CN115905023A - Integrated test platform, test method, test terminal, storage medium and device - Google Patents
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Abstract
The invention discloses an integrated test platform, a test method, a test terminal, a storage medium and equipment, which comprise a system pressure simulation module and an application safety simulation module; the system pressure simulation module simulates concurrent data, outputs the concurrent data to the tested object and carries out pressure test on the system performance of the tested object; the application security simulation module downloads an application security vulnerability, and after application security threats are simulated, security threat data are output to the tested object; the method simulates incomplete data which can be received in the data receiving process and is received by a tested object, simultaneously repairs information missing data through a test platform and the tested object, finally compares the similarity of the repaired data, obtains the maximum bearable pressure of the tested object through iterative optimization, and completes the pressure test; the security testing of the tested object is realized by connecting the security vulnerability rule base, simulating the security vulnerability attack through the testing platform and by a one-by-one iterative simulation method.
Description
Technical Field
The invention relates to the technical field of software testing, in particular to an integrated testing platform, a testing method, a testing terminal, a storage medium and equipment.
Background
Software testing can be classified into functional testing, performance testing, reliability testing, safety testing, usability testing and the like from the point of view of tested characteristics.
Generally, different environments and different use requirements are required for the use of test tools with different characteristics, and a general user needs to independently perform corresponding tool deployment, installation and maintenance when using different test tools, and the results of different test tools also need to be independently analyzed. The whole process is scattered and fine crushed, and the working efficiency is reduced.
Taking performance testing as an example, various data are increased rapidly as the technical level of the internet develops, the average data amount processed by general software per hour can be calculated according to TB at the same time, meanwhile, due to the openness of the internet, various software is often in a high concurrency environment, and data are transmitted to a software end through a network, so that the condition of data loss may exist in the process of acquisition and transmission, a large amount of incomplete data received by the software end is caused, the incomplete data need to be repaired when the software processes the data, the performance requirement on the software end is high, the existing testing equipment can complete concurrency testing, but the concurrency testing capability for incomplete information is insufficient, and finally the condition that the testing result is greater than the actual concurrency number may occur.
Disclosure of Invention
The invention aims to solve the technical problem that corresponding tool deployment, installation and maintenance are required to be independently carried out when different testing tools are used, and aims to provide an integrated testing platform, a testing method, a testing terminal, a storage medium and equipment.
The invention is realized by the following technical scheme:
an integrated test platform, comprising: the system comprises a system pressure simulation module and an application safety simulation module;
the system pressure simulation module simulates concurrent data, outputs the concurrent data to the tested object and carries out pressure test on the system performance of the tested object;
and the application security simulation module is in communication connection with the application security vulnerability rule base, downloads the application security vulnerability to the application security simulation software, and outputs security threat data to the tested object after simulating the application security threat.
An integrated test method is based on the integrated test platform, and comprises a pressure test method and a safety test method; the pressure test method for the system performance comprises the following steps:
s10, generating an information missing data stream, wherein the information missing data stream comprises N information missing data;
s20, selecting n pieces of information missing data to obtain a data stream to be detected;
s30, inputting a data stream to be detected into a detected object, and obtaining first data after concurrent processing of the detected object;
s40, inputting the selected n information missing data into a test platform, and obtaining second data after the repair processing of the test platform;
s50, comparing the first data with the second data to obtain the similarity of the first data and the second data;
s60, if the similarity is larger than a set value, indicating that the pressure test is successful;
if the similarity is smaller than the set value, indicating that the pressure test fails;
s70, if the pressure test is successful, making n = n + n', and skipping to the step S20;
if the pressure test fails, making n = n-n', and jumping to step S20; n 'represents the pressure test span, n' is greater than 1;
s80, the steps S20 to S70 are repeated until the results of the two adjacent pressure tests are opposite;
s90, reducing n ', repeating the steps S20-S80 until n' =1, obtaining an n value when the pressure test under the condition of the step S80 succeeds, outputting the n value, and completing the pressure test;
the application safety testing method comprises the following steps:
s10, updating and acquiring security vulnerability information in a security vulnerability rule base;
s20, sequencing all security vulnerability information, and numbering the security vulnerability information;
s30, let m =1;
s40, judging whether the simulation of all the security hole information is finished, if not, simulating the mth security hole information, and inputting the security hole information to the tested object;
if yes, go to step s70
s50; judging whether the tested object has corresponding security holes;
s60, if yes, skipping to step s70;
if not, making m = m +1, and skipping to the step s30;
and s70, outputting the test result.
