Intelligent case execution priority assessment mechanism

Abstract

The embodiment of the invention relates to an intelligent use case execution priority evaluation mechanism. Methods, systems, and non-transitory processor-readable storage media for a test selection system are provided herein. An example method includes selecting, by a test selection system, a regression test case from a plurality of regression test cases in a software test lifecycle system. The test selection system calculates a fault detection score for the regression test case based on a fault detection attenuation rate score associated with the regression test case. The test selection system selects a regression test case for execution on the test system based on the fault detection score, and executes the regression test case on the test system.

Description

Technical Field

[0001] This field generally concerns the optimal selection of regression test cases in information processing systems. Background Art

[0002] System testing is an important part of the quality engineering activities before the release of information processing systems. There are two important parts in the system testing phase: new feature testing and regression test cases. For complex information processing systems, such as storage products, there may be thousands of system regression test cases. Summary of the invention

[0003] The illustrative embodiments provide techniques for implementing a test selection system in a storage system. For example, the illustrative embodiments select a regression test case from a plurality of regression test cases in a software testing lifecycle system through a test selection system. The test selection system calculates a fault detection score for the regression test case based on a fault detection decay rate score associated with the regression test case. The test selection system selects the regression test case to be executed on the test system based on the fault detection score, and executes the regression test case on the test system. Other types of processing devices may be used in other embodiments. These and other illustrative embodiments include, but are not limited to, apparatus, systems, methods, and processor-readable storage media. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 An information handling system is shown that includes a test selection system in an illustrative embodiment.

[0005] Figure 2 A flow chart of a process for testing a selection system in an illustrative embodiment is shown.

[0006] Figure 3 and Figure 4 An example of a processing platform that may be used to implement at least a portion of an embodiment of a test selection system is shown. DETAILED DESCRIPTION

[0007] Illustrative embodiments will be described herein with reference to an exemplary computer network and associated computers, servers, network devices, or other types of processing devices. However, it should be understood that these and other embodiments are not limited to use with the specific illustrative network and device configurations shown. Therefore, the term "computer network" as used herein is intended to be interpreted broadly to include, for example, any system including multiple networked processing devices.

[0008] A technique for implementing a test selection system is described below, which can be used to provide regression test case selection and optimization, etc. by selecting a regression test case from a plurality of regression test cases in a software testing lifecycle system by the test selection system. The test selection system calculates a fault detection score of the regression test case based on a fault detection decay rate score associated with the regression test case. The test selection system selects the regression test case to execute on a test system based on the fault detection score, and executes the regression test case on the test system.

[0009] The system testing phase of pre-release quality engineering activities for an information processing system may include thousands of regression test cases. Regression test cases must be executed to ensure that newly implemented features do not affect existing functionality. However, resources to execute these regression test cases may be limited. Therefore, when executing a test set of regression test cases, it is critical to execute the most valuable regression test cases first (i.e., those regression test cases that are considered more important than other regression test cases). Executing the most valuable regression test cases early in the testing cycle results in the detection of more critical issues earlier in the testing phase.

[0010] Conventional techniques only consider the number of detected faults linked to regression test cases (and the priority associated with the detected faults) without considering how much time has passed since those faults were initially detected by the regression test cases. Software products typically have many updates and modifications over time. Critical "bugs" detected in older versions are typically no longer a problem in newer versions because the problem is resolved once the detected critical "bug" is detected and fixed in the new product architecture. Conventional techniques do not prioritize newer detected faults over older detected problems. Conventional techniques do not provide a more optimized regression test case execution schedule by balancing the extent to which regression test cases have recently detected faults and the priority associated with those detected faults to provide an optimized test schedule that maximizes the effectiveness and efficiency of regression test cases to achieve the overall goal of providing the most robust products to customers. Conventional techniques do not provide a method for selecting the best regression test cases to adequately provide regression test coverage of new features in new product versions, especially where resources are limited. A regression testing team may not have the bandwidth to rerun the full set of regression test cases for a new product release.

[0011] In contrast, in at least some implementations of the present technology described herein, regression test case selection is optimized by selecting, by a test selection system, a regression test case from a plurality of regression test cases in a software testing lifecycle system. The test selection system calculates a fault detection score for the regression test case based on a fault detection decay rate score associated with the regression test case. The test selection system selects the regression test case to execute on a test system based on the fault detection score, and executes the regression test case on the test system.

[0012] Therefore, an objective of the current technology is to provide a method and system for a test selection system that can evaluate the value of regression test cases, rank the regression test cases according to their corresponding values, and execute the regression test cases in order of value. Another objective is to consider the number of detected faults and the priorities associated with the detected faults linked to the regression test cases, while considering how much time has passed since the regression test cases originally detected those faults. Another objective is to make the value of recently detected faults higher than that of earlier detected problems. Yet another objective is to provide a more optimized regression test case execution schedule by balancing the degree to which regression test cases have recently detected faults and the priorities associated with those detected faults to provide an optimized test schedule, thereby maximizing the effectiveness and efficiency of the regression test cases to achieve the overall goal of providing the most robust products to customers.

