A method for facilitating root cause analysis of a software crash by core dump analysis is disclosed. The method comprises receiving a core dump file relating to a software program, identifying unique source code lines in the core dump file for each running thread at the crash time, and determining unique source code lines as conspicuous source code lines depending on an abstraction level value indicating a number of occurrences of the conspicuous source code line in different threads. Furthermore, the method comprises determining an abstraction ratio as a function of a number of conspicuous source code lines and a number of unique source code lines, evaluating whether the predefined abstraction level value has to be adjusted by determining unique source code line as a conspicuous source code line and determining an abstraction ratio, and outputting the conspicuous source code lines and an assessment value for the abstraction ratio.

The disclosure describes techniques that enable detection of memory leaks of software executing on devices within a computer network. An example network device includes memory and processing circuitry. The processing circuitry monitors a usage of the memory by a software component operating within the network device. The processing circuitry periodically determines a memory growth pattern score for the software component based on the usage of the memory. The processing circuitry also predicts whether the user-level process is experiencing a memory leak based on the memory growth pattern score. The processing circuitry applies confirmation criteria to current memory usage of the software component to confirm that the software component is experiencing the memory leak. When the software component is experiencing the memory leak, the processing circuitry generates an alert.

A method for monitoring an operation condition of an interactive information system is provided. The interactive information system includes a touch panel and a touch control unit, and executes a resident scanning program to detect whether a hardware-related issue or a software-related issue of the touch panel or the touch control unit has occurred. When the hardware-related issue has occurred, the interactive information system downloads and executes a hardware diagnosis program to obtain a diagnosis result. When the software-related issue has occurred, the interactive information system downloads and executes a software adjustment program to resolve the software-related issue.

Examples of computing devices for establishing a communication link with a remote device to contact a remote technical support service are described herein. In an example, a computing device may include a cellular communication interface and a processor. The processor may receive a first user input indicative of contacting a remote technical support service for the computing device. Based on the first user input, the processor may further establish a communication link between the computing device and a remote device over the cellular communication interface to contact the remote technical support service when the computing device is unbootable.

Systems, methods, and computer media are described for creating efficient error reproduction scenarios. Raw workload capture data can be consolidated based on transactional dependence of the requests in the capture data. The consolidated workload capture data can be stored as a separate data structure that can be accessed to identify requests on which a request of interest in the raw workload capture data is transactionally dependent. For a request of interest (e.g., a request that caused an error), a lightweight error reproduction scenario can be generated that includes the identified transactionally dependent requests and excludes unrelated requests.

In an approach to improve detecting and correcting errors in one or more machine learning pipelines. Embodiments comprise generating a plurality of test machine learning pipeline instances based upon a target machine learning pipeline and evaluating the plurality of test machine learning pipeline instances for failure in a task. Further, embodiments identify one or more root causes of error based upon the evaluated plurality of test machine learning pipeline instances and failure in the task, and create a remediated target machine learning pipeline based upon the identified one or more root causes of error. Additionally, embodiments output the remediated machine learning pipelines.

The invention relates to a system (300) and method for performing end-to-end simulation and testing of an IoT application (102). An IoT data simulator (310) is configured to simulate an IoT environment using data received from different components in the IoT environment, which include IoT messages/data from IoT devices (106), master data from different databases (108) and data from third-party web services (110). Device templates are created that are used as blueprint for defining a plurality of device instances which include simulated device instances and live device instances. An IoT application validator (326) is configured for testing and validating the IoT application (102) by transmitting a plurality of IoT messages to the IoT application (102) and validating the behavior of the IoT application (102) to the plurality of IoT messages for all layers including, but not limited to, a UI layer (112), a business logic (114) and a data layer (116), using one or more device instances.

A debug test system is provided. The debug test system includes one or more interfaces configured to communicate with a target system and processing circuitry configured to control the one or more interfaces. Further, the processing circuitry is configured to receive information about an operation state of the target system from the target system and to generate control information for the target system to adjust a debug session on the target system. The processing circuitry is further configured to transmit the control information to the target system.

Methods, computer program products, and/or systems are provided that perform the following operations: determining that a log multi-process debug mode is specified; obtaining a log file for debugging a source code, wherein the log file includes a plurality of log records; inserting a plurality of process identifier fields into each current log record in the log file; inserting a new log record into the log file for a created new process; and providing for performance of debugging for the source code based in part on the plurality of process identifier fields inserted into each current log record.

An improved SDK includes a set of APIs and a crash handler registered with the operating system. Each API is an interface accessible by a computer software application. Up on entrance, each API determines the current thread identifier, and inserts it into a list if it is not already in the list. Each thread identifier corresponds to an API call counter, which is incremented by one at the entrance and decremented by one at the exit point of the API. The SDK also records the identifier of the thread it creates for callback functions. When a crash occurs, the crash handler is executed. It determines that the crash is related to a callback interface if the crash thread identifier matches the callback thread identifier. The crash is determined to be caused by the SDK if the API call counter corresponding to the crash thread identifier is greater than zero.