CN113254344A - Novel computing engine test platform, device and system - Google Patents
Novel computing engine test platform, device and system Download PDFInfo
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- CN113254344A CN113254344A CN202110631985.8A CN202110631985A CN113254344A CN 113254344 A CN113254344 A CN 113254344A CN 202110631985 A CN202110631985 A CN 202110631985A CN 113254344 A CN113254344 A CN 113254344A
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Abstract
The invention belongs to the technical field of computer engine test, in particular to a new computer engine test platform, a device and a system, which comprises an off-line testing platform, a real-time testing platform and a testing item analysis module, wherein the off-line testing platform comprises an off-line performance test, an off-line function test and an off-line stability test, the real-time test platform comprises a real-time performance test, a real-time function test and a real-time stability test, the test item analysis module comprises an off-line test item analysis and an off-line real-time test item analysis, can respectively obtain indexes such as a CPU, throughput, memory, data analysis, real-time performance and the like from three directions of an off-line platform and a real-time platform in sequence, and the improved algorithm can not only predict the indexes under the offline condition, but also predict and analyze the indexes under the offline and real-time conditions to the performance of the calculation engine to be tested.
Description
Technical Field
The invention relates to the technical field of computing engine testing, in particular to a novel computing engine testing platform, device and system.
Background
Nowadays, a plurality of industries use big data, and the big data is used for balancing left and right to analyze practical problems, so that how to construct a platform for processing the big data is the basis for researching the big data. Generally speaking, the data volume required for real-time calculation is small, it is more focused on the rapid response, it is done in the memory, according to the related report, more than 90 percent of the data information comes from the sensors, devices and technologies, therefore, the data growth speed will also be accelerated. We first analyze from a technical level, and the processing of these data in large data fields will become more and more complex. For example, when using the 'dripping' APP, the user must use real-time information to reserve taxi taking information and then conveniently take taxi. For another example, when monitoring a patient, real-time information is also used for processing. The development and research of the computer engine attracts the attention and attention of developers in the computer industry. For machine testing methods on the market, each test module cannot be shared and the maintenance workload is relatively large.
In recent years, testers also need to have new knowledge of the computing core, which results in the need of prolonging training period and wasting economic output. Meanwhile, in recent years, along with the increase of the application of the same kind of products in the market, the competition is intensified. In order to meet the market, the versions of the same product of the same company are updated more frequently. This increases the workload of the tester. Because, after each update of the version, the tester will test the old and new functions on different platforms and devices. Sometimes, the on-line time is urgent, the testing personnel are not enough, and the task is performed, so that much time and energy are delayed.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above and/or other problems associated with the testing of existing computing engines.
Therefore, an object of the present invention is to provide a new computing engine testing platform, device and system, which can sequentially obtain indexes such as CPU, throughput, memory, data analysis, real-time performance and the like from the offline and real-time platforms respectively, and apply an improved algorithm to predict not only the indexes under the offline condition, but also the indexes under the offline and real-time conditions to predict and analyze the performance of the computing engine to be tested.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
a new computing engine testing platform, comprising: the device comprises an offline test platform, a real-time test platform and a test item analysis module.
A novel computing engine test system comprises the computing engine test platform, wherein the offline test platform comprises an offline performance test, an offline function test and an offline stability test, the real-time test platform comprises a real-time performance test, a real-time function test and a real-time stability test, the test item analysis module comprises an offline test item analysis module and an offline real-time test item analysis module, the offline performance test comprises throughput, a memory and a CPU, the offline function test comprises data analysis and data conversion, the real-time performance test comprises operation performance, JVM and instantaneity, and the real-time function test comprises abnormal data processing and data formats.
A new computing engine testing device comprises the computing engine testing platform and the computing engine testing system.
Compared with the prior art: the method has practical significance in the test process of the platforms such as the computing engine and the like, can respectively and sequentially obtain indexes such as a CPU, throughput, memory, data analysis, instantaneity and the like from an offline platform and a real-time platform in three directions, and can predict the indexes under the offline condition and predict and analyze the performance of the computing engine to be tested by applying the improved algorithm.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail with reference to the accompanying drawings and detailed embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise. Wherein:
FIG. 1 is a schematic structural diagram of a functional module of the system of the present invention;
FIG. 2 is a diagram illustrating the accuracy of the present invention varying with K;
FIG. 3 is a diagram of the test results of the offline test item test set of the present invention;
FIG. 4 is a diagram of the test results of the offline real-time test item test set of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and it will be apparent to those of ordinary skill in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention will be described in detail with reference to the drawings, wherein for convenience of illustration, the cross-sectional view of the device structure is not enlarged partially according to the general scale, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides a new computing engine test platform, device and system, which can respectively obtain indexes such as a CPU, throughput, memory, data analysis, instantaneity and the like from an offline platform and a real-time platform in sequence, and can predict the indexes under the offline condition and predict and analyze the performance of a computing engine to be tested by applying an improved algorithm, wherein the performance test platform, the device and the system can also predict and analyze the indexes under the offline condition and the real-time condition, and the performance test platform, the device and the system comprise: the system comprises an offline test platform, a real-time test platform and a test item analysis module;
a novel computing engine test system comprises any one of the computing engine test platforms, wherein the offline test platform comprises an offline performance test, an offline function test and an offline stability test, the real-time test platform comprises a real-time performance test, a real-time function test and a real-time stability test, the test item analysis module comprises an offline test item analysis module and an offline real-time test item analysis module, the offline performance test comprises throughput, a memory and a CPU, the offline function test comprises data analysis and data conversion, the real-time performance test comprises operating performance, JVM and real-time performance, and the real-time function test comprises abnormal data processing and data formats.