Specifically, in step S10, the method for generating the missing information data stream includes:
obtaining a UML activity diagram, and taking an activity node and a branch merging bar in the activity diagram as a vertex to obtain a directed graph corresponding to a concurrent model diagram;
go through the set of vertices V if Owner (V) i )＝o k ，v i E is V, then V is i Adding Act (o) k ) Finally, a vertex set of each object is obtained; owner (v) i )＝o k Representing an activity v i Subject of (A) is o k And move v i On the activity diagram at object o k Within the lane of (3);
go through the set of edges E, if End (E) j )＝v i ，Owner(Begin(e j ))≠Owner(v i )v i ∈V，e j E, then Owner (Begin (E) j ) Adding VR (v) i ) Finally, a set of reference object sets of each vertex is obtained; end (e) j )＝v n Represents an edge e j Ending at vertex v n ，Begin(e j )＝v m Represents an edge e j Starting at vertex v m ，VR(v i ) Representing the vertex v i A set of direct reference objects;
selecting a process object from the reference object set of each vertex of the key objects in the sequence to form an object set, and constructing a vertex sub-graph of the primary concurrent model graph by taking the vertex related to the object set and the key objects as the vertex;
and repeatedly constructing the vertex subgraphs until all the objects appear at least once, obtaining a plurality of subgraphs, and generating a plurality of information missing data by using a functional test clue generating method based on the UML activity graph to form an information missing data stream.
Specifically, in step S40, the information missing data is repaired by the test platform, and the repairing method includes:
acquiring a missing data statistic value storage address, and constructing a training relation between template data and current missing data;
determining the number of proper hidden layer nodes according to the data of the complete data sample;
establishing a nonlinear mapping model of the missing attributes and other attributes of the missing samples;
obtaining the connection weight of the neuron between the hidden layer and the output layer;
and finishing the repair of the information missing data.
Specifically, the method for acquiring the missing data statistics storage address comprises the following steps:
two independent Hash function families are given according to a minimum counting/frequency summary method;
expressing and accumulating the data attribute values of the information flow in a given time period to a two-dimensional table data structure;
calculating a clustering function of the current missing data attribute;
and (4) introducing a conflict identification variable to calculate a missing data statistic value storage address.
Specifically, the specific method for acquiring the missing data statistic storage address includes:
two mutually independent Hash function families e' dg (f f ' g ) The calculation formula of (c) is:
wherein, S' fgj For the data stream to be tested, a pageX er1 ,X er2 ,…,X ern Is S' fgj Each vector X represents a vector eri Including j-dimensional attributes
Is a self-domain set J K Value of (A), M k Is value domain, h 'corresponding to the k-dimension attribute' fj Is from set U' sd To aggregate W' sfjj Of hash function family, d' dg Is a prime number;
d Hash functions (h) are uniformly and randomly selected from a Hash function family 1 ,h 2 ,...,h d ) Accumulated to two-dimensional table data structure W' aq The calculation formula of (c) is:
wherein, δ is a probability parameter, and S (x, v) is a one-dimensional data stream;
the calculation formula of the clustering function for calculating the attribute of the current missing data is as follows:
wherein h is xc Is history statistical information v 'corresponding to the information keyword x' sdjj Is the mapping result of the Hash function to x, b' cnkk Is the current attribute j 'of any missing data' zf Of certain attribute value u' xb Weight of d' sg As attribute value u i Statistic of (2), E' sjk Is an attribute value u k The statistical value of (a);
calculating missing data statistic value storage address e' afh The calculation formula of (c) is:
wherein, X (u) i J) is the number of conflicts, k' zvkkl Historical summary statistics, u 'stored for a data structure' hjk The number of missing data storage units.
