Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. in response to a success rate of an attribute-value pair being below a predetermined threshold, a subset of testcases that use the attribute-value pair is identified. Further, sets of code paths for the subset of testcases are identified, each set of code path respectively corresponding to a testcase from the subset of testcases. Further, an intersection of the sets of code paths is determined. Code paths of the SUT that are in the intersection, are highlighted to represent a soft failure with the SUT.

Abstract: A computer-implemented method for test case priority selection includes initiating execution of a set of test cases associated with a system under test. Further, the set of test cases are executed according to a first order of execution, and during the execution, at runtime a failing test case, from the set of test cases is determined. Further, at runtime, from the set of test cases, a subset of test cases that are not yet executed is identified. Further, at runtime, a priority value is assigned to each test case from the subset of test cases, the priority value based on a similarity of the test case with the failing test case. Further, at runtime, the set of test cases is reorganized to execute the subset of test cases in a second order of execution based on priority values that are assigned.

Abstract: A method for testing a system under test (SUT) in an active environment to identify cause of a soft failure includes recording a first difference vector by executing a set of test cases on a baseline system and monitoring performance parameters of the baseline system before and after executing the test cases. Each performance record represents differences in the performance parameters of the baseline system from before and after the execution of a corresponding test case. The method further includes, similarly, recording a second difference vector by executing the test cases on the SUT and monitoring performance parameters of the SUT before and after executing the test cases. The method further includes identifying an outlier performance record from the second difference vector by comparing the difference vectors and further, determining a root cause of the soft failure by analyzing a test case corresponding to the outlier.

Abstract: A method for testing a system under test (SUT) in an active environment includes receiving, by a testing system, a code path of the SUT that causes a soft failure in the active environment. The soft failure occurs in the active environment during execution of the SUT based at least on a parameter of the active environment. The method further includes generating, by the testing system, multiple tests for testing the SUT, the tests generated based on a coverage model of the SUT, wherein the coverage model uses several attributes. The method further includes selecting, by the testing system, from the generated tests, a set of tests that are associated with the code path. The method further includes executing, by the testing system, only the set of tests that are selected on the SUT to analyze a cause of the soft failure.

Abstract: A method for testing a system under test (SUT) in an active environment includes executing, by the testing system, on the SUT, a test from a set of tests. The method further includes, monitoring a first execution time to complete the test on the SUT in the active environment. Based on the first execution time being different than a second execution time of the test, marking, by the testing system, a code path associated with the test. The second execution time is a duration to complete execution of the test on the SUT in a clean execution environment. The method further includes communicating, by the testing system, the code path for analyzing a soft failure of the SUT in the active environment, wherein the soft failure occurs in the active environment during execution of the SUT based at least on a parameter of the active environment.

Abstract: A method for testing a system under test (SUT) in an active environment to identify cause of a soft failure includes recording a first difference vector by executing a set of test cases on a baseline system and monitoring performance parameters of the baseline system before and after executing the test cases. Each performance record represents differences in the performance parameters of the baseline system from before and after the execution of a corresponding test case. The method further includes, similarly, recording a second difference vector by executing the test cases on the SUT and monitoring performance parameters of the SUT before and after executing the test cases. The method further includes identifying an outlier performance record from the second difference vector by comparing the difference vectors and further, determining a root cause of the soft failure by analyzing a test case corresponding to the outlier.

Abstract: A method for identifying optimal tests includes defining functional coverage by a test suite based on a functional coverage model of a System Under Test (SUT). The test suite includes a plurality of tests. The functional coverage model includes a plurality of attributes, each attribute having a set of possible values. The functional coverage model defines possible combinations of values of the attributes as covered by the test suite. A subset of the possible combinations of values is determined. A subset of the plurality of tests is selected that is operative to cover the determined subset of the possible combinations of values. A plurality of trees is generated to represent the selected subset of tests. Complexity of the generated trees is analyzed based on user-specified criteria. An optimal tree is selected from the subset of the plurality of trees to achieve the objective of the test suite.

Abstract: A method for testing a system under test (SUT) in an active environment to identify cause of a soft failure includes recording a first difference vector by executing a set of test cases on a baseline system and monitoring performance parameters of the baseline system before and after executing the test cases. Each performance record represents differences in the performance parameters of the baseline system from before and after the execution of a corresponding test case. The method further includes, similarly, recording a second difference vector by executing the test cases on the SUT and monitoring performance parameters of the SUT before and after executing the test cases. The method further includes identifying an outlier performance record from the second difference vector by comparing the difference vectors and further, determining a root cause of the soft failure by analyzing a test case corresponding to the outlier.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. The method further includes outputting, to a user, in response to the success rate SAV of the attribute-value pair being below a predetermined threshold, an identification of one or more testcases that use the attribute-value pair, wherein the one or more testcases are to be used for diagnosing a soft failure associated with the SUT.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. The method further includes outputting, to a user, in response to the success rate SAV of the attribute-value pair being below a predetermined threshold, an identification of one or more testcases that use the attribute-value pair, wherein the one or more testcases are to be used for diagnosing a soft failure associated with the SUT.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. in response to a success rate of an attribute-value pair being below a predetermined threshold, a subset of testcases that use the attribute-value pair is identified. Further, sets of code paths for the subset of testcases are identified, each set of code path respectively corresponding to a testcase from the subset of testcases. Further, an intersection of the sets of code paths is determined. Code paths of the SUT that are in the intersection, are highlighted to represent a soft failure with the SUT.

