Abstract: A system automatically generates resiliency tests to detect the resiliency of an application implemented over multiple machines. In operation, one or more services operating on a plurality of machines is automatically identified. Fault targets are then automatically created based on the identified one or more services. The generation of the fault targets may be based on the identified services, user input, or past performance of the plurality of machines. Experiments may then be conducted, based on the created targets, on the one or more services. The experiments may insert faults into resources associated with the services. The resources may include CPU resources, memory, networking resources, and APIs. A resiliency score may be generated based on the experiments.

Abstract: A system that automatically reduces the time to execute software testing through intelligent test selection and execution. The system automatically detects what tests to execute based on code that has been changed, which is a subset of the entire list of tests to run for the block of code. Once the subset of tests is identified, annotations for tests are processed to update the subset as desired by the code administrator. Once updated, the system then automatically obtains the tests for the updated subset of tests. The tests to be executed are then distributed into groups or buckets. The distribution is set so that each group of tests will have as close to the same execution time as possible. The tests in each group or bucket are then executed concurrently with the other grouped tests.

Abstract: The present system automatically allows a user to create a pipeline for performing a progressive rollout and automatically performs the rollout in progressive steps. As part of creating a pipeline, a user creates multiple rollout phases and multiple approval phases. At each rollout phase, a portion of users using the current version of a software receive a rollout or update. The types of users may be configured based on user attributes. The approval phase for each rollout may determine if the software at the customer location is satisfying certain key performance indicator (KPI) requirements and whether the software update is correcting what it was intended to address. The present technology may automatically apply the updates, automatically review the performance of the updated application, and automatically approve the rollout to move onto the next phase, all without any administrator decisions.

Abstract: A system that provides intelligent shutdown of a resource when the resource is idle for a set period of time. A set of rules may be configured to determine when to shut down the resource, in what order, how to handle traffic when a resource is shut down, and how to startup resources that have been previously shut down when demand requires it. In addition to setting an idle time at which point to shut down the resource, the type of shutdown may be configured. Dependencies may be configured, which indicate how the resource should be shut down or powered up with respect to other resources. A user interface may be used to configure rules, and a dashboard may be provided to see the current status and cost savings information for a set of resources for a system.

Abstract: An intelligent editor for creating a pipeline configuration file. The pipeline configuration file can be a YAML configuration file or in some other data serialization language. The intelligent editor is in the form of a YAML editor that provides multiple user interfaces for editing a pipeline configuration file. The YAML editor includes a code-based editing UI to edit the YAML configuration file code itself, and a visual-based editor UI for creating and modifying the configuration file through using graphical icons. The code-based editor UI includes an intelligent field and value recommendation feature, auto-complete feature, a semantic error detection feature, and a field documentation feature.

Abstract: A pipeline management system that allows for creating and managing pipelines and pipeline execution. The system allows a user to create pipeline stages, steps for the pipeline stages, and commands for the pipeline steps. The system efficiently allows users to create and manage multiple pipelines by allowing attributes of commands, such as for example variables in the commands, to be entered at runtime. The runtime entry attributes for a pipeline make up an input set. A pipeline can be bound with one or more input sets that are used to fill or instantiate the pipeline variables at runtime.

Abstract: A system that automatically reduces test cycle time to save resources and developer time. The present system selects a subset of tests from a full test plan that should be selected for a particular test cycle, rather than running the entire test plan. The subset of tests is intelligently selected using metrics such as tests associated with changed code and new and modified tests.

Abstract: A system can monitor applications and analyze the metrics to determine if one or more of the applications are regressing or performing as expected. The metric analysis includes performing a first short term data analysis and, if data is not as expected, a second short term analysis based on machine learning-based pattern recognition machines. If the short-term analysis finds the metrics aren't as expected, a long-term analysis is performed. The long-term analysis can compare chunks of streaming metric data to cached metric blocks and historical data, and can include a concept drift analysis.

Abstract: A system that automatically reduces test cycle time to save resources and developer time. The present system selects a subset of tests from a full test plan that should be selected for a particular test cycle, rather than running the entire test plan. The subset of tests is intelligently selected using metrics such as tests associated with changed code and new and modified tests.

Abstract: A system automatically allocates network infrastructure resource costs with business services. The present system continuously monitors the software system to detect events and pricing data for a software system. The system then allocates network infrastructure resource costs with business services based on the detected event data and pricing data. In some instances, the cost for a resource for a business service is determined based on the percentage of overall resource usage that is allocated to the particular business service. The allocated amounts can be added to running totals and aggregated for extended periods.

Abstract: A system automatically correlates infrastructure usage and cost data to key performance indicators for a software system. The system continuously monitors the software system to detect operational events, key performance indicators, service degradation, and other events of a software system. The events may include memory usage, processor cycles used, and other data that may be expressed as time series data. The metrics such as for example metrics associated with operations initiated directly or indirectly by a user or customer request or service usage. The system then correlates network infrastructure resource usage with key performance indicator data. Based on detected correlations, the present system can automatically predict future infrastructure requirements based on forecast KPI data, as well as the corresponding costs of the predicted infrastructure requirements.