Specifically, the specific method for repairing the information missing data includes:
building template data and current missing dataThe training relationship between the two is as follows:
wherein it is present>
For missing data topological correlation point set, l is time series parameter, n f For missing data point set size, t zv Is the current time, k 'on the time series' fgj Is at time t' zv Complete data, λ 'without repair' dfy Is radial basis function weight, p' xb Is the distance, t ', of the current marker point to its nearest neighbor marker point' sg A parameter matrix for a training network; />
The calculation formula for determining the appropriate number of hidden nodes is as follows:
wherein, (x' i ,y' i ) Is missing sample, l' K Is the capacity, x 'of the missing sample' i Is an input complete data sample value, y' i Is an output value, r " sd Is empirical risk, h' sg Is structural risk, e' sgjj The remaining attribute value of the missing sample;
the nonlinear mapping model of the missing attribute and other attributes of the missing sample is as follows:
wherein, f' xbn Is the center of the basis function, σ' xv Is the radial basis function variance, jk' jkkl Is the variance of a Gaussian function, x p Is the p-th input sample, o ″) poi The number of all data attribute samples;
the calculation formula of the connection weight of the neuron between the hidden layer and the output layer is as follows:
wherein, d' dfj Is hidden layer node center, k' dh A weight vector for a hidden layer node>
Is the actual output, g ", of the j" th output node of the network corresponding to the input sample fgk An interval mapped for the data sample;
the calculation formula for the repair of information missing data is:
wherein, σ' dfkk To call the function newrb, p' cbm The structure information of the current missing data.
An integrated test terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of an integrated test method as described above when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of an integration testing method as set out above.
An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: the steps of implementing an integration test method as described above.
Compared with the prior art, the invention has the following advantages and beneficial effects:
according to the invention, an integrated test platform is arranged, a system pressure simulation module and an application safety simulation module are arranged in the platform, the system pressure simulation module is used for carrying out pressure test on the system performance of a tested object, the application safety simulation module is used for carrying out application safety test on the tested object, and a simulation system is adopted, so that the real performance load and safety threat can be simulated, the risk investigation on the system can be carried out under various environments, and the real situation can be accurately mastered;
the method simulates incomplete data which can be received in the data receiving process and is received by a tested object through generating a plurality of information missing data, simultaneously repairs the information missing data through a test platform and the tested object, finally compares the similarity of the repaired data, obtains the maximum bearable pressure of the tested object through iterative optimization, and thus completes the pressure test of the tested object;
the invention realizes the security test of the tested object by connecting the security vulnerability rule base, simulating the security vulnerability attack through the test platform and by a method of one-by-one iterative simulation.
The invention can help users to conveniently simulate and realize system performance pressure, application safety simulation, excellent performance and expandability by a built-in pressure test method and a safety test method and combining the test method with a test platform based on a physical device and a virtualization environment.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the principles of the invention.
FIG. 1 is a flow chart of an integrated test method according to the present invention, in which a pressure test method is illustrated.
Fig. 2 is a flow chart of an integration test method according to the present invention, in which a safety test method is shown.
Fig. 3 is a schematic flowchart of a method for repairing data with missing information by using a test platform according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the invention.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
In the present invention, the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example one
The embodiment provides an integrated test platform, including: the system comprises a system pressure simulation module and an application safety simulation module;
the test platform is composed of a system layer, a component layer and an application layer.
The application layer comprises a pressure test and a safety test, the component layer comprises an analysis management module and an operation management module, and the system layer comprises a platform management module, a simulation system service workflow engine, a service simulation system, a load/threat simulation system and a report analysis system.
The analysis management module comprises result AI analysis, data visualization display, report generation analysis, exploratory analysis, big data statistical analysis and enhanced analysis.
The operation management module comprises a simulation process management system, a simulation operation monitoring system, a simulation resource monitoring system, a big data reporting system, a data implementation analysis system and a task scheduling/warning system.
The platform management module comprises load/threat simulation, user management, authority management and report analysis relation.
The application layer is composed of a control platform, a performance test platform, a safety test platform and a resource monitoring platform, wherein the control platform unifies user and authority management, the performance test platform integrates to provide performance test capability, the safety test platform integrates to provide safety test capability, and the resource monitoring platform integrates environment operation and monitoring functions. The corresponding test service can be completed under the integral cooperation of the control platform, the performance test platform and the safety test platform.
The system pressure simulation module simulates concurrent data, outputs the concurrent data to the tested object and carries out pressure test on the system performance of the tested object; the simulation of the service simulation system is controlled through the platform management module, the analysis and test structure is analyzed through the operation management module and other control system layers of the component layer and the analysis and management module, and finally the pressure simulation of the application layer is achieved.
