Abstract: Methods, systems, and computer-readable storage media for receiving a crash report provided as a computer-readable file, providing a stack trace from the crash report, adding component information to the stack trace, for each component identified in the stack trace, determining a set of features, processing sets of features through a ML model to provide a prediction identifying a component as a bug component, and assigning the bug component for resolution through a crash management system.

Abstract: In an example embodiment, a machine learned model is utilized for identifying duplicate crash dumps. After a developer submits code, corresponding test cases are used to ensure the quality of the software delivery. Test failures can occur during this period, such as crashes, errors, and timeouts. Since it takes time for developers to resolve them, many duplicate failures can occur during this time period. In some embodiments, trash triggering is the most time-consuming task of development, and thus if duplicate crash failures can be automatically identified, the degree of automation will be significantly enhanced. To locate such duplicates, a training-based machine learned model uses component information of an in-memory database system to achieve better crash similarity comparison.

Abstract: A lower rail for a seat of a vehicle has a structure with a curved movement path, in which the seat may be moved in an oblique direction or a diagonal direction according to various seat positions, thereby allowing a degree of freedom for adjusting a seat position movement. In addition, the lower rail includes a curved inner channel bent at a predetermined angle and a curved outer channel bent at about a same angle as the curved inner channel, which are separately provided and configured to be mutually combined in order to provide one curved movement path for moving the seat.

Abstract: Methods, systems, and computer-readable storage media for receiving a crash report provided as a computer-readable file, providing a stack trace from the crash report, adding component information to the stack trace, for each component identified in the stack trace, determining a set of features, processing sets of features through a ML model to provide a prediction identifying a component as a bug component, and assigning the bug component for resolution through a crash management system.

Abstract: Methods, systems, and computer-readable storage media for receiving a set of crash reports, determining a set of trace vectors by processing a set of stack traces through a first DL model, each trace vector in the set of trace vectors being a multi-dimensional vector representation of a stack trace of a respective crash report provided from the set of stack traces, generating a set of feature vectors by processing the set of trace vectors through a second DL model, each feature vector being a multi-dimensional vector representation of a stack trace of a respective crash report, and clustering each crash report in the set of crash reports into a group of a set of groups based on comparing feature vectors of respective crash reports, each group representative of a root cause resulting in respective crashes of the software system represented in one or more crash reports.

Abstract: In an example embodiment, a machine learned model is utilized for identifying duplicate crash dumps. After a developer submits code, corresponding test cases are used to ensure the quality of the software delivery. Test failures can occur during this period, such as crashes, errors, and timeouts. Since it takes time for developers to resolve them, many duplicate failures can occur during this time period. In some embodiments, trash triggering is the most time-consuming task of development, and thus if duplicate crash failures can be automatically identified, the degree of automation will be significantly enhanced. To locate such duplicates, a training-based machine learned model uses component information of an in-memory database system to achieve better crash similarity comparison.