[0013] In at least some implementations of the present technology described herein, use of a test selection system may provide one or more of the following advantages: provide a method and system for a test selection system that may assess the value of regression test cases, rank the regression test cases according to their respective values, and execute the regression test cases in order of value, taking into account the number of detected faults and the priorities associated with the detected faults linked to the regression test cases, taking into account how much time has passed since the regression test cases initially detected those faults, giving more recently detected faults a higher value than older detected issues, and providing a more optimized regression test case execution schedule by balancing how recently the regression test cases detected faults and the priorities associated with those detected faults to maximize the effectiveness and efficiency of the regression test cases to achieve the overall goal of providing the most robust products to customers.

[0014] Compared to conventional techniques, in at least some implementations of the current techniques described herein, regression test case selection is optimized by selecting, by a test selection system, a regression test case from a plurality of regression test cases in a software testing lifecycle system. The test selection system calculates a fault detection score for the regression test case based on a fault detection decay rate score associated with the regression test case. The test selection system selects the regression test case to execute on a test system based on the fault detection score, and executes the regression test case on the test system.

[0015] In an example embodiment of the present technology, a previous execution of the regression test case on the first system detected at least one fault on the first system.

[0016] In an example embodiment of the current technology, if the regression test case is associated with the new feature fault detection tag, the test selection system assigns the regression test case to a first priority plurality of regression test cases, and if the regression test case is not associated with the new feature fault detection tag, the test selection system assigns the regression test case to a second priority plurality of regression test cases.

[0017] In an example embodiment of the present technology, a test selection system determines a plurality of detection faults associated with a regression test case and calculates a fault detection score using the plurality of detection faults.

[0018] In an example embodiment of the present technology, a plurality of detection failures are associated with validity periods.

[0019] In an example embodiment of the present technology, a test selection system calculates a fault detection decay rate score for each of a plurality of detected faults.

[0020] In an example embodiment of the present technology, a test selection system calculates a fault detection decay rate score for at least one priority level associated with each of a plurality of detected faults.

[0021] In an example embodiment of the present technology, a test selection system calculates a fault detection decay rate score for each priority level associated with each detected fault of a plurality of detected faults.

[0022] In an example embodiment of the present technology, the test selection system uses Newton's law of cooling to calculate the fault detection decay rate score.

[0023] In an example embodiment of the present technology, a test selection system calculates a fault detection decay rate score using validity periods associated with a plurality of detected faults.

[0024] In an example embodiment of the present technology, a test selection system determines a fault detection time decay factor for each detected fault of a plurality of detected faults.

[0025] In an example embodiment of the present technology, a test selection system determines a sum of fault detection time decay factors for each of a plurality of detected faults to determine a fault detection score for a regression test case.

[0026] In an example embodiment of the present technology, a test selection system ranks the first priority plurality of regression test cases according to a corresponding fault detection score associated with each regression test case in the first priority plurality of regression test cases to determine an execution priority associated with each regression test case in the first priority plurality of regression test cases.

[0027] In an example embodiment of the present technology, the test selection system ranks the second priority plurality of regression test cases according to the respective fault detection scores associated with each regression test case in the second priority plurality of regression test cases to determine an execution priority associated with each regression test case in the second priority plurality of regression test cases.

[0028] In an example embodiment of the present technology, the test selection system executes the first priority plurality of regression test cases according to an execution priority associated with each regression test case in the first priority plurality of regression test cases, and upon completion of execution of the first priority plurality of regression test cases, executes the second priority plurality of regression test cases according to the execution priority associated with each regression test case in the second priority plurality of regression test cases.

[0029] In an example embodiment of the present technology, a first priority plurality of regression test cases is associated with a first validity period, and a second priority plurality of regression test cases is associated with a second validity period.

[0030] In an example embodiment of the present technology, the first validity period is different from the second validity period.

[0031] In an example embodiment of the present technology, an execution priority associated with each regression test case in a first priority plurality of regression test cases optimizes effective fault detection on a test system.