And the application security simulation module is in communication connection with the application security vulnerability rule base, downloads the application security vulnerability to application security simulation software, and outputs security threat data to the tested object after simulating the application security threat. The principle is similar to the system pressure simulation module.
The test platform unifies user and authority management, the performance test platform integrates to provide performance test capability, the safety test platform integrates to provide safety test capability, and the resource monitoring platform integrates environment operation and monitoring functions. The corresponding test service can be completed under the integral cooperation of the control platform, the performance test platform and the safety test platform.
Example two
The embodiment provides an integrated test method, which is based on the integrated test platform, and the test method comprises a pressure test method and a safety test method.
The pressure test method for the system performance comprises the following steps:
s10, generating an information missing data stream, wherein the information missing data stream comprises N information missing data; by generating the information missing data stream, a large amount of incomplete data which can be received by the tested object in practice is simulated.
S20, selecting n information missing data to obtain a data stream to be detected; the n pieces of information missing data are concurrent data received by the object to be tested.
S30, inputting a data stream to be detected into a detected object, and obtaining first data after concurrent processing of the detected object; the measured object has a data restoration function, and first data for completing data restoration can be obtained through processing of the measured object.
S40, inputting the selected n information missing data into a test platform, and obtaining second data after the repair processing of the test platform; and the test platform synchronously realizes the repair of the data stream to be tested.
S50, comparing the first data with the second data to obtain the similarity of the first data and the second data; and judging the accuracy of the first data by taking the second data as a reference.
S60, if the similarity is larger than a set value, indicating that the pressure test is successful; namely, the tested object can simultaneously receive the concurrence of n information missing data.
If the similarity is smaller than the set value, indicating that the pressure test fails; namely proving that the tested object can not receive the concurrence of n information missing data simultaneously.
S70, if the pressure test is successful, making n = n + n', and skipping to the step S20; namely, information missing data in the data stream to be tested is added, and the upper limit is further tested.
If the pressure test fails, making n = n-n', and skipping to the step S20; n 'represents the pressure test span, n' is greater than 1; namely, information missing data in the data stream to be tested is reduced. Setting n' to be larger can reduce the number of tests and obtain accurate data more quickly.
S80, the steps S20 to S70 are repeated until the results of the two adjacent pressure tests are opposite; i.e. to determine the range of n.
S90, reducing n ', repeating the steps S20-S80 until n' =1, obtaining an n value when the pressure test under the condition of the step S80 succeeds, outputting the n value, and completing the pressure test; and reducing the pressure test span by reducing n' until a final n value is obtained, namely the maximum concurrent data which can be received by the tested object.
The application safety testing method comprises the following steps:
s10, updating and acquiring security vulnerability information in a security vulnerability rule base; and the security vulnerability information can be updated in real time by adopting the most authoritative global application security vulnerability rule base.
s20, sequencing all the security vulnerability information, and numbering the security vulnerability information; the numbering may be random or chronological.
s30, let m =1; i.e. application security tests are performed from the security breach information numbered 1.
s40, judging whether the simulation of all the security hole information is finished, if not, simulating the mth security hole information, and inputting the security hole information to the tested object;
if yes, go to step s70
s50; judging whether the tested object has corresponding security holes; thousands of application security threats are simulated, and the security of an application system is comprehensively detected.
s60, if yes, jumping to step s70;
if not, making m = m +1, and jumping to the step s30; and (4) performing simulation test on all security vulnerability information through iteration.
And s70, outputting the test result. The test results include a security breach and no security breach.
EXAMPLE III
In this embodiment, step S10 in the second embodiment is described in detail, and the method for generating the missing information data stream includes: marking corresponding methods and parameters on the top points of the active graph, then traversing the active graph deeply to obtain traversal paths with multiple parameters, merging some paths according to constraints, and finally replacing the methods and parameters with a bottom calling sequence by combining a timing diagram to form information missing data.
The specific method comprises the following steps:
obtaining a UML activity diagram, and taking an activity node and a branch merging bar in the activity diagram as a vertex to obtain a directed graph corresponding to a concurrent model diagram; and for convenience of the subsequent description, the following definitions are made:
V＝{v i }，v i is the vertex on the activity diagram, i is a natural number, v s Denotes the starting vertex, v e Indicating an ending vertex.
