Abstract: Computing devices, computer-readable storage media, and computer-implemented methods are disclosed for prediction of capacity. In a central tier, central-tier benchmark values are generated from benchmark testing performed on different test configurations in a reference execution environment. In a deployment tier, deployment-tier benchmark values are generated from benchmark testing performed on a baseline deployed configuration in many execution environments. A sizing model is learned from the central-tier benchmark values to predict execution platform requirements given a set of workload input parameters. A performance model is learned from the deployment-tier and the central-tier benchmark values to predict a performance delta value reflecting relative performance between a particular execution environment and the reference execution environment. The performance delta value is used to adjust predicted execution platform requirements to tailor the prediction to a particular execution environment.

Abstract: The invention provides an N-benzyl-N-arylsulfonamide derivative, which is an N-benzyl-N-arylsulfonamide compound represented by formula (I) or a pharmaceutically acceptable salt or solvate thereof. The N-benzyl-N-arylsulfonamide derivative is obtained by condensing a substituted nitrobenzene with 5- or 6-membered nitrogen-containing aliphatic heterocycle (the ring B), reducing the nitro group to an amino group, and subjecting the amino group to reductive amination, sulfonamidation; or by subjecting a substituted nitrobenzene to nitro reduction, reductive amination and sulfonamidation, and condensing the resultant intermediate with 5- or 6-membered nitrogen-containing aliphatic heterocycle (the ring B). It has been experimentally demonstrated that the N-benzyl-N-arylsulfonamide derivative of the invention can specifically bind to Kv1.3 potassium channel and inhibit or decrease its activity, and is useful in the treatment of autoimmune diseases caused by abnormal activation of the Kv1.

Abstract: Systems and methods for testing a subject system with a software testing process are described. The system receives Boolean states responsive to repeatedly applying a first test case to a subject system. Each Boolean state signifies an outcome of an application of the first test case to a version of a first software feature over a span of time. The system identifies test case outcomes for the first test case that are adjacent in time and different and generates an intermittency value for the first test case. The system determines that the intermittency value for the first test case exceeds an intermittency threshold and alerts an engineering resource. Finally, the system repeats the above operations until the intermittency value for the first test case does not exceed the intermittency threshold.

Abstract: Systems and methods for testing a subject system with a software testing process are described. The system receives Boolean states responsive to repeatedly applying a first test case to a subject system. Each Boolean state signifies an outcome of an application of the first test case to a version of a first software feature over a span of time. The system identifies test case outcomes for the first test case that are adjacent in time and different and generates an intermittency value for the first test case. The system determines that the intermittency value for the first test case exceeds an intermittency threshold and alerts an engineering resource. Finally, the system repeats the above operations until the intermittency value for the first test case does not exceed the intermittency threshold.

Abstract: Computing devices, computer-readable storage media, and computer-implemented methods are disclosed for prediction of capacity. In a central tier, central-tier benchmark values are generated from benchmark testing performed on different test configurations in a reference execution environment. In a deployment tier, deployment-tier benchmark values are generated from benchmark testing performed on a baseline deployed configuration in many execution environments. A sizing model is learned from the central-tier benchmark values to predict execution platform requirements given a set of workload input parameters. A performance model is learned from the deployment-tier and the central-tier benchmark values to predict a performance delta value reflecting relative performance between a particular execution environment and the reference execution environment. The performance delta value is used to adjust predicted execution platform requirements to tailor the prediction to a particular execution environment.

Abstract: Systems and methods for testing a subject system with a software testing process are described. The system receives Boolean states responsive to repeatedly applying a first test case to a subject system. Each Boolean state signifies an outcome of an application of the first test case to a version of a first software feature over a span of time. The system identifies test case outcomes for the first test case that are adjacent in time and different and generates an intermittency value for the first test case. The system determines that the intermittency value for the first test case exceeds an intermittency threshold and alerts an engineering resource. Finally, the system repeats the above operations until the intermittency value for the first test case does not exceed the intermittency threshold.

Abstract: Systems and methods for monitoring and improving software development quality are described herein. In accordance with one aspect of the present disclosure, an occurrence of a monitoring task related to source code is monitored. The source code is compiled and tested to produce a test result. The test result is analyzed. The test result analysis includes quality analysis to assess the quality of the source code.

Abstract: A method comprises sniffing an input from a client device to a host device and an output from the host device to the client device; recording a first time parameter in response to a characteristic data unit being inserted into the input that is outputted from a host stack of a host device; recording a second time parameter in response to the output that comprises the characteristic data unit being received by the host stack; and measuring a latency based on the first time parameter, the second time parameter and arrival time of the input and the output at a sniffing point.

Abstract: A method comprises sniffing an input from a client device to a host device and an output from the host device to the client device; recording a first time parameter in response to a characteristic data unit being inserted into the input that is outputted from a host stack of a host device; recording a second time parameter in response to the output that comprises the characteristic data unit being received by the host stack; and measuring a latency based on the first time parameter, the second time parameter and arrival time of the input and the output at a sniffing point.