[0032] Figure 1 A computer network (also referred to herein as an information processing system) 100 is shown configured in accordance with an illustrative embodiment. The computer network 100 includes a software testing lifecycle system 101, a test selection system 105, and test systems 102-N. The software testing lifecycle system 101, the test selection system 105, and the test systems 102-N are coupled to a network 104, wherein the network 104 in this embodiment is assumed to represent a sub-network or other related portion of a larger computer network 100. Therefore, elements 100 and 104 are both referred to herein as examples of a "network," but the latter is not necessarily the same in this embodiment. Figure 1 Components of the former are assumed in the context of the embodiments. Also coupled to the network 104 is a test selection system 105 which may reside on a storage system. Such a storage system may include any of a variety of different types of storage, including network attached storage (NAS), storage area network (SAN), direct attached storage (DAS), and distributed DAS, as well as combinations of these and other storage types, including software defined storage.

[0033] Each of the test systems 102-N may include, for example, a server and / or portions of one or more server systems, as well as devices such as mobile phones, laptop computers, tablet computers, desktop computers, or other types of computing devices. Such devices are examples of what are more generally referred to herein as "processing devices." Some of these processing devices are also generally referred to herein as "computers."

[0034] In some embodiments, test system 102-N includes respective computers associated with a particular company, organization, or other enterprise. In addition, at least portions of computer network 100 may also be referred to herein as collectively constituting an "enterprise network." Many other operational scenarios involving various different types and arrangements of processing devices and networks are possible, as will be appreciated by those skilled in the art.

[0035] Furthermore, it should be understood that in this context and elsewhere herein, the term "user" is intended to be interpreted broadly to include, for example, a person, a hardware, software, or firmware entity, and various combinations of these entities.

[0036] Network 104 is assumed to comprise a portion of a global computer network such as the Internet, although other types of networks may be part of computer network 100, including a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or wired network, a cellular network, a wireless network such as a Wi-Fi or WiMAX network, or various portions or combinations of these and other types of networks. Thus, in some embodiments, computer network 100 comprises a combination of multiple different types of networks, each including a processing device configured to communicate using the Internet Protocol (IP) or other related communication protocols.

[0037] Also associated with the test selection system 105 are one or more input-output devices, which illustratively include a keyboard, a display, or any combination of other types of input-output devices. Such input-output devices may be used, for example, to support one or more user interfaces to the test selection system 105, as well as to support communication between the test selection system 105 and other related systems and devices not explicitly shown. For example, a dashboard may be provided for a user to view the execution progress of the test selection system 105. One or more input-output devices may also be associated with any of the test systems 102-N.

[0038] Additionally, assuming Figure 1 The test selection system 105 in the embodiment is implemented using at least one processing device. Each such processing device generally includes at least one processor and associated memory, and implements one or more functional modules for controlling certain features of the test selection system 105.

[0039] More specifically, the test selection system 105 in this embodiment may include a processor coupled to a memory and a network interface.

[0040] The processor illustratively includes a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other type of processing circuit, as well as portions or combinations of these circuit elements.

[0041] The memory illustratively includes any combination of random access memory (RAM), read only memory (ROM), or other types of memory. The memory and other memories disclosed herein may be viewed as examples of what is more generally referred to as a "processor-readable storage medium" that stores executable computer program code or other types of software programs.

[0042] One or more embodiments include an article of manufacture such as a computer-readable storage medium. Examples of articles of manufacture include, but are not limited to, storage devices such as storage disks, storage arrays, or integrated circuits containing memory, as well as various other types of computer program products. The term "article of manufacture" as used herein should be understood to exclude transient propagating signals. These and other references to "disc" mentioned herein are intended to refer generally to storage devices including solid-state drives (SSDs), and therefore should not be considered to be limited in any way to rotating magnetic media.

[0043] The network interface allows the test selection system 105 to communicate with the software testing lifecycle system 101 and the test systems 102 -N via the network 104 and includes, for example, one or more conventional transceivers.

[0044] The test selection system 105 may be implemented at least in part in the form of software stored in a memory and executed by a processor, and may reside in any processing device. The test selection system 105 may be a stand-alone plug-in that may be included within a processing device.

[0045] It should be understood that Figure 1 The specific set of elements of the test selection system 105 shown in the figure relating to the software testing lifecycle system 101 and the test systems 102-N of the computer network 100 are presented only as illustrative examples, and additional or alternative elements may be used in other embodiments. Therefore, another embodiment includes additional or alternative systems, devices, and other network entities, as well as different arrangements of modules and other components. For example, in at least one embodiment, one or more of the test selection systems in the test selection system 105 can be on the same processing platform and / or parts of the same processing platform.

[0046] References such as Figure 2 The flowchart of FIG. 1 describes an exemplary process of the test selection system 105 in the computer network 100 in more detail.

[0047] Figure 2 is a flow chart of a process for executing the test selection system 105 in an illustrative embodiment. It should be appreciated that this particular process is merely an example and that additional or alternative processes may be performed in other embodiments.