E＝{e j }，e j Is directed edge connecting top points on the graph, j is natural number, and directed edge e j Starting point of (b) is v m End point is v n ，e j Represented as ordered pairs (v) m ,v n )。
End(e j )＝v n Represents an edge e j Ending at vertex v n ，Begin(e j )＝v m Represents an edge e j Start ofAt vertex v m 。
Owner(v i )＝o k Representing an activity v i Subject of is o k And move v i On the activity diagram at object o k In the lane of (2).
The specific information missing data production algorithm is described as follows:
go through the set of vertices V, if Owner (V) i )＝o k ，v i E is V, then V is i Adding Act (o) k ) Finally, a vertex set Act (o) of each object is obtained k )；
Go through the set of edges E, if End (E) j )＝v i ，Owner(Begin(e j ))≠Owner(v i )v i ∈V，e j E, then Owner (Begin (E) j ) Adding VR (v) i ) Finally, a set VR (v) of reference objects for each vertex is obtained i )；VR(v i ) Representing a vertex v i A set of direct reference objects;
selecting a process object in a reference object set of each vertex of the key objects in sequence (preferentially selecting processes which do not appear in other information missing data in the selection process) to form an object set (if the number of the objects in the object set is less than 2, then randomly selecting a process object to add into the set), and taking the vertex related to the object set and the key objects as a vertex to construct a vertex subgraph of the primary concurrent model graph;
and repeatedly constructing the vertex subgraphs until all the objects appear at least once, obtaining a plurality of subgraphs, and generating a plurality of information missing data by using a functional test clue generating method based on the UML activity graph to form an information missing data stream.
Example four
In this embodiment, step S40 in the second embodiment is described in detail, and the missing information data is repaired by using the test platform, where the repairing method includes:
first, setting
For missing data topological correlation point sets, l is a time series parameter,n' f is missing data point set size, t' zv Is the current time, k 'on the time series' fgj Is at time t' zv Complete data, λ 'without repair' dfy Is a radial basis function weight, p' xb Is the distance, t ', of the current marker point to its nearest neighbor marker point' sg A parameter matrix for a training network; e' afh Addresses are stored for missing data statistics.
Constructing a training relationship between the template data and the current missing data using the following equation:
second, set (x' i ,y' i ) Is missing sample, l' K Is the capacity, x 'of the missing sample' i Is the complete data sample value, y 'of the input' i Is an output value, r " sd Is empirical Risk, h' sg Is structural risk, e' sgjj The remaining attribute values for the missing samples.
Determining the appropriate number of hidden layer nodes from the data of the complete data sample using the following equation:
third, f 'is set' xbn Is the center of the basis function, σ' xv Is the variance of the radial basis function, jk' jkkl Is the variance of a Gaussian function, x p Is the p-th input sample, o ″) poi Is the number of all data attribute samples.
Establishing a nonlinear mapping model of the missing attribute and other attributes of the missing sample by using the following formula:
fourth, d 'is set' dfj Is hidden layer node center, k' dh The weight vectors for the hidden layer nodes are,
is the actual output, g ", of the j" th output node of the network corresponding to the input sample fgk The interval mapped for the data sample.
Solving the connection weight of the neuron between the hidden layer and the output layer by using the following formula:
fifthly, setting sigma' dfkk To call the function newrb, p' cbm For the structural information of the current missing data, the repairing of the information missing data in the high concurrency environment is completed by using the following formula:
in the first part, the missing data statistic storage address needs to be obtained by: according to a minimum counting/frequency summary method, two Hash function families which are independent of each other are given, data attribute values of information streams in a given time period are expressed and accumulated into a two-dimensional table data structure, a clustering function of current missing data attributes is calculated, and a conflict identification variable is introduced to calculate a missing data statistic value storage address. Specific acquisition methods are provided below:
step one, setting S' fgj For the data stream to be tested, { X er1 ,X er2 ,…,X ern Is S' fgj Each vector X represents a vector eri Including j-dimensional attributes
Is a self-domain set J K Value of (A), M k Is value field, h 'corresponding to k-dimension attribute' fj Is from set U' sd To aggregate W' sfjj Of hash function family, d' dg Are prime numbers.
