Abstract: According to some embodiments, systems and methods are provided, including a memory storing processor-executable code; and a processing unit to execute the executable program code to: execute an automation test for a user interface application, wherein execution of the automation test includes accessing a web-object; determine whether the web-object is included in an object repository; in a case it is determined the web-object is not included in the object repository, create the web-object in the object repository; and in a case it is determined the web-object is included in the object repository: determine whether the accessed web-object includes at least one different property from the web-object included in the object repository; and update the web-object included in the object repository with the at least one different property in the accessed web-object. Numerous other aspects are provided.

Abstract: According to some embodiments, a system and method comprising a plurality of automation tools; and a shared automation module, coupled to the plurality of automation tools including: a computer processor; a computer memory, coupled to the computer processor, storing instructions that, when executed by the computer processor cause the shared automation module to: receive a selection of a first automation tool of the plurality of automation tools; receive a selection of a second automation tool of the plurality of automation tools; execute the first automation tool to generate a first automation tool output; and execute the second automation tool using the stored first automation tool output to generate a second automation tool output. Numerous other aspects are provided.

Abstract: A device extracts data files from different data sources, and processes the data files to generate data from the data files. The device trains a first artificial intelligence model and a second artificial intelligence model based on historical data parameters, wherein the historical data parameters are associated with historical data previously processed by the device. The device utilizes the trained first artificial intelligence model and the data to generate data parameters for the data, and utilizes the trained second artificial intelligence model, the data, and the data parameters to generate a data structure configuration. The device generates a data structure for the data based on the data parameters and the data structure configuration, stores the data in the data structure, and provides a user interface for accessing the data structure.

Abstract: Data is received that encapsulate a test case document including a series of test instructions written in natural language for testing a software application. The software application includes a plurality of graphical user interface views (e.g., views in a web browser, etc.). Thereafter, the test case document is parsed using at least one natural language processing algorithm. This parsing includes tagging instructions in the test case document with one of a plurality of pre-defined sequence labels. Subsequently, a test automate is generated using at least one machine learning model trained using historical test case documents, corresponding automates, and their successful executions and based on the tagged instructions in the test case document. The generated test automate includes one or more test scripts which, when executed, perform a testing sequence of the software application according to the series of test instructions. Related apparatus, systems, techniques and articles are also described.

Abstract: A device extracts data files from different data sources, and processes the data files to generate data from the data files. The device trains a first artificial intelligence model and a second artificial intelligence model based on historical data parameters, wherein the historical data parameters are associated with historical data previously processed by the device. The device utilizes the trained first artificial intelligence model and the data to generate data parameters for the data, and utilizes the trained second artificial intelligence model, the data, and the data parameters to generate a data structure configuration. The device generates a data structure for the data based on the data parameters and the data structure configuration, stores the data in the data structure, and provides a user interface for accessing the data structure.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: A method and apparatus for analyzing end user license agreements and taking action based on the analysis outcome. In one embodiment, the method includes sensing initialization of an installable software program installation, extracting a EULA from the installable during installation, pausing presentation of the EULA to a user, providing a database of suspect terms, analyzing whether a suspect term exists in the EULA, and presenting at least one of the EULA or a suspect term to the user.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: Systems and methods for detecting malicious processes in a non-signature based manner are disclosed. The system and method may include gathering features of processes running on an electronic device, applying a set of rules to the features, and applying a statistical analysis to the results of the rules application to determine whether a process should be classified into one or more of a plurality of process categories.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: A method for providing malware cleaning includes detecting potential malware on a first device connected to a network. A request including information to allow a second device connected to the network to determine an appropriate cleaning response is sent from the first device to the second device over the network. Upon receiving the request, the second device attempts to identify an appropriate cleaning response and, if a response is identified, sends the cleaning response over the network to the first device. The cleaning response is usable by the first device to address the detected potential malware.

Abstract: A file stored in a first portion of a computer memory of a computer is determined to be a malicious file. A duplicate of the file is stored in a quarantine area in the computer memory, the quarantine area being in a second portion of the computer memory that is different from the first portion of the computer memory. One or more protection processes are performed on the file. The determination that the file is a malicious file is determined to be a false positive and the file is restored, during a boot sequence, to a state prior to the one or more protection processes being performed on the file.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for rolling back protection processes. In one aspect, a method includes determining that a file is a malicious file, storing a duplicate of the file in a quarantine area, performing one or more protection processes on the file, if the determination that the file is a malicious file is a false positive determination, restoring the file by a pre-boot rollback process to a state prior to the one or more protection processes performed on the file, and booting the computer with the restored file, and if the determination that the file is a malicious file is not a false positive determination, not restoring the file to a state prior to the one or more protection processes performed on the file, and booting the computer.

Abstract: A method for providing malware cleaning includes detecting potential malware on a first device connected to a network. A request including information to allow a second device connected to the network to determine an appropriate cleaning response is sent from the first device to the second device over the network. Upon receiving the request, the second device attempts to identify an appropriate cleaning response and, if a response is identified, sends the cleaning response over the network to the first device. The cleaning response is usable by the first device to address the detected potential malware.

Abstract: A system, method, and computer program product are provided for segmenting a database based, at least in part, on a prevalence associated with known objects included in the database. In operation, a database including a plurality of known objects is identified. Additionally, the database is segmented into a plurality of segments. Furthermore, each of the plurality of known objects are assigned to one of the plurality of segments, based at least in part on a prevalence associated with each of the plurality of known objects.

Abstract: Systems and methods for detecting malicious processes in a non-signature based manner are disclosed. The system and method may include gathering features of processes running on an electronic device, applying a set of rules to the features, and applying a statistical analysis to the results of the rules application to determine whether a process should be classified into one or more of a plurality of process categories.