[0048] At 200, the test selection system 105 selects a regression test case from a plurality of regression test cases in a software testing lifecycle system. In an example embodiment, a previous execution of the regression test case on a first system detected at least one fault on the first system. In other words, the regression test case is associated with detecting a fault or "bug." In an example embodiment, the test selection system 105 selects each regression test case from the plurality of regression test cases individually to evaluate each regression test case to identify an optimal execution order of the regression test cases for each product version.

[0049] At 202, the test selection system 105 calculates a fault detection score for the regression test case based on the fault detection decay rate score associated with the regression test case. In an example embodiment, the plurality of regression test cases are filtered into two categories according to whether there is a "new feature" tag associated with the regression test case. In an example embodiment, if the regression test case is associated with the new feature fault detection tag, the test selection system 105 assigns the regression test case to a first priority plurality of regression test cases, and if the regression test case is not associated with the new feature fault detection tag, the regression test case is assigned to a second priority plurality of regression test cases. The first priority plurality of regression test cases are associated with new features, and the second priority plurality of regression test cases are associated with legacy features.

[0050] In an example embodiment, the test selection system 105 determines a plurality of detected faults associated with the regression test case and uses the plurality of detected faults to calculate a fault detection score. In an example embodiment, the plurality of detected faults are associated with an effective period. For example, the test selection system 105 collects detected faults associated with the regression test case detected within the effective period.

[0051] In an example embodiment, the test selection system 105 calculates a fault detection decay rate score for each of the plurality of detected faults. In an example embodiment, to assess the value of detecting a fault for a regression test case, the test selection system 105 determines the decay rate of detecting the fault over time. In other words, when the regression test case initially detects a fault, the value of detecting the fault is high, but decreases over time because the detected fault may be fixed in a subsequent release. In an example embodiment, the test selection system 105 calculates the fault detection decay rate score using Newton's law of cooling.

[0052] Newton's law of cooling is an exponential decay model that determines the temperature of an object being cooled or warmed based on the temperature of the surrounding environment and the time that has passed since the object entered the environment. Detection failures can also be associated with such a temperature, where a recently detected detection failure has a higher importance (or value) to the regression test case than a detection failure that was detected earlier than the most recently detected failure.

[0053] In the example embodiment, “T” represents the temperature at which a fault is detected, where the temperature belongs to the set [1,100], T t0 represents the initial temperature for fault detection, and T t represents the temperature of the detected fault at time t. The fault detection decay rate score k is determined based on the historical records associated with the regression test case and the associated detected faults. Therefore, the fault detection decay rate score is determined using the following formula:

[0054]

[0055] In an exemplary embodiment, the test selection system 105 uses the validity periods associated with the plurality of detected faults to calculate the fault detection decay rate score. For example, if the validity period is 2 years, the temperature of the detected fault will drop to 1 after 2 years.

[0056] In an example embodiment, the detected faults have a priority attribute that reflects the importance of the detected faults. For example, P0 indicates a severe detected fault, P1 indicates an important detected fault, and P2 indicates a low impact detected fault. In an example embodiment, the test selection system 105 calculates a fault detection decay rate score for at least one priority associated with each of the plurality of detected faults. In an example embodiment, the temperatures of the initial detected faults generated 2 years ago are {100, 85, 70} corresponding to the respective priorities {P0, P1, P2} associated with those detected faults. The current temperatures T of those detected faults are now = 1. Therefore, the fault detection attenuation rate score k of the detected fault with priority P0 is:

[0057]

[0058]

[0059] k P0 =0.1919

[0060] In an example embodiment, the test selection system 105 calculates a fault detection decay rate score for each priority level associated with each of the plurality of detected faults. Therefore, the fault detection decay rate score k for the detected fault with priority level P1 is k P1 =0.1851, and the decay rate k of the detection fault with priority P2 is k P2 =0.1770.

[0061] In an example embodiment, the test selection system 105 determines a fault detection time decay factor λ(t, Pi) for each of the plurality of detected faults. For example, the fault detection time decay factor may be determined as follows:

[0062]

[0063] In an example embodiment, the test selection system 105 determines the sum of the fault detection time decay factors for each of the plurality of detected faults to determine a fault detection score for the regression test case. The fault detection score may be determined as follows:

[0064]

[0065] Thus, a fault detection decay rate score is determined for each detected fault associated with, for example, regression test case j, and the fault detection decay rate score is used to determine a fault detection time decay factor. The sum of all fault detection time decay factors associated with regression test case j yields the fault detection score for regression test case j. In an example embodiment, a high fault detection decay rate score indicates that the regression test case is associated with a recently detected fault that is considered a "high quality error."