Abstract: The present system provides continuous delivery and service regression detection in real time based on log data. The log data is clustered based on textual and contextual similarity and can serve as an indicator for the behavior of a service or application. The clusters can be augmented with the frequency distribution of its occurrences bucketed at a temporal level. Collectively, the textual and contextual similarity clusters serve as a strong signature (e.g., learned representation) of the current service date and a strong indicator for predicting future behavior. Machine learning techniques are used to generate a signature from log data to represent the current state and predict the future behavior of the service at any instant in time.

Abstract: A system can monitor applications and analyze the metrics to determine if one or more of the applications are regressing or performing as expected. The metric analysis includes performing a first short term data analysis and, if data is not as expected, a second short term analysis based on machine learning-based pattern recognition machines. If the short-term analysis finds the metrics aren't as expected, a long-term analysis is performed. The long-term analysis can compare chunks of streaming metric data to cached metric blocks and historical data, and can include a concept drift analysis.

Abstract: A system monitors applications, analyzes metrics, and provides a dashboard that communicates whether an application is performing as expected. The metric analysis includes performing one or more of a first short term data analysis, a second short term analysis based on machine learning-based pattern recognition machines, and a long-term analysis is performed. Transaction performance metrices are determined based on the monitored of the application. The transaction performance metrices are scored, scaled, and aggregated into a single scaled representation for the application. The scaled application value is then reported to a user through a dynamically updated dashboard. The dashboard displays graphical information representing the health of monitored transactions over time. The reported information can be expanded to additional layers of detail.

Abstract: The present system uses delegates installed in remote environments to called and transmit, to a remote manager, time series metric data (or data from which metrics can be determined) in real-time. The numerical time series data is persisted, and a learned representation is generated from the data, for example by discretization. The learned representation is then clustered, the clusters are compared to new data, anomalies are determined, and deviation scores are calculated for the anomalies. The derivation scores are compared to thresholds, and results are reported through, for example, a user interface, dashboard, and/or other mechanism.

Abstract: The present system provides continuous delivery and service regression detection in real time based on log data. The log data is clustered based on textual and contextual similarity and can serve as an indicator for the behavior of a service or application. The clusters can be augmented with the frequency distribution of its occurrences bucketed at a temporal level. Collectively, the textual and contextual similarity clusters serve as a strong signature (e.g., learned representation) of the current service date and a strong indicator for predicting future behavior. Machine learning techniques are used to generate a signature from log data to represent the current state and predict the future behavior of the service at any instant in time.

Abstract: The present system uses delegates installed in remote environments to called and transmit, to a remote manager, time series metric data (or data from which metrics can be determined) in real-time. The numerical time series data is persisted, and a learned representation is generated from the data, for example by discretization. The learned representation is then clustered, the clusters are compared to new data, anomalies are determined, and deviation scores are calculated for the anomalies. The derivation scores are compared to thresholds, and results are reported through, for example, a user interface, dashboard, and/or other mechanism.

Abstract: A connector for connecting electrical conductors and including first and second conductor-containing bodies configured to sealingly connect the conductors when the first and second body are matingly joined. A passageway is formed to accept a pin when the first and second body are matingly joined so that the pin, which is a different color than the first and second body, is readily observable in position to confirm that the first and retains the first and second body in mating engagement and so that the pin, which is a different second body are matingly joined.

Abstract: The specification discloses a tractor-trailer electrical connector system including a first socket for being mounted in the tractor cab and having a plurality of male terminals for being wired to the fuse box of the tractor. A second socket is provided for being mounted in the trailer and includes a plurality of male terminals for being wired to the trailer terminal box. A multiwire cable is provided with a length to extend between the first and second sockets. First and second plugs are integrally molded to the ends of the cable, with each plug having a plurality of female receptacles dimensioned to receive the male terminals of the first and second sockets. Each of the sockets and plugs includes an inner core of elastomeric material which surrounds and insulates the terminals or receptacles. A rigid synthetic polymer housing covers the inner core to provide one piece sockets and plugs.

Abstract: The specification discloses a replaceable electrical connector plug for attachment to a multi-wire cable. A rigid thermoplastic housing includes a front plug portion and a removable rear portion which forms a rear cavity. A set of first female electrical terminals are disposed in the front of the front plug portion in order to receive male terminals which extend from a mating electrical socket. A second set of female electrical terminals are disposed in the rear of the front plug portion. The first and second sets of female terminals are electrically interconnected. Male terminals are connected to the ends of the multi-wire cable and are received within the second set of female terminals. The cable extends through the rear cavity. The removable rear portion may be selectively removed in order to enable replacing of the electrical connector plug when desired.