Abstract: A request to send a file is received. A file type for the file is identified. For example, the file type may a Joint Photographic Experts Group (JPEG) file or a Portable Document Format (PDF) file. In response to receiving the request to send the file and identifying the file type for the file, a format of the file is reordered. Reordering the format of the file is based on an application and/or an application type that will be receiving the file. For example, the application may be an Adobe Acrobat® reader that is receiving a PDF file that has been reordered for more efficient reading.

Abstract: Methods and apparatus are disclosed to automatically generate a software test script from video and/or user input. Example methods disclosed herein include generating a first set of images from a video comprising recorded testing of software. The example method also includes processing the first set of images to remove unnecessary images. The example method also includes comparing each image to one or more adjacent images to identify one or more user actions performed in each image. The example method also includes translating each identified user action and/or user input into one or more test script parameters to generate an automated test script to execute on the software.

Abstract: In some examples, push control may include generation of a learning-based decision model based on analysis of data associated with historical usage of an application. For a request for content associated with usage of the application, the learning-based decision model may be analyzed to determine a next request for additional content expected to occur after the request for content. Further, a push operation may be performed to push the additional content associated with the next request to a source of the request for content.

Abstract: A plurality of events associated with a communication device are received. The plurality of events relate to a plurality of changes of hardware, firmware, and/or software in the communication device. The plurality of events are stored as a plurality of blocks in a blockchain. One or more anomalies associated with the plurality of changes of the hardware, the firmware, and/or the software are identified. In response to identifying the one or more anomalies associated with the plurality of changes of the hardware, the firmware, and/or the software in the communication device, a software image of the communication device is received and added to the blockchain. A second embodiment discloses using this process for a hypervisor using virtual machines or containers.

Abstract: Techniques for Precision-Preserving Format-Preserving Encryption (PPFPE) to protect numeric values and strings with leading/trailing and special characters, while preserving their precision, both as a protected value (e.g., ciphertext) and when decrypted are provided. In one aspect, a plaintext string is parsed to detect a first and last sentinel characters, wherein any character(s) preceding the first sentinel character comprises a leading character(s) and any character(s) following the last sentinel character comprises a trailing character(s). Any leading character(s) preceding the first sentinel character and/or any trailing character(s) following the last sentinel character are passed through. A ciphertext string is generated by encrypting the first and last sentinel character using one or more subsets of a set of encryption characters; and encrypting the portion of the plaintext string between the first and last sentinel character using the set of encryption characters.

Abstract: A method includes applying, by a computer, supervised machine learning to classify a network device that is associated with a security event occurring in a computer system based on data representing features of the network device. The security event is associated with a potential security threat to the computer system, and the classification of the network device by the supervised machine learning is associated with a confidence. The technique includes, in response to the confidence being below a threshold, applying an active machine learning classifier to the data to learn a classification for the data and using the classification learned by the active machine learning classifier to adapt the supervised machine learning to recognize the classification.

Abstract: A predefined hierarchical service tree can be stored that includes a top at a service category definition level and a bottom at a level of a number of devices, each of the number of devices selected to perform a specific service function. A sequential progression can be enforced through the predefined hierarchical service tree to perform a service.

Abstract: Systems and methods of associating text with a graphical user interface (GUI) object are disclosed. Disclosed systems and methods include identifying a GUI object and a text string. A positional relationship between the GUI object and the text string is determined. Based on the positional relationship between the GUI object and the text string, a map with an indication associating the GUI object with the text string is updated.

Abstract: A request is received from a browser (e.g., a tool that runs on top of or in a browser). The request is to authenticate via an authentication credential provided from a communication device that is external to a test communication device running the browser. The request is queued in a request queue. The authentication credential is received from an interceptor that intercepts the authentication credential in the external communication device. The authentication credential is sent to the browser, which in turn sends the authentication credential to the application under test. This completes the authentication process in the application under test. The request is then removed from the request queue. This allows for an automated multi-factor authentication process that can be used for testing the application under test.

Abstract: An image of a graphical user interface is captured. A process is started to record identification of a graphical object (e.g., a text field) in the image of the graphical user interface. Recording identification of the graphical object in the image of the graphical user interface is based on machine learning. An end to the process to record identification of the graphical object in the image of the graphical user interface is identified. A machine learned software object is updated. For example, the machine learned software object is updated with a location of the graphical object. The updated machine learned software object then is used to test the graphical object. For example, to automatically fill in the text field using a test script.

