent event and the old-to-new value map.

Abstract: Automated evaluation and improvement of code quality is disclosed. A method may include: receiving code to optimize; evaluating the code for a plurality of code quality dimensions using a LLM, wherein the LLM returns a code quality score and a qualitative summary for each code quality dimension; generating an overall code quality score based on the code quality score and the qualitative summary; improving the code using the LLM based on the code quality score and the qualitative summary for each code quality dimension; evaluating the improved code for the plurality of code quality dimensions using the wherein the LLM returns an updated code quality score and an updated qualitative summary; generating an overall code quality score based on the updated code quality score and the updated qualitative summary; and outputting the improved code in response to the updated code quality score being higher than the overall code quality score.

Abstract: Systems and methods for anomaly detection in software-defined networks from observed host metrics are disclosed. A method may include: (1) training a random forest model comprising a plurality of trees with historical metrics from a software defined network, the software defined network comprising a plurality of hosts; (2) receiving metrics for a plurality of features from the hosts in the software defined network; (3) providing the metrics to the trained random forest model; (4) receiving, from the trained random forest model, a prediction of an anomalous hosts for one of the hosts; (5) identifying a subset of the plurality of trees that contributed to the prediction; (6) generating feature scores for the feature from the subset of trees; (7) generating an anomaly score for the feature based on the feature scores and an explanation; and (8) executing an automated action in response to the anomaly score.

Abstract: Systems and methods for providing an archive of precedents are provided. The archive of precedents includes determining precedent data; parsing a new-event XML file for string data, wherein the string data is related to a new event; generating a vector embedding from the string data; determining a similar embedding in a vector space, wherein the similar embedding is a vector embedding of an instance of precedent data; parsing XML files of the precedent and new event and using parsed data to generate an old-to-new value map; retrieving the corresponding XML file and the corresponding precedent-event document associated with the vector embedding of the instance of precedent data; and providing, as input to a natural language processing model, the list of text strings recovered from the editable form of the document of the precedent event and the old-to-new value map.