Abstract: The design for modern graphical interfaces generated by an application under test (AUT) can take many forms. A codeless testing system reads the output generated by the AUT to identify components, however, scroll bars may be present but in a form that is not reliably identified. As a result, an AUT may comprise scrollable graphical elements that are not identified as such resulting in the performance of tests that are incomplete. By applying a middle button click event, an associated pointer changes form and reveals an associated graphical element to comprise a scrollable element. When the pointer change occurs, testing directed to the scrollable element is then included to perform a more complete testing of the features of the AUT.

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 request to authenticate is received. For example, the request to authenticate may be to authenticate to a software application or a device. The request to authenticate is for a first authentication level for a user. The first authentication level is one of a plurality of authentication levels for the user. The request to authenticate is authorized based on a provided one or more authentication factors (e.g., a valid username/password). A plurality of authentication tokens are retrieved. The plurality of authentication tokens are associated with the first authentication level. In addition, each authentication token of the plurality of authentication tokens is associated with an individual application of a plurality of applications. Access to information in the plurality of applications is granted based on the plurality of authentication tokens.

Abstract: Examples relate to detecting an abnormality. The examples disclosed herein enable receiving, from a first user, a first request to perform a first transaction on at least one data record. A plurality of transactions originated from the first request may be organized in a first hierarchical tree-based data structure having multiple depth levels. The data structure may comprise a root node representing the first transaction and a leaf node representing a second transaction. The examples further enable detecting the abnormality based on at least one parameter where the at least one parameter comprises a size of the data structure and a depth level associated with the leaf node.

Abstract: Command line inputs to a system by a user or automated script can comprise a number of legitimate commands but, as a series, reveal a reconnaissance attack, such as to gain knowledge of a system without a legitimate reason to do so. A trained artificial intelligence monitors the command line inputs to the system, as a series, and determines therefrom whether a match exists to a reconnaissance attack. The match may be a non-exact match, such as a match determined by a long short-term memory (LSTM) machine learning model. A reconnaissance attack response may then be initiated upon determining a match is present.

Abstract: A request to install a first version of a software application is received. The first version of the software application is stored in a first blockchain in a first distributed blockchain ledger. In response to receiving the request to install the first version of the software application, the first version of the software application is validated by running a hash of the first blockchain. In response to validating the first version of the software application, the first version of the software application is installed from the blockchain to a device. The software application may also be validated after being installed to the device.

Abstract: A query plan includes steps to implement a query and debug steps interleaved among the steps. An execution engine of a database system executes each step of the query plan to realize the query. The execution engine executes each debug step of the query plan to generate debugging information for the step preceding the debug step within the query plan. The debugging information is queryable.

Abstract: Example implementations relate to simulating an environment. For example, a system for environment simulation may include a simulation engine to build an environment simulation to mimic portions of a real environment relevant to a detected anomaly trend, an acceleration engine to simulate, within the environment simulation, a scenario associated with the detected anomaly at a rate faster than the scenario occurs in the real environment, a abnormal behavior engine to detect a abnormal behavior associated with the scenario, and an adaptation engine to modify a device within the real environment to be adaptive to the scenario, based on the detected abnormal behavior.

Abstract: Examples herein involve authorization of use of cryptographic keys based on cryptocurrency payments. An example method includes analyzing a request for a cryptographic key of a key server where the request may be received from a requesting device and the cryptographic key is used to decrypt or encrypt a message of the request, and authorizing use of the cryptographic key to decrypt or encrypt the message based on a balance in a cryptocurrency wallet associated with the request.

Abstract: Network traffic is monitored over a period of time (e.g., network traffic of a corporate network). Based on the monitored network traffic: an abstract temporal graph of the network traffic is generated; graph-based node embeddings of the abstract temporal graph are learned; edge tabular embeddings for edges of the abstract temporal graph are learned; and hybrid embeddings are computed. The computed hybrid embeddings are based on the learned graph-based node embeddings for the abstract temporal graph and the learned edge tabular embedding for the edges of the abstract temporal graph. This process is then repeated over multiple time periods and temporal trajectories are computed using the computed hybrid embeddings for each time period. The temporal trajectories are then used for analysis of the network. For example, the temporal trajectories are used to identify anomalies for prevention of security breaches of the network.