[0066] At 204, the test selection system 105 selects regression test cases to execute on the test system based on the fault detection scores. In an example embodiment, the test selection system 105 determines all detected faults (within a validity period) associated with each regression test case in the first priority plurality of regression test cases, and calculates a fault detection score for each regression test case. In an example embodiment, the test selection system 105 ranks the first priority plurality of regression test cases according to the corresponding fault detection scores associated with each regression test case in the first priority plurality of regression test cases (e.g., from highest to lowest) to determine an execution priority associated with each regression test case in the first priority plurality of regression test cases. The execution priority results in executing more valuable regression test cases before executing less valuable regression test cases. The test selection system 105 outputs an execution order of the first priority plurality of regression test cases.

[0067] In an example embodiment, the test selection system 105 determines all detected faults (within a validity period) associated with each regression test case in the second priority plurality of regression test cases, and calculates a fault detection score for each regression test case. In an example embodiment, the test selection system 105 ranks the second priority plurality of regression test cases according to the corresponding fault detection scores associated with each regression test case in the second priority plurality of regression test cases to determine an execution priority associated with each regression test case in the second priority plurality of regression test cases. The test selection system 105 outputs an execution order of the second priority plurality of regression test cases. For example, the test selection system 105 may rank the regression test cases in the first priority plurality of regression test cases from the highest fault detection score to the lowest fault detection score, and then rank the regression test cases in the second priority plurality of regression test cases from the highest fault detection score to the lowest fault detection score.

[0068] At 206, the test selection system 105 executes the regression test cases on the test system. In an example embodiment, the test selection system 105 executes the first priority plurality of regression test cases according to the execution priority associated with each regression test case in the first priority plurality of regression test cases, and upon completion of execution of the first priority plurality of regression test cases, the test selection system 105 executes the second priority plurality of regression test cases according to the execution priority associated with each regression test case in the second priority plurality of regression test cases. In other words, the test selection system 105 ranks the first priority plurality of regression test cases according to the respective fault detection scores associated with the regression test cases, and executes the first priority regression test cases in the ranked order of the regression test cases. The execution priority associated with each regression test case in the first priority plurality of regression test cases optimizes effective fault detection on the test system, meaning that more valuable regression test cases that are likely to detect critical faults (i.e., "bugs") are executed first, and the remaining regression test cases in the first priority plurality of regression test cases are executed sequentially according to the value of the detected "bugs" associated with each regression test case in the first plurality of regression test cases. Once completed, the test selection system 105 executes the second priority multiple regression test cases according to the ranking order of the second priority multiple regression test cases. Therefore, regression test cases associated with higher value detection failures are given more importance during the evaluation and are executed earlier in the test cycle. This provides early notification of detection failures to software developers responsible for resolving detection failures, and provides developers with sufficient time to determine the root cause of the detection failure and resolve the detection failure. This helps avoid project release delays caused by critical bug fixes.

[0069] In an example embodiment, a first priority plurality of regression test cases is associated with a first validity period, and a second priority plurality of regression test cases is associated with a second validity period. In another example embodiment, the first validity period is different from the second validity period.

[0070] In an example embodiment, there are 185 regression test cases scheduled for execution. The test selection system 105 determines the order in which the regression test cases are executed to ensure that the most important regression test cases are executed earlier than other less important regression test cases. The test selection system 105 determines whether each of the 185 regression test cases {regression test case 1, ..., regression test case 185} is associated with a new feature fault detection tag, and classifies the regression test cases into a first priority plurality of regression test cases (if associated with the new feature fault detection tag) or a second priority plurality of regression test cases (if not associated with the new feature fault detection tag). For example, the first priority plurality of regression test cases includes 35 regression test cases {regression test case 1, ..., regression test case 185}. 1 , regression test cases 4 , regression test cases 7 ,…,Regression test cases j ,…,Regression test cases 184}, and the second priority multiple regression test cases include 150 cases {regression test cases 2 , regression test cases 3 , regression test cases 5 ,…,Regression test cases k ,…,Regression test cases 185 The test selection system 105 determines a plurality of detection faults for each of the 35 regression test cases in the first priority plurality of regression test cases within the validity period of 24 months, and then calculates a fault detection score {S 回归测试用例1 , S 回归测试用例4 , S 回归测试用例7 , …, S 回归测试用例j , …, S 回归测试用例184 The test selection system 105 determines the execution priority of the 35 first priority regression test cases by ranking the regression test cases. 回归测试用例1 , S 回归测试用例184 , S 回归测试用例j , …, S 回归测试用例7 , …, S 回归测试用例4 The test selection system 105 determines a plurality of detected faults for each of the 150 regression test cases in the second plurality of regression test cases within the validity period of 24 months, and then calculates a fault detection score {S 回归测试用例2 , S 回归测试用例3 , S 回归测试用例5 , …, S 回归测试用例k , …, S 回归测试用例185The test selection system 105 determines the execution priority of the 150 second priority regression test cases by ranking the regression test cases. 回归测试用例5 , S 回归测试用例3 , S 回归测试用例185 , …, S 回归测试用例k , …, S 回归测试用例2 Then, the test selection system 105 executes 185 regression test cases according to the execution priority {S 回归测试用例1 , S 回归测试用例184 , …, S 回归测试用例j , …, S 回归测试用例7 , S 回归测试用例4 , S 回归测试用例5 , S 回归测试用例3 , S 回归测试用例185 , …, S 回归测试用例k , …, S 回归测试用例2}.