Two separate Hash function families are given by:
and step two, setting delta as a probability parameter, and setting S (x, v) as a one-dimensional data stream.
D Hash functions (h) are uniformly and randomly selected from a Hash function family 1 ,h 2 ,...,h d ) And expressing and accumulating the data attribute values of the information flow in a given time period into a two-dimensional table data structure by using the following formula:
step three, setting h xc Is the historical statistical information v 'corresponding to the information key word x' sdjj Is the mapping result of the Hash function to x, b' cnkk Is the current attribute j 'of any missing data' zf Of certain attribute value u' xb Weight of d' sg As attribute value u i Statistic value of (1), E' sjk As attribute value u k The statistical value of (1).
The clustering function of the current missing data attribute is calculated using the following formula:
step four, setting X (u) i J) is the number of conflicts, k' zvkkl Historical summary statistics, u 'stored for a data structure' hjk The number of missing data storage units.
And (3) calculating a missing data statistic value storage address by introducing a conflict identification variable according to the following formula:
EXAMPLE five
An integrated test terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of an integrated test method as above when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of an integration testing method as set out above.
The memory may be used to store software programs and modules, and the processor may execute various functional applications of the terminal and data processing by operating the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an execution program required for at least one function, and the like.
The storage data area may store data created according to the use of the terminal, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to: the steps of an integrated testing method as above are implemented.
Without loss of generality, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instruction data structures, program modules or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media is not limited to the foregoing. The system memory and mass storage devices described above may be collectively referred to as memory.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/modes or examples and features of the various embodiments/modes or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It will be appreciated by those skilled in the art that the above embodiments are only for clarity of illustration of the invention, and are not intended to limit the scope of the invention. It will be apparent to those skilled in the art that other variations or modifications may be made on the above invention and still be within the scope of the invention.
Claims (10)
1. An integrated test platform, comprising: the system comprises a system pressure simulation module and an application safety simulation module;
the system pressure simulation module simulates concurrent data, outputs the concurrent data to the tested object and carries out pressure test on the system performance of the tested object;
and the application security simulation module is in communication connection with the application security vulnerability rule base, downloads the application security vulnerability to the application security simulation software, and outputs security threat data to the tested object after simulating the application security threat.
2. An integrated test method, based on the integrated test platform as claimed in claim 1, wherein the test method comprises a pressure test method and a safety test method; the pressure test method for the system performance comprises the following steps:
s10, generating an information missing data stream, wherein the information missing data stream comprises N information missing data;
s20, selecting n information missing data to obtain a data stream to be detected;
s30, inputting a data stream to be detected into a detected object, and obtaining first data after concurrent processing of the detected object;
s40, inputting the selected n information missing data into a test platform, and obtaining second data after the repair processing of the test platform;
s50, comparing the first data with the second data to obtain the similarity of the first data and the second data;
s60, if the similarity is larger than a set value, indicating that the pressure test is successful;
if the similarity is smaller than the set value, indicating that the pressure test fails;
s70, if the pressure test is successful, making n = n + n', and skipping to the step S20;
if the pressure test fails, making n = n-n', and jumping to step S20; n 'represents the pressure test span, n' is greater than 1;
s80, the steps S20 to S70 are repeated until the results of the two adjacent pressure tests are opposite;
s90, reducing n ', repeating the steps S20-S80 until n' =1, obtaining an n value when the pressure test under the condition of the step S80 succeeds, outputting the n value, and completing the pressure test;
the application safety testing method comprises the following steps:
s10, updating and acquiring security vulnerability information in a security vulnerability rule base;
s20, sequencing all the security vulnerability information, and numbering the security vulnerability information;
s30, let m =1;
s40, judging whether the simulation of all the security hole information is finished, if not, simulating the mth security hole information, and inputting the security hole information to the tested object;
if yes, go to step s70
s50; judging whether the tested object has corresponding security holes;
s60, if yes, jumping to step s70;
if not, making m = m +1, and skipping to the step s30;
and s70, outputting the test result.
