A system and method in accordance with examples may include systems and methods for detecting failure of microservice applications in communication with an orchestration layer of a microservice-architecture. The system may include memory and an orchestration layer including one or more processors coupled to the memory. The one or more processors may be configured to connect the orchestration layer to a plurality of microservice applications that are each associated with a respective dataset. The one or more processors may be configured to validate, responsive to the connection of each of the microservice applications, the microservice applications by performing a first test and a second test. The one or more processors may be configured to deploy, responsive to the validation of the microservice applications, the microservice applications to execute a plurality of workflow actions.

According to an aspect of an embodiment, a method may identifying a fault location of a fault in a tested software program using a test suite and obtaining a repair candidate for the fault. In addition, the method may include obtaining a repair code pattern of the repair candidate and determining a number of occurrences of the repair code pattern in existing code of multiple existing software programs. Moreover, the method may include prioritizing the repair candidate as a repair of the tested software program based on the number of occurrences of the repair code pattern. The method may also include performing repair operations on the tested software program according to the prioritizing of the repair candidate.

Described herein is a system for automatically reconfiguring a test environment. As described above, performance testing can be time-consuming and error-prone resulting in the use of unnecessary computational resources. The system may use machine-learning to determine whether the test environment, test data, and/or test script is to be reconfigured to optimize the performance test. The system may iteratively reconfigure the test environment, test data, and/or test script, and re-execute the performance test, until an optimal performance test of an application is executed based on a specified performance requirement.

Techniques are provided for implementing a unified object format. The unified object format is used to format data in a performance tier (e.g., infrequently accessed data, snapshot data, etc.) into objects that are stored into an object store for low cost, scalable, long term storage compared to storage of the performance tier. With the unified object format, compression of the data may be retained when the data is stored as the objects into the object store. Additional compression may also be provided for the data in the objects. The unified object format includes slot header metadata used to track the location of the data within the object notwithstanding the data being compressed and/or stored at non-fixed boundaries. The slot header metadata may be cached at the performance tier for improved read performance and may be repaired by a repair subsystem (a slot header repair subsystem).

Methods, media, and systems for detecting anomalous program executions are provided. In some embodiments, methods for detecting anomalous program executions are provided, comprising: executing at least a part of a program in an emulator; comparing a function call made in the emulator to a model of function calls for the at least a part of the program; and identifying the function call as anomalous based on the comparison. In some embodiments, methods for detecting anomalous program executions are provided, comprising: modifying a program to include indicators of program-level function calls being made during execution of the program; comparing at least one of the indicators of program-level function calls made in the emulator to a model of function calls for the at least a part of the program; and identifying a function call corresponding to the at least one of the indicators as anomalous based on the comparison.

Methods, media, and systems for detecting anomalous program executions are provided. In some embodiments, methods for detecting anomalous program executions are provided, comprising: executing at least a part of a program in an emulator; comparing a function call made in the emulator to a model of function calls for the at least a part of the program; and identifying the function call as anomalous based on the comparison. In some embodiments, methods for detecting anomalous program executions are provided, comprising: modifying a program to include indicators of program-level function calls being made during execution of the program; comparing at least one of the indicators of program-level function calls made in the emulator to a model of function calls for the at least a part of the program; and identifying a function call corresponding to the at least one of the indicators as anomalous based on the comparison.

In an approach to cleanup of unpredictable test results, one or more computer processors generate a data area associated with a first test instruction in a test stream. The one or more computer processors determine whether the generated data area overlaps with an unpredictable data area. In response to determining the generated data area overlaps with an unpredictable data area, the one or more computer processors determine a second test instruction associated with the overlapped unpredictable data area, where the second test instruction precedes the first test instruction in the test stream. The one or more computer processors select a location in the test stream between the first test instruction and the second test instruction. The one or more computer processors insert one or more pre-requisite instructions in the selected location, where the one or more pre-requisite instructions load the overlapped unpredictable data area with pre-defined data.

In an approach to cleanup of unpredictable test results, one or more computer processors generate a data area associated with a first test instruction in a test stream. The one or more computer processors determine whether the generated data area overlaps with an unpredictable data area. In response to determining the generated data area overlaps with an unpredictable data area, the one or more computer processors determine a second test instruction associated with the overlapped unpredictable data area, where the second test instruction precedes the first test instruction in the test stream. The one or more computer processors select a location in the test stream between the first test instruction and the second test instruction. The one or more computer processors insert one or more pre-requisite instructions in the selected location, where the one or more pre-requisite instructions load the overlapped unpredictable data area with pre-defined data.

Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

According to an aspect of an embodiment, a method may identifying a fault location of a fault in a tested software program using a test suite and obtaining a repair candidate for the fault. In addition, the method may include obtaining a repair code pattern of the repair candidate and determining a number of occurrences of the repair code pattern in existing code of multiple existing software programs. Moreover, the method may include prioritizing the repair candidate as a repair of the tested software program based on the number of occurrences of the repair code pattern. The method may also include performing repair operations on the tested software program according to the prioritizing of the repair candidate.