[0071] Therefore, combined with Figure 2 The specific processing operations and other functions described in the flowcharts are presented only as illustrative examples and should not be interpreted as limiting the scope of the present disclosure in any way. For example, in other embodiments, the order of the process steps can be changed, or some steps can be performed simultaneously with each other rather than sequentially.

[0072] The above illustrative embodiments provide significant advantages over traditional methods. For example, some embodiments are configured to significantly improve the timely detection of system failures through regression test cases. Compared with traditional methods, these and other embodiments can effectively improve regression testing for each product version. For example, the embodiments disclosed herein provide a method and system for test selection system, which can evaluate the value of regression test cases, rank regression test cases according to their corresponding values, and execute regression test cases in order of value. The embodiments disclosed herein consider the number of detected failures and the priorities associated with the detected failures linked to the regression test cases, considering how much time has passed since the regression test cases initially detected these failures. The embodiments disclosed herein make the value of recently detected failures higher than older detected problems. The embodiments disclosed herein provide more optimized regression test case execution scheduling by balancing the recent degree of failures detected by regression test cases and the priorities associated with those detected failures to provide optimized test scheduling.

[0073] It should be understood that the specific advantages described above and elsewhere herein are associated with specific illustrative embodiments and need not exist in other embodiments. In addition, the specific types of information processing system features and functions shown in the drawings and described above are merely exemplary, and many other arrangements may be used in other embodiments.

[0074] As previously mentioned, at least part of the information handling system 100 can be implemented using one or more processing platforms. A given such processing platform includes at least one processing device, which includes a processor coupled to a memory. In some embodiments, the processor and memory include corresponding processors and memory elements of a virtual machine or container provided using one or more underlying physical machines. The term "processing device" used here is intended to be broadly interpreted as including a variety of different physical processors, arrangements of memory and other device components and virtual instances of these components. For example, in some embodiments, a "processing device" may include one or more virtual processors or execute across one or more virtual processors. Therefore, a processing device may be physical or virtual and may execute across one or more physical or virtual processors. It should also be noted that a given virtual device may be mapped to a portion of a physical device.

[0075] Some illustrative embodiments of a processing platform for implementing at least part of an information handling system include a cloud infrastructure that includes virtual machines implemented using a hypervisor running on a physical infrastructure. The cloud infrastructure also includes a collection of applications running on each virtual machine under the control of the hypervisor. Multiple hypervisors may also be used, each hypervisor providing a collection of virtual machines using at least one underlying physical machine. Different sets of virtual machines provided by one or more hypervisors may be used to configure multiple instances of various components of the system.

[0076] These and other types of cloud infrastructures can be used to provide what is also referred to herein as a multi-tenant environment. One or more system components or portions thereof are illustratively implemented for use by tenants of such a multi-tenant environment.

[0077] As previously mentioned, the cloud infrastructure disclosed herein may include a cloud-based system. In an illustrative embodiment, a virtual machine provided in such a system may be used to implement at least part of a computer system.

[0078] In some embodiments, the cloud infrastructure additionally or alternatively includes multiple containers implemented using a container host device. For example, as described in detail herein, a given container of the cloud infrastructure illustratively includes a Docker container or other type of Linux container (LXC). The container runs on a virtual machine in a multi-tenant environment, although other arrangements are possible. Containers are used to implement various different types of functions within the information processing system 100. For example, containers can be used to implement corresponding processing devices that provide computing and / or storage services for a cloud-based system. Similarly, containers can be used in conjunction with other virtualization infrastructures, such as virtual machines implemented using a hypervisor.

[0079] Now refer to Figure 3 and Figure 4Illustrative embodiments of the processing platforms are described in further detail. Although described in the context of information handling system 100, in other embodiments, these platforms may also be used to implement at least portions of other information handling systems.

[0080] Figure 3 An example processing platform is shown that includes a cloud infrastructure 300. The cloud infrastructure 300 includes a combination of physical and virtual processing resources for implementing at least a portion of the information processing system 100. The cloud infrastructure 300 includes a plurality of virtual machines (VMs) and / or container sets 302-1, 302-2, ... 302-L implemented using a virtualization infrastructure 304. The virtualization infrastructure 304 runs on a physical infrastructure 305 and illustratively includes one or more hypervisors and / or operating system-level virtualization infrastructure. The operating system-level virtualization infrastructure illustratively includes a kernel control group of a Linux operating system or other type of operating system.

