Methods and systems for verifying control coupling analysis in testing of software code include: selecting a source file to be tested, the source file having source code, the source file selected from a system set including a plurality of source files from one or more nodes in a system; identifying one or more control couples within the source file by performing static analysis on the source code of the source file; defining one or more test runs of the software code, the one or more test runs including one or more of the identified control couples, and the one or more test runs using dynamic analysis; executing the one or more defined test runs; identifying control coupling coverage of the source file based on the dynamic analysis; and generating a control coupling report based on the identified control coupling coverage of the source file.

A method for detecting a defect may include extracting, from application code and using a framework support specification corresponding to a framework, a framework interaction between the application code and the framework. The framework interaction specifies an object used by the application code and managed by the framework. The method may further include performing, using the framework interaction, a dynamic analysis of the application code to obtain a heap snapshot, performing, using the heap snapshot and the framework interaction, a static analysis of the application code, and detecting, by the static analysis, the defect.

This application relates to a computing system and method for an automated program error repair. In one aspect, the computing system includes a storage, a preprocessing processor, and an automated error repair processor. The storage stores a program code. The preprocessing processor acquires the program code from the storage and preprocesses the program code. Preprocessing includes tokenizing the program code with tokens, converting the tokens into vectors, and adding location information for the tokens. The automated error repair processor receives the preprocessed program code as an input from the preprocessing processor, detects an error in the preprocessed program code, corrects the detected error, and outputs the error-corrected program code. Detecting and correcting the error are performed based on a deep learning result and the location information for the tokens.

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.

A mixed mode programming method permitting users to program with graphical coding blocks and textual code within the same programming tool. The mixed mode preserves the advantages of graphical block programming while introducing textual coding as needed for instructional reasons and/or for functional reasons. Converting a graphical code block or group of blocks to a textual block lets the user see a portion of the textual code in the context of a larger program. Within one programming tool the mixed mode method allows users to learn programming and build purely graphical blocks; then transition into mixed graphical and textual code and ultimately lead to their ability to program in purely textual code. The mixed mode further allows users to program using any combination of drag-and-drop graphical blocks and typed textual code in various forms.

A method for a cyber threat defense system is provided. The method comprises receiving a first abnormal behavior pattern where the first abnormal behavior pattern represents behavior on a first network deviating from a normal benign behavior of that network; and receiving a second abnormal behavior pattern where the second abnormal behavior pattern representing either behavior on the first network or on a second network deviating from a normal benign behavior of that network. The method further comprises comparing the first and second abnormal behavior patterns to determine a similarity score between the first and second abnormal behavior patterns and determining, based on the comparison, that the first abnormal behavior pattern likely corresponds to malicious behavior when the similarity score is above a threshold. A corresponding non-transitory computer readable medium is also provided.

Systems and methods for performing a complete assessment of a disconnected environment to determine if any prerequisite components (dependencies) necessary for the installation of cloud infrastructure are missing from the disconnected environment and generating a report based on the assessment are provided. An offline bundle having an assessment playbook may be imported into the disconnected environment. The assessment playbook may determine whether the disconnected environment includes each of a set of prerequisite components of the cloud infrastructure and generate a report indicating one or more of the set of prerequisite components that are missing from the disconnected environment. An automation playbook may be generated based on the report, wherein the automation playbook installs each of the one or more of the set of prerequisite components that are missing from the disconnected environment.

A method for determining an efficacy of an application includes identifying a plurality of application components deliverable within the application, identifying a component from the plurality of application components to execute to perform the step based upon a profile; providing the particular component; detecting an interaction with the provided component; and determining an efficacy of the application. A system for determining an efficacy of an application includes a processor and a memory storing computer-executable instructions that, when executed by the one or more processors, cause the computing system to identify a plurality of application components deliverable within the application, identify, from the plurality of application components, a component to execute to perform the step, based upon a profile; provide the particular component; detect an interaction with the provided component; and determine an efficacy of the application, based at least in part upon the detected interaction.

The present disclosure relates to a non-transitory computer-readable recording medium storing an analysis program that causes a computer to execute a process. The process includes sampling an instruction address of one of instructions included in a program during execution of the program, identifying a first function that includes the sampled instruction address in an address range, rewriting mark information associated with the identified first function, identifying first information corresponding to the instruction address of the first function among a plurality of first information based on the rewritten mark information, identifying second information corresponding to the instruction address of the first function among a plurality of second information based on the rewritten mark information, storing the first information and the second information in a memory, and analyzing performance of the program based on the first information and the second information stored in the memory.

Techniques for performing machine learning-based program analysis using synthetically generated labeled data are described. A method of performing machine learning-based program analysis using synthetically generated labeled data may include receiving a request to perform program analysis on code, determining a first portion of the code associated with a first error type, sending the first portion of the code to an endpoint of a machine learning service associated with an error detection model to detect the first error type, the error detection model trained using synthetically generated labeled data, and receiving inference results from the error detection model identifying one or more errors of the first error type in the first portion of the code.