Abstract: A dynamic transaction associated with a user is identified. For example, the user logging into a server is identified as a dynamic transaction. The dynamic transaction associated with the user is stored in a record (e.g., stored as a block in a blockchain). A first hash from a user device associated with the user is received. The second hash is generated based on the dynamic transaction. A second hash is generated based on the record. The first hash is compared to the second hash. In response to the first hash matching the second hash, the user is authenticated by providing access to a computer resource. For example, the user may be granted access to a database.

Abstract: Examples relate to load testing. The examples disclosed herein enable obtaining lines of code that are recorded as an application is executed in a client computing device, the lines of code being recorded in chronological order of the execution; determining whether a dependency on at least one variable exists in individual lines of the lines of code; in response to determining that the dependency exists, storing the dependency in a data storage; identifying, from the lines of code, a line of code including a network call statement that calls a called variable; and eliminating a first subset of the lines of code based on the called variable and dependencies stored in the data storage, wherein a second subset of the lines of code that remain after the elimination comprises user-entered parameter data.

Abstract: A plurality of different types of resource access events are identified. For example, a resource access event may be an administration event where a user is given certain access rights to view/modify a resource, such as, a database record. A plurality of blocks are generated, where each block is associated with an individual one of the plurality of different types of resource access events. The plurality of blocks are added to a first resource access blockchain. The blockchain can be used to track the various types of resource access events.

Abstract: A poll of a first database is initiated (e.g., a SOT database). The poll of the first database is to identify if any transactions have occurred since a previous poll of the first database. Based on the poll of the first database, information is received that identifies one or more transaction identifiers. A missing transaction identifier is identified based on a contagious list of transaction identifiers. In response to identifying the missing transaction identifier, the missing transaction identifier is stored in a list of missing transaction identifiers for use in a later poll of the first database. By tracking the missing transaction identifier, the process can identify transactions that take longer to commit without introducing lag for transactions that commit quickly.

Abstract: A technique includes accessing, by at least one hardware processor, a recorded request and a recorded response associated with an integration test involving a first computer system and a second computer system. The recorded request was previously issued by the first computer system to the second computer system to cause the second computer system to provide the recorded response. The technique includes, in a virtualized integration test involving the second computer system and initiated using the recorded request, comparing, by the hardware processor(s), the recorded response to a request produced by the second computer system in the virtualized integration test. The technique includes identifying, by the hardware processor(s), an action taken by the second computer system as being likely to be associated with a regression based on the comparison.

Abstract: Method and system to automatically verify the layout of a webpage on various screen sizes. The method includes retrieving screen size data associated with the webpage. The method further includes determining anchor points for the webpage based on the retrieved screen size data. The method also includes rendering the plurality of elements of the webpage based on the retrieved screen size data and the determined anchor points and identifying a respective location of each of the plurality of elements of the webpage to verify the layout of the plurality of elements of the webpage. If an error in the layout of the webpage is detected, a visual and/or audio alert is sent.

Abstract: A docker image is received. The docker image is for a container. The container contains files that allow for virtualization of applications that run within the container. The docker image is parsed to identify layer files in the docker image. Installed software components (e.g., installed files) and/or hardware components in the layer files are identified. Software application index calls are made to generate information that identifies relationships between the installed software components and/or hardware components. The relationships between the installed software components and/or hardware components are then displayed to a user.

Abstract: An image of a graphical user interface is captured. For example, a screen shot of a browser display is captured. Text syntax is executed that contains one or more parameters for identifying a graphical object. For example, the text syntax may identify a rectangle that contains the text “OK” where the text is red. Based on the text syntax, a graphical object is identified in the image of the graphical user interface. Information is returned that identifies how to access the graphical object in the graphical user interface. For example, coordinates of the graphical object are identified. This information can then be used in a test script using existing programming languages to test the graphical user interface. For example, the coordinates may be used to click on the OK button.

Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor cause the processor to receive a first IP address using a type of a discovery process for discovery of network configuration information. The processor may determine whether the first IP address is a private IP address or a public IP address. The processor may determine a second attribute of the first IP address, which may include information on a routing domain for the first IP address based on the type of the discovery process used for discovery of the network configuration information. The processor may merge the first IP address with a second IP address based on the second attribute of the first IP address, in which the second IP address may be replaced with the first IP address.

Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor cause the processor to receive parameters for a partition range for a partition key associated with a table. The table may have a vertical table structure. The processor may generate an index of vertical table columns based on the received parameters for the partition range. The generated index may be a physical store for data from the table and may have a subset of rows of the table correlating to the partition range. The processor may receive a query having a predicate associated with the partition key associated with the table and, based on a determination that the predicate included in the received query is within a partition range for the generated index, the processor may execute the query using the generated index.