This disclosure describes systems, methods, and devices related to anomaly detection of CPE firmware revisions. A method may include collecting metrics data for a plurality of customer-provided equipment (CPE) models over a window of time; training a first autoencoder for a first CPE model of the plurality of CPE models using at least a portion of the metrics data to detect anomalies within a plurality of firmware versions of the first CPE model; identifying, using the first autoencoder, that a first firmware version of the plurality of firmware versions is anomalous across a first time series; and storing data indicating that the first firmware version of the plurality of firmware versions is anomalous across the first time series. Metrics data may include one or more of interactive voice response (IVR) session data; calls handled data; and truck schedule data.

An intelligent determination of code change review assignments and subsequent secured access to the determined assignments. Code changes undergo code change complexity determination which is based on (i) a level of importance of the module(s) in which the changes occur, (ii) the volume of metadata files impacted by the code changes, and (iii) the dependency of the code changes on external modules. A distributed trust computing network is implemented and a code change smart contract which relies on smart contract rules is used to determine and allocate code change review assignments. In this regard, data blocks within a distributed ledger define individual segments/portions of the code change file with each data block identifying a code change review assignment.

A hardware monitor arranged to assess performance of a hardware design for an integrated circuit to complete a task. The hardware monitor includes monitoring and counting logic configured to count a number of cycles between start and completion of the symbolic task in the hardware design; and property evaluation logic configured to evaluate one or more formal properties related to the counted number of cycles to assess the performance of the hardware design in completing the symbolic task. The hardware monitor may be used by a formal verification tool to exhaustively verify that the hardware design meets a desired performance goal and/or to exhaustively identify a performance metric (e.g. best case and/or worst case performance) with respect to completion of the task.

A system for enforcing compliance and testing for software development, comprising an indexing service configured to create a dataset by processing and indexing source code of a project by a developer, perform a code audit on the indexed source code, store results from the code audit in the dataset, gather additional information relating to the provided project, store the additional information in the dataset, and store the dataset into memory; and a monitoring service configured to continuously monitor the project for source code changes and make changes to the dataset as needed. Further comprising an enforcement module to automatically verify code and other media related to the software development process by ensuring obligations from a rules database are met and where not able to automate the compliance check forward to an appropriate authority, receive back the manually reviewed compliance check, then produce and implement automated recommendations for compliance adherence.

A system and method for meta-indexing, search, compliance, and test framework for software development using smart contracts is provided, comprising an indexing service configured to create a dataset by processing and indexing source code of a project provided by a developer, perform a code audit on the indexed source code, store results from the code audit in the dataset, gather additional information relating to the provided project, store the additional information in the dataset, and store the dataset into memory; and a monitoring service configured to continuously monitor the project for at least source code changes and make changes to the dataset as needed. Additionally, a smart contract authority creates and enforces smart contracts for every transaction taking place upon the software essentially mandating and guaranteeing the security and authenticity of the software during the software's development and use.

A method for troubleshooting abnormal behavior of an application hosted on a networked computer system. The method may be implemented by a root cause analyzer. The method includes tracking a single application performance metric across all the clients of an application hosted on a networked computer system and analyzing an aggregated application based on the single application metric. The method involves determining outlier client attributes associated with an abnormal transaction of the application and ranking the outlier client attributes based on comparisons of historical and current abnormal transactions. The method associates one or more of the ranked outlier client attributes with the root cause of the current abnormal transaction. Association rule learning is used to associate one or more of the ranked outlier client attributes with the root cause.

Systems and methods for using instrumentation for maintenance of a user-configured program in a cloud computing environment are herein disclosed as comprising, in an implementation, intercepting operation data pertaining to the user-configured program, including a start time, an execution time interval, an operation, and an origin of the operation, canonicalizing the intercepted operation data by stripping operation-specific variable data from the operation data, aggregating the canonicalized operation data based on the start time, the canonicalized operation data, and the origin of the operation, and storing the aggregated operation data within a time series database in the execution time interval based on the start time.

A method may include extracting, from a function included in code, sub-tokens and program analysis features, generating sub-token vectors from the sub-tokens and a program analysis vector from the program analysis features, combining, by a machine learning model, the sub-token vectors to obtain a combined sub-token vector, combining the combined sub-token vector and the program analysis vector to obtain a function vector, and classifying, using the function vector, the function as a function category.

A program development device provides a development environment of a user program executed by a control device. The program development device receives setting of an attribute related to reference from a program for each of one or more variables used in the user program including a first program and a second program having a calling relationship. The set attribute includes a first attribute referred to by any one of the first program and the second program and a second attribute referred to by both the first program and the second program. The program development device analyzes the user program and evaluates consistency based on the set attribute related to reference from the program for each of the one or more variables.

Database systems and methods are provided for identifying a change associated with an update to executable code resulting in test failure. One method involves calculating risk scores for different changes associated with the update based on change characteristics associated with the respective changes, identifying a change from among the different changes associated with the update based on the risk scores associated with the respective changes, generating a modified update to the executable code that includes the identified change and excludes remaining changes of the update from the modified update, and initiate execution of one or more tests with respect to a compiled version of the modified update to the executable code. When execution of the one or more tests against the modified update results in a test failure, the change is identified as a potential root cause of the test failure associated with the update.