Abstract: According to one or more embodiments of the present invention, a computer-implemented method for machine code analysis includes executing a set of test cases associated with a software product. The method further includes determining a failing test case, from the set of test cases. The method further includes identifying a portion of a machine code of the software product, the portion of the machine code corresponding to the failing test case. The method further includes analyzing the portion of the machine code to identify a pattern of machine code causing the failing test case to fail. The method further includes searching the machine code of the software product to find the identified pattern of machine code.

Abstract: Systems, methods, and computer-readable media for identifying a champion test case that provides an increased likelihood of exposing a fault and expanding a set of test cases to include the champion test case are described. The fault may occur in a System Under Test (SUT), which may be a hardware system or a software system. A weight may be assigned to a champion test case that indicates the relative strength of the test case in detecting faults. The weight may be used to influence the selection of other test cases.

Abstract: Systems, methods, and computer-readable media are described for utilizing breakpoint value-based fingerprints of failing regression test cases to determine specific components of a System Under Test (SUT) that are causing a fault such as specific lines of source code. A failing test case from a regression run is selected and fault localization and inverse combinatorics techniques are employed to generate a set of failing test cases around the selected failing test case. A set of test fingerprints corresponding to the set of failing test cases is compared to a set of test fingerprints corresponding to a set of passing test cases to determine breakpoints that are indicated as being encountered during execution of at least one failing test case and that are not encountered during execution of any of the passing test cases. Specific lines of source code that correspond to these breakpoints are then identified as causing the fault.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. in response to a success rate of an attribute-value pair being below a predetermined threshold, a subset of testcases that use the attribute-value pair is identified. Further, sets of code paths for the subset of testcases are identified, each set of code path respectively corresponding to a testcase from the subset of testcases. Further, an intersection of the sets of code paths is determined. Code paths of the SUT that are in the intersection, are highlighted to represent a soft failure with the SUT.

Abstract: A method for testing a system under test (SUT) in an active environment includes generating, by a testing system, a set of tests for testing the SUT, the tests generated based on a coverage model of the SUT, wherein the coverage model uses several attributes. The method further includes creating, by the testing system, a minimal set of tests from the generated tests by selecting tests for a disjoint set of attributes from the several attributes of the coverage model. The method further includes executing, by the testing system, the minimal set of tests on the SUT for analyzing a soft failure of the SUT in the active environment, wherein the soft failure occurs in the active environment during execution of the SUT based at least in part on a parameter of the active environment.

Abstract: A method for testing a system under test (SUT) in an active environment includes generating, by a testing system, several tests for testing the SUT. The tests are generated based on a coverage model of the SUT, which includes multiple attributes. The method further includes creating, by the testing system, a minimal set of tests from the tests by selecting tests that do not exceed a predetermined performance threshold. The method further includes executing, by the testing system, the minimal set of tests on the SUT for analyzing a soft failure of the SUT in the active environment. The soft failure occurs in the active environment during execution of the SUT based at least in part on a performance parameter of the active environment.

Abstract: A system and related method comprise using a processor for executing a plurality of tests associated with a covering array of a test framework of the software test system on a first version of a system under test (SUT). For each of the plurality of tests, on a current test, the method comprises determining a current success rate value (SRV) for the current test that represents a success rate of the current test for the first version of the SUT. The method further comprises combining the current SRV of the first version of the SUT and current SRVs of the current test for prior versions of the SUT into a current test eigenvector associated with the current test. The method further comprises converting the current test eigenvector into a first eigenvalue that represents a health, accuracy, and quality of the first version of the SUT.

Abstract: A method for assigning test case priority includes analyzing, based on a set of test vectors, one or more test cases from a set of test cases on source code to determine a particular combination of attribute values associated with the one or more analyzed test cases. The method further includes generating a priority value for each attribute in the determined particular combination of attribute values. A priority value for each of the analyzed one or more test cases is generated based on the generated priority values of the particular combination of attribute values associated with the analyzed one or more test cases.

Abstract: Inputs to a system under test (SUT) are modeled as a collection of attribute-value pairs. A set of testcases is executed using an initial set of test vectors that provides complete n-wise coverage of the attribute-value pairs. For each execution of the testcases, for each attribute-value pair, a non-binary success rate (SAV) is computed based on the binary execution results. An attribute is selected in response to a set of success rates corresponding to a set of attribute-value pairs that includes said attribute are all below a predetermined threshold. The set of testcases is executed using another set of test vectors using additional values for the selected attribute. For each execution of the set of testcases, for each attribute-value pair, a second non-binary success rate (SAV?) is recorded. If the predetermined threshold is now satisfied, a user is notified of the additional values for the attribute that were detected.