A new computing engine testing device is characterized by comprising any one of the computing engine testing platforms and any one of the computing engine testing systems.
Improved KNN to test item results analysis
Aiming at the defects of the traditional KNN algorithm, a weighting-KNN algorithm is introduced, and in fact, a weight value is added when the distance is calculated. The improved KNN algorithm comprises the following steps:
the algorithm is as follows: weighting-KNN algorithm
Inputting: test set test _ x, training set train _ x, labels, k values
And (3) outputting: classification result label
The method comprises the following steps:
1) calculating distance between test _ x and train _ x by using Euler distance
2) Sorting the ascending order of distance
3) Selecting k points with the minimum distance from test _ x
4) Calculating the weight w
5) Determining the probability of the occurrence of the class in the k points
6) And returning the class with the highest probability as a predicted classification result label of test _ x.
Decision tree algorithm based on Relieff
The above has improved the ReliefF algorithm due to unbalanced data in the data set, and it will be explained next that the improved ReliefF algorithm is introduced into the decision tree algorithm, resulting in a new algorithm, and is named as: ReliefF _ DecisionTree.
The role of the Relieff algorithm is to eliminate some irrelevant features first, and then sort the rest features according to the weight value to remove some features which are not important.
The algorithm is as follows: relieff _ DesionTree
Inputting: training set D, label set T, sample extraction times n, and nearest neighbor sample number k
And (3) outputting: weight W of each feature
The method comprises the following steps:
1) assume that a given feature has a weight of 0 and W is an empty set
2)for j＝1 to n do
2.1) randomly selecting a sample R from the training set D;
2.2) finding k nearest neighbors Hj (j is 1, 2.. multidot.k) of the R from the same type of samples of the R, and finding k nearest neighbors Mj (C) from each different type of sample set;
3)for A＝1 to n All feature do
End
4)If Min(W(A))&&W(A))＝＝0 do
delete feature
off-line experimental result analysis
In the experimental process, three classification problems are carried out by using 3609 data sets, and the F1 value, the recall rate and the accuracy rate are used for judging the quality of the model. And adopting the Euler distance as a distance formula of KNN. Firstly, 30 percent of the training set is selected for testing when testing is carried out, and a change graph of the accuracy along with the selection of the K value is obtained. From fig. 2, it can be seen that the accuracy is the highest when K is 5.
Next, after determining that K is 5, the same data set is used, and the test is performed using the ratio of 7:3, and the obtained experimental results are as follows, as shown in table 1, it can be seen that the weighted KNN algorithm has a good prediction effect.
TABLE 1 comparative experimental results
Offline + real-time experimental result analysis
The data sets used in the experimental procedure are the test items mentioned in chapter two for testing the computational engine in off-line and real-time situations. Wherein, the CPU utilization rate, the memory occupancy rate, the throughput and the numerical value conversion are selected off line; and selecting exception handling and real-time delay time under the real-time condition, and labeling the obtained test items according to classification rules in the second chapter industry. And respectively carrying out decision tree and RelieF-decision Tree algorithms to train the model and obtain the following results.
Table 2 comparative experimental results
By analyzing the experimental results, we can clearly see that the RelieF _ decisionTree is more suitable for the classification algorithm of the test items in the test field.
Establishing connections for offline computing engines
1) Performing tasks
Before the tool is used, connection needs to be established, and four parameters of a target address, a port number, a user name and a password are needed so as to establish remote connection with the target address. We can also delete the corresponding connection and add a new connection.
Performance testing of real-time computing engines
After the connection is established, the program automatically transmits the test design package and the test set to the machine to be tested, clicks the 'execute task' button under the 'performance test', and displays all test cases added in the current database.
System implementation
Test field oriented test item analysis result
And analyzing and predicting the four indexes of the CPU utilization rate, the memory utilization rate, the throughput and whether numerical value conversion occurs or not by using an improved KNN algorithm. And analyzing and predicting the indexes such as CPU utilization rate, memory utilization rate, throughput, whether numerical value conversion occurs, whether exception processing exists, real-time delay time and the like by using an improved RF _ DescriptionTree algorithm. The research is not only suitable for the prediction analysis in the field of computer engine test, but also suitable for the analysis of test items in other test fields. The method makes great contribution to the test field.