Abstract: A secondary fragment of an encryption key is received. The secondary fragment is associated with an authentication process of a user. The secondary fragment is one of a plurality of secondary fragments of the encryption key. The user is authenticated (e.g., by validating a username/password). The encryption key is regenerated using the secondary fragment and a primary fragment of the encryption key. In response to regenerating the encryption key using the secondary fragment and the primary fragment, and authenticating the user: access is granted, to the user, by unencrypting an encrypted data record using the regenerated encryption key.

Abstract: Prior to initiating discovery of computing resources of an information technology (IT) infrastructure, an input query and a discovery job are received, and one or more trigger configuration items (CIs) are calculated. The input query specifies how the computing resources are discovered, and has a filter parameter. The discovery job is for the input query, has a job parameter, and is associated with a trigger query that is a subset of the input query. The trigger CIs are calculated based on the trigger query, such that the filter parameter of the input query is replaced with the job parameter in the trigger CIs. Discovery of the computing resources is initiated by dispatching each trigger CI. As discovery of the computing resources occurs, CIs representing the computing resources matching the trigger CIs are received, and the CIs are populated within a configuration management database (CMDB).

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: Results of an authentication process are received. The authentication process allows for a graded level of authentication using a plurality of authentication types (e.g., a username/password and a fingerprint scan). Encrypted data is then accessed. The encrypted data has been encrypted using a plurality of encryption levels. The data is unencrypted based on the graded level of authentication. In a second embodiment, a system and method are provided that establish a communication session (e.g., a voice or email communication session). The communication session is between a plurality of users. During the communication session, an indication is received to change an encryption level for the communication session. In response to receiving the indication to change the encryption level for the communication session, an encryption level of the first communication session is dynamically changed from a first level of encryption to a second level of encryption.

Abstract: In some examples, first segment of computer language text in a first rule in IT workflow data and a second segment of computer language text in a second rule in the IT workflow data may be identified. In some examples, a similarity score may be determined between the first and the second rules based on a comparison of the first segment with the second segment.

Abstract: Systems and methods include determining a target date; detecting a calendar in a graphical user interface; recognizing a month name in the detected calendar; recognizing one or more date numbers in the detected calendar; identifying a week start day for the detected calendar; identifying a position in the detected calendar associated with the target date based on the identified week start day for the detected calendar and the recognized one or more date numbers in the detected calendar; and automatically selecting the position in the detected calendar associated with the target date.

Abstract: A request to authenticate is received (e.g., a request to login with a username/password). The request to authenticate comprises an address associated with the request to authenticate (e.g., an IP address). The request to authenticate is validated. In response to validating the request to authenticate, a message is sent to a routing device that identifies the address as authenticated for routing packets. In a second embodiment, a DHCP discover message is received. The DHCP discover message is a request to get an IP address. A determination is made to determine if the DHCP discover message comprises a watermark. In response to determining that the DHCP discover message comprises the watermark: a DHCP offer message is sent with an IP address and a third message is sent to a routing device that identifies the IP address as valid for routing packets.

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, may cause the processor to receive a request from a client for a status of the client, and based on the status of the client, generate a token associated with application programming interface (API) calls to be received from the client. In some examples, the token may include a value representing a priority for determining an adaptive rate limiting of the API calls to be received from the client. The processor may send a response to the request, in which the response may include the status of the client and the token.

Abstract: A method for the sonification of continuous integration data to audibly represent the health/behavior of builds. The method includes receiving, by a processor, from a build server, continuous integration data for a build job in the continuous integration environment. The method also includes converting, by the processor, the continuous integration data into sonification data. The method further includes applying at least one of toccata, legato, or arpeggio to the sonification data. The method includes generating an audio file from the sonification data; and publishing and/or storing the generated audio file for end users. In some embodiments, the method also includes combining, by the processor, different sets of sonification data, wherein each set of sonification data is associated with a different build job, and wherein each set of sonification data is represented by a different instrument of a plurality of instruments.

Abstract: Quality indicators regarding a software project under development that has a plurality of functional areas are collected. Each quality indicator is normalized to a coverage of the functional area of the software project to which the quality indicator corresponds. The normalized quality indicators are correlated to previously identified anomalies of the software project, yielding an anomaly indicative value for each normalized quality indicator corresponding to a probability that the normalized quality indicator is revelatory of unidentified anomalies of the software project. A normal behavior for each normalized quality indicator is estimated. For each functional area of the software project, an anomaly score indicative of a likelihood of an unidentified anomaly within the functional area is calculated.