3. The integration test method of claim 2, wherein in step S10, the method for generating the missing-information data stream comprises:
obtaining a UML activity diagram, and taking an activity node and a branch merging bar in the activity diagram as a vertex to obtain a directed graph corresponding to a concurrent model diagram;
go through the set of vertices V if Owner (V) i )＝o k ，v i E is V, then V is i Adding Act (o) k ) Finally, a vertex set of each object is obtained; owner (v) i )＝o k Representing an activity v i Subject of is o k And move v i On the activity diagram at object o k Within lanes of (e);
go through the set of edges E, if End (E) j )＝v i ，Owner(Begin(e j ))≠Owner(v i )v i ∈V，e j E, then Owner (Begin (E) j ) Adding VR (v) i ) Finally, a set of reference object sets of each vertex is obtained; end (e) j )＝v n Represents an edge e j Ending at vertex v n ，Begin(e j )＝v m Represents an edge e j Starting at vertex v m ，VR(v i ) Representing a vertex v i A set of direct reference objects;
selecting a process object from the reference object set of each vertex of the key objects in the sequence to form an object set, and constructing a vertex sub-graph of the primary concurrent model graph by taking the vertex related to the object set and the key objects as the vertex;
and repeatedly constructing the vertex subgraphs until all the objects appear at least once, obtaining a plurality of subgraphs, and generating a plurality of information missing data by using a functional test clue generation method based on the UML activity graph to form an information missing data stream.
4. The integration test method according to claim 2, wherein in step S40, the missing information data is repaired by the test platform, and the repair method includes:
acquiring a missing data statistic value storage address, and constructing a training relation between template data and current missing data;
determining the number of proper hidden layer nodes according to the data of the complete data sample;
establishing a nonlinear mapping model of the missing attributes and other attributes of the missing samples;
obtaining a connection weight of a neuron between the hidden layer and the output layer;
and finishing the repair of the information missing data.
5. The integration test method of claim 3, wherein the missing data statistics storage address obtaining method comprises:
two independent Hash function families are given according to a minimum counting/frequency summary method;
expressing and accumulating the data attribute values of the information flow in a given time period to a two-dimensional table data structure;
calculating a clustering function of the current missing data attribute;
and (4) introducing a conflict identification variable to calculate a missing data statistic value storage address.
6. The integration test method of claim 5, wherein the specific method for obtaining the missing data statistics storage address comprises:
two mutually independent Hash function families e' dg (f′ fg ) The calculation formula of (c) is:
wherein，S′ fgj For the data stream to be tested, { X er1 ,X er2 ,…,X ern Is S' fgj Each vector X represents a vector eri Including j-dimensional attributes
Is a self-domain set J K Value of (A), M k Is value domain, h 'corresponding to the k-dimension attribute' fj Is from set U' sd To aggregate W' sfjj Of hash function family, d' dg Is a prime number;
d Hash functions (h) are uniformly and randomly selected from a Hash function family 1 ,h 2 ,...,h d ) Accumulated to two-dimensional table data structure W' aq The calculation formula of (A) is as follows:
wherein δ is a probability parameter, and S (x, v) is a one-dimensional data stream;
the calculation formula of the clustering function for calculating the attribute of the current missing data is as follows:
wherein h is xc Is history statistical information v 'corresponding to the information keyword x' sdjj Is the mapping result of the Hash function to x, b' cnkk Is the current attribute j 'of any missing data' zf Of certain attribute value u' xb Weight of d' sg As attribute value u i Statistic value of (1), E' sjk As attribute value u k The statistical value of (a);
calculating missing data statistic value storage address e' afh The calculation formula of (A) is as follows:
wherein, X (u) i J) is the number of conflicts, k' zvkkl History stored for data structureSummary statistics, u' hjk The number of missing data storage units.
7. The integration test method according to claim 6, wherein the specific method for repairing missing information data comprises:
constructing a training relation between the template data and the current missing data as follows:
wherein it is present>
Is a missing data topological correlation point set, l is a time sequence parameter, n' f Is missing data point set size, t' zv Is the current time, k 'on the time series' fgj Is at time t' zv Complete data, λ 'without repair' dfy Is radial basis function weight, p' xb Is the distance, t ', of the current marker point to its nearest neighbor marker point' sg A parameter matrix for a training network;
the calculation formula for determining the appropriate number of hidden nodes is as follows:
wherein, (x' i ,y′ i ) Is missing sample, l' K Is the capacity, x 'of the