Offline computing engine test item analysis
1. Analysis results of off-line calculation engine test items
1.1) adding task templates. And obtaining a test item of the calculation engine test under the offline data through an offline calculation engine test tool, and then inputting the specified test item to obtain a calculation engine test item analysis result. For example, we input "cpu usage" as 20, "memory usage" as 30, "throughput" as 40, "and" yes "if there is a numerical conversion.
1.2) executing tasks
When clicking 'execute task', the user jumps to the executing task list to display the task name, scene and execution date. If the page is blank after waiting for a moment, the task is executed completely. Otherwise, the task execution fails.
1.3) completed execution task
And the completed task list comprises the display task name, scene and execution date. In addition, we can click on the "run results" button to display the run results.
1.3.1) task execution results
2. Analysis result of off-line calculation engine test item group
2.1) assume that the input is a set of offline engine test items.
2.2) executing tasks
When clicking 'execute task', the user jumps to the executing task list to display the task name, scene and execution date. If the page is blank after waiting for a moment, the task is executed completely. Otherwise, the task execution fails.
2.3) completed execution task
And the completed task list comprises the display task name, scene and execution date. In addition, we can click on the "run results" button to display the run results.
2.3.1) results of task runs
In the pie chart of FIG. 3, "no _ stress _ Count" represents the number of neutral performance pressures, "touch _ stress _ Count" represents the number of touching performance thresholds, and "sesors _ stress _ Count" represents the number of severe performance pressures. From the graph, it can be clearly seen that for a given test _ data _ list _ offline. xls, the analysis result of the calculation engine is that for a performance threshold, no performance pressure occupies 36.26 percent of the total test case, 27.46 percent of the total test case is occupied, and the remaining percentage is the severe performance threshold.
3.3.1.2 offline, real-time computing Engine test item analysis
1. Analysis and prediction result of off-line and real-time calculation engine test item
1.1) obtaining the test items of the calculation engine test by using an off-line test tool and a real-time test tool, and then inputting the specified test items to obtain the analysis result of the calculation engine test items. Such as: let us input a CPU usage of 20, a memory occupancy of 30, a throughput of 10, a true numerical conversion, and exception handling, and a real-time delay time of 1000 ms.
1.2) executing tasks
When clicking 'execute task', the user jumps to the executing task list to display the task name, scene and execution date. If the page is blank after waiting for a moment, the task is executed completely. Otherwise, the task execution fails.
1.3) completed execution task
And the completed task list comprises the display task name, scene and execution date. In addition, we can click on the "run results" button to display the run results.
1.3.1) task execution results
2. Analyzing and predicting result of test item group of off-line and real-time computing engine
2.1) assuming that a group of off-line and real-time engine test items are input, adding a task template interface.
2.2) executing tasks
When clicking 'execute task', the user jumps to the executing task list to display the task name, scene and execution date. If the page is blank after waiting for a moment, the task is executed completely. Otherwise, the task execution fails.
2.3) completed execution task
Within this page is a list of completed tasks, including displaying the task name, scene, and execution date. In addition, we can click on the "run results" button to display the run results.
2.3.1) results of task runs
In the chart of fig. 4, no stress Count represents the number of dead pressures, touch stress Count represents the number of dead pressures that reach the performance threshold, and sesors stress Count represents the number of severe performance pressures. From the graph, it can be seen that for a given test _ data _ list.xls, the neutral energy pressure occupies 40.09 percent of the total test case, reaching a performance threshold of 29.26 percent of the total test case, with the remaining percentage of the severe performance threshold.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the disclosed embodiments of the invention may be used in any combination, provided that no structural conflict exists, and the combinations are not exhaustively described in this specification merely for the sake of brevity and resource conservation. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (3)
1. A new computing engine test platform, comprising: the device comprises an offline test platform, a real-time test platform and a test item analysis module.
2. A new computing engine testing system comprising the computing engine testing platform of any of claim 1, wherein the offline testing platform comprises offline performance testing, offline functional testing, and offline stability testing, the real-time testing platform comprises real-time performance testing, real-time functional testing, and real-time stability testing, the test item analysis module comprises offline test item analysis and offline real-time test item analysis, the offline performance testing comprises throughput, memory, and CPU, the offline functional testing comprises data analysis and data conversion, the real-time performance testing comprises operational performance, JVM, and real-time, and the real-time functional testing comprises exception data handling and data format.
3. A new computing engine testing apparatus comprising the computing engine testing platform of any one of claim 1 and the computing engine testing system of any one of claim 2.
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- 2021-06-07 CN CN202110631985.8A patent/CN113254344A/en active Pending
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