A log message grouping apparatus calculates a coincidence degree evaluation value E1 representing the degree of coincidence between minority words, which are relatively low occurrence frequency words, out of the words that form a log message and minority words in another log message. The log message grouping apparatus further calculates an occurrence degree evaluation value E2 representing the degree of occurrence of the combination of a classification value of a log message and the classification value of a past log message on the basis of the occurrence frequency of the combination of the classification values and calculates an association degree evaluation value E3 representing the degree of association between the log message and the past log message on the basis of the coincidence degree evaluation value E1 and the occurrence degree evaluation value E2.

There are disclosed various methods, apparatuses and computer program products for a testing apparatus. In accordance with an embodiment the testing apparatus includes a frame; a gripping head for gripping a device to be tested; a first movement element for moving the gripping head with respect to the frame; a movement detector to detect at least one of a location and a position of the device; a touching element for touching the device; an imaging device for capturing images of the device; a display for generating visual information for capturing by the device; a set of sensors for examining operations of the device; a set of actuators for providing signals for reception by the device; and a set of plugs adapted to be inserted into a socket of the device.

Embodiments of the invention are directed to computer-implemented methods, computer systems, and computer program products for testing hardware. The method includes reading a stream of test instructions. The method further includes determining if test instruction exceptions present in the stream of test instructions. The method further includes inserting an interrupt into the test instruction stream for each determined test instruction exception. The method further includes generating one or more error messages for each determined test instruction exception.

A method to securely collect diagnostic information from an IT product is disclosed. In one embodiment, such a method includes contacting, by an IT product, an external service provider. Upon contacting the external service provider, the IT product checks a database of the external service provider to determine if diagnostic information associated with the IT product needs to be collected. If diagnostic information needs to be collected, an authorization request is sent to an administrator of the IT product. If the authorization request is approved, the IT product collects the diagnostic information and transmits the diagnostic information to the external service provider. A corresponding system and computer program product are also disclosed.

Embodiments of the invention are directed to computer-implemented methods, computer systems, and computer program products for testing hardware. The method includes reading a stream of test instructions. The method further includes determining if test instruction exceptions present in the stream of test instructions. The method further includes inserting an interrupt into the test instruction stream for each determined test instruction exception. The method further includes generating one or more error messages for each determined test instruction exception.

An automated test platform is disclosed for use in developing and troubleshooting customized software for multimedia content receivers. The automated test platform allows developers to script test cases that permit interaction with multiple content receivers at the same time. The test platform can be applied generally to any client-server system. The automated test platform is used to create scripts, run the scripts on multiple content receivers, and view test results. A graphical user interface (GUI) is provided that allows technicians without any programming experience to build and run complex interactive test sequences in a modular fashion. Such an automated test platform can be used to test cable and satellite television set top boxes, as well as DVD players, streaming media receivers, and game consoles.

Example techniques may be implemented as a method, a system or more non-transitory machine-readable media storing instructions that are executable by one or more processing devices, Operations performed by the example techniques include obtaining data representing results of tests executed by one or more test instruments on an initial set of devices under test (DUTs) in a test system; and using the data to train a machine learning model. The machine learning model is for predicting which of the tests will produce failing results for a different set of DUTs. DUTs in the different set have one or more features in common with DUTs in the initial set.

An error rate measurement apparatus includes an operation display unit and a display control unit. Displays a measurement result when the matrix scan function is executed. The display control unit displays a first coefficient value in a selectable manner by tabs of a number corresponding to the Full Swing value, uses each one of combinations of the first coefficient value, each second coefficient value, and each third coefficient value on the selected table as the cell, displays an error count value and the bit error rate for each cell, which are obtained by the matrix scan function on a display screen in a matrix, and identifies and displays the bit error rate for each cell on the display screen according to an error degree.

Systems and methods for tracking mobile device software errors are disclosed. A mobile device, in response to receiving an indication of shaking, may capture error diagnostic information including, for instance, a screen shot, user information, and/or a session log. The mobile device may generate an error report including the error diagnostic information, and may submit the error report to a server after a user authorizes the submission. The mobile device may further subscribe the user to error report tracking, which may include periodically receiving and displaying progress status updates for a software error indicated by the error report. The progress status update may indicate that, for instance, the software error has previously been reported by a second user, that a solution for the software error is pending, or that a solution for the software error has been found.

A hierarchical data processor test system (and method) is usable to configure a hierarchical plurality of similarity operators and process predetermined input test data through a data processor under test (DPUT) to produce corresponding DPUT output data. The test system is further usable to process the DPUT output data and the predetermined output test data through at least some of the plurality of similarity operators in hierarchical order, determine a highest order similarity operator that indicates a match between the DPUT output data and the predetermined output test data, and compute a confidence score based on the highest order similarity operator that indicated the match. Based on the confidence score exceeding a threshold, the test system also may include activating the DPUT for use by non-test data.