[0081] The cloud infrastructure 300 also includes a set of applications 310-1, 310-2, ... 310-L, which run on corresponding sets of VMs / containers 302-1, 302-2, ... 302-L under the control of the virtualization infrastructure 304. The VM / container set 302 includes a corresponding VM, a corresponding set of one or more containers, or a corresponding set of one or more containers running in a VM. Figure 3 In some implementations of the embodiments, the VM / container set 302 includes respective VMs implemented using a virtualization infrastructure 304 including at least one hypervisor.

[0082] A hypervisor platform may be used to implement a hypervisor within the virtualized infrastructure 304, wherein the hypervisor platform has an associated virtual infrastructure management system. The underlying physical machines include one or more distributed processing platforms including one or more storage systems.

[0083] exist Figure 3 In other implementations of the embodiment, the VM / container set 302 includes corresponding containers implemented using a virtualization infrastructure 304, which provides operating system-level virtualization functions, such as support for Docker containers running on a bare metal host or Docker containers running on a VM. The containers are illustratively implemented using individual kernel control groups of the operating system.

[0084] As is apparent from the above, one or more processing modules or other components of information handling system 100 may each run on a computer, server, storage device or other processing platform element. A given such element is considered an example of what is more generally referred to herein as a "processing device". Figure 3The cloud infrastructure 300 shown may represent at least a portion of a processing platform. Another example of such a processing platform is Figure 4 A processing platform 400 is shown.

[0085] The processing platform 400 in this embodiment comprises a portion of the information handling system 100 and includes a plurality of processing devices, denoted as 402 - 1 , 402 - 2 , 402 - 3 , . . . 402 -K, which communicate with each other via a network 404 .

[0086] Network 404 includes any type of network, including, for example, a global computer network such as the Internet, a WAN, a LAN, a satellite network, a telephone or wired network, a cellular network, a wireless network such as a Wi-Fi or WiMAX network, or various portions or combinations of these and other types of networks.

[0087] The processing device 402 - 1 in the processing platform 400 includes a processor 410 coupled to a memory 412 .

[0088] Processor 410 includes a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other types of processing circuits, as well as portions or combinations of these circuit elements.

[0089] The memory 412 includes any combination of random access memory (RAM), read-only memory (ROM), or other types of memory. The memory 412 and other memories disclosed herein should be viewed as illustrative examples of what is more generally referred to as a "processor-readable storage medium" that stores executable program code for one or more software programs.

[0090] Articles of manufacture including such processor-readable storage media are considered to be illustrative embodiments. Such articles of manufacture given include, for example, a storage array, a storage disk, or an integrated circuit containing RAM, ROM or other electronic memory, or any of various other types of computer program products. The term "article of manufacture" as used herein should be understood to exclude transient propagating signals. Many other types of computer program products including processor-readable storage media may be used.

[0091] Also included in the processing device 402-1 is a network interface circuit 414 for interfacing the processing device with the network 404 and other system components, and may include a conventional transceiver.

[0092] It is assumed that the other processing devices 402 of the processing platform 400 are configured in a manner similar to the processing device 402 - 1 shown in the figure.

[0093] Likewise, the specific processing platform 400 shown in the figure is presented only as an example, and information processing system 100 may include additional or alternative processing platforms, as well as any combination of many different processing platforms, each such platform including one or more computers, servers, storage devices or other processing devices.

[0094] For example, other processing platforms for implementing the illustrative embodiments may include different types of virtualization infrastructures, instead of or in addition to virtualization infrastructures including virtual machines. Such virtualization infrastructures illustratively include container-based virtualization infrastructures configured to provide Docker containers or other types of LXC.

[0095] As another example, in some embodiments, portions of a given processing platform may include a converged infrastructure.

[0096] Therefore, it should be understood that in other embodiments, different arrangements of additional or alternative elements may be used.At least a subset of these elements may be implemented collectively on a common processing platform, or each such element may be implemented on a separate processing platform.

[0097] Additionally, many other arrangements of computers, servers, storage products or devices, or other components are possible in information handling system 100. These components may communicate with other elements of information handling system 100 via any type of network or other communication medium.

[0098] For example, in the illustrative embodiments, specific types of storage products that may be used to implement a given storage system of a distributed processing system include all-flash and hybrid flash storage arrays, scale-out all-flash storage arrays, scale-out NAS clusters, or other types of storage arrays. In the illustrative embodiments, a combination of multiple of these and other storage products may also be used to implement a given storage system.

