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.

System and method of compiling a program having a mixture of host code and device code to enable Profile Guided Optimization (PGO) for device code execution. An exemplary integrated compiler can compile source code programmed to be executed by a host processor (e.g., CPU) and a co-processor (e.g., a GPU) concurrently. The compilation can generate an instrumented executable code which includes: profile instrumentation counters for the device functions; and instructions for the host processor to allocate and initialize device memory for the counters and to retrieve collected profile information from the device memory to generate instrumentation output. The output is fed back to the compiler for compiling the source code a second time to generate optimized executable code for the device functions defined in the source code.

Building a first layer model of a three-layer model based on attributes that are sensitive features is provided. A first dimension reduction of sensitive features removes each sensitive feature having an indicator that it is present in a patch and does not contribute to one or more of three probabilities. A second dimension reduction of insensitive features is performed using vectorizing and using one-hot encoding. The remaining insensitive features are main features. One or more second layer models of the three-layer model is built based on the main features. The third layer model is built based on a verification dataset and the first layer model. Regression test coverage is recommended based on prediction result of the third layer model, and wherein regression tests are selected. The training dataset is updated based on probability calculations of the first layer model.

A system and method for determining a relative importance of a selected test in a plurality of tests, comprising a computational device for receiving one or more characteristics relating to an importance of the code, an importance of each of the plurality of tests, or both; and for determining the relative importance of the selected test according to said characteristics.

Program analysis is provided. An intermediate representation of a program is generated. A set of structured inputs is provided to the program. The set of structured inputs are derived from the intermediate representation. The program is executed using the set of structured inputs. A set of action steps is performed in response to observing a violation of a policy during execution of the program using the structured inputs.

A code deployment system deploys code to a set of application systems that execute the application, which may be across several tiers of systems that service requests related to the application. At each system, the application executes and is analyzed during execution to determine active code that is loaded by the application during execution, which may include dynamically-generated code. The active code is then analyzed using static analysis to determine security vulnerabilities and errors in the code that was loaded and operated at each application tier. The active code may also be associated with a specific use case or set of inputs that were applied to the application during the monitoring.

Systems and methods are disclosed herein for improving data migration operations including testing and setup of computing environments. In one example, the method may include receiving data for one or more application programming interfaces (APIs). The method may further include generating one or more tests to test the one or more APIs in a first computing environment, testing the APIs, storing the results in a database, and performing a change data capture operation. The method may further include augmenting the one or more tests with the CDC data to generate an updated test. The method may further include testing, using the updated test, a second set of the one or more APIs and comparing the test results. The method may also include outputting a confidence score indicating a correlation between the first environment and the second environment.

A system can identify a file comprising computer-executable instructions, wherein the file has been modified since the file was last transformed into a computer-executable program on which a group of tests was performed. The system can, for respective tests, determine respective line coverage ratios, respective function coverage ratios, and respective branch coverage ratios. The system can select an updated group of tests from the group of tests based on the respective line ratios, the respective function ratios, and the respective branch ratios, the updated group of tests comprising a subgroup of the group of tests. The system can create an updated computer-executable program from the file. The system can test the updated computer-executable program with the updated group of tests.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for executing efficient and reliable techniques for testing application programming interfaces (APIs) by utilizing at least one of API endpoint modeling data entities and workflow design user interfaces that are generated based at least in part on API endpoint modeling data entities.

The computer-implemented method for producing a test code for automated testing a software comprises providing a high-level test model for modelling a set of requirements for the software to be tested; generating a plurality of high-level test cases on the basis of the high-level test model; providing a human tester with the software to be tested; executing the software; prompting the human tester to execute the high-level test cases by the software under test with using input values arbitrarily selected from an available set of input values of the software in accordance with the high-level test model; producing a plurality of low-level test models for the high-level test cases by the human tester; and generating a computer-executable test program code for the low-level test models.