[0099] It should be emphasized again that the above-described embodiments are presented for illustrative purposes only. Many variations and other alternative embodiments may be used. In addition, the specific configurations of the system and device elements illustratively shown in the drawings and the associated processing operations may be changed in other embodiments. Thus, for example, the specific types of processing devices, modules, systems, and resources deployed in a given embodiment and their corresponding configurations may be different. In addition, the various assumptions made above in the process of describing the illustrative embodiments should also be regarded as exemplary, rather than requirements or limitations of the present disclosure. Many other alternative embodiments within the scope of the appended claims will be clear to those skilled in the art.

Claims

1. A method comprising: A regression test case is selected by a test selection system from a plurality of regression test cases in a software testing lifecycle system; calculating a fault detection score for the regression test case based on a fault detection decay rate score associated with the regression test case; selecting the regression test case for execution on a test system based on the fault detection score; as well as The regression test case is executed on the test system, wherein the method is implemented by at least one processing device including a processor coupled to a memory. 2 . The method of claim 1 , wherein a previous execution of the regression test case on a first system detected at least one fault on the first system.

3. The method of claim 1 , wherein calculating the fault detection score for the regression test case comprises: If the regression test case is associated with a new feature fault detection tag, assigning the regression test case to a plurality of regression test cases of a first priority; as well as If the regression test case is not associated with the new feature fault detection tag, the regression test case is assigned to a second priority plurality of regression test cases.

4. The method of claim 1 , wherein calculating the fault detection score for the regression test case comprises: determining a plurality of detection faults associated with the regression test case; as well as The fault detection score is calculated using the plurality of detected faults. The method of claim 4 , wherein the plurality of detected faults are associated with validity periods.

6. The method of claim 4, wherein using the plurality of detected faults to calculate the fault detection score comprises: The fault detection decay rate score is calculated for each detected fault of the plurality of detected faults.

7. The method of claim 6, wherein calculating the fault detection decay rate score comprises: The fault detection decay rate score is calculated for at least one priority level associated with each detected fault of the plurality of detected faults.

8. The method according to claim 7, further comprising: The fault detection decay rate score is calculated for each priority level associated with each detected fault of the plurality of detected faults.

9. The method of claim 6, wherein calculating the fault detection decay rate score comprises: The fault detection decay rate score is calculated using Newton's law of cooling.

10. The method of claim 9, wherein using Newton's law of cooling to calculate the fault detection decay rate score comprises: The fault detection decay rate score is calculated using validity periods associated with the plurality of detected faults.

11. The method according to claim 10, further comprising: A fault detection time decay factor is determined for each detected fault of the plurality of detected faults.

12. The method according to claim 11, further comprising: A sum of the fault detection time decay factors for each of the plurality of detected faults is determined to determine the fault detection score for the regression test case.

13. The method of claim 1 , wherein selecting the regression test case for execution on the test system based on the fault detection score comprises: The first priority plurality of regression test cases are ranked according to a corresponding fault detection score associated with each regression test case in the first priority plurality of regression test cases to determine an execution priority associated with each regression test case in the first priority plurality of regression test cases.

14. The method of claim 1 , wherein selecting the regression test case for execution on the test system based on the fault detection score comprises: The second priority plurality of regression test cases are ranked according to a corresponding fault detection score associated with each regression test case in the second priority plurality of regression test cases to determine an execution priority associated with each regression test case in the second priority plurality of regression test cases.

15. The method of claim 1, wherein executing the regression test case on the test system comprises: executing the first priority plurality of regression test cases according to an execution priority associated with each regression test case in the first priority plurality of regression test cases; as well as Upon completion of execution of the first priority plurality of regression test cases, the second priority plurality of regression test cases are executed according to the execution priority associated with each regression test case in the second priority plurality of regression test cases.

16. The method of claim 15, wherein the first priority plurality of regression test cases is associated with a first validity period and the second priority plurality of regression test cases is associated with a second validity period. The method of claim 16 , wherein the first validity period is different from the second validity period.

18. The method of claim 15, wherein the execution priority associated with each regression test case in the first priority plurality of regression test cases optimizes effective fault detection on the test system.

19. A system comprising: at least one processing device including a processor coupled to a memory; The at least one processing device is configured to: A regression test case is selected by a test selection system from a plurality of regression test cases in a software testing lifecycle system; calculating a fault detection score for the regression test case based on a fault detection decay rate score associated with the regression test case; selecting the regression test case for execution on a test system based on the fault detection score; as well as The regression test case is executed on the test system.

20. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code, when executed by at least one processing device, causes the at least one processing device to: A regression test case is selected by a test selection system from a plurality of regression test cases in a software testing lifecycle system; calculating a fault detection score for the regression test case based on a fault detection decay rate score associated with the regression test case; selecting the regression test case for execution on a test system based on the fault detection score; as well as The regression test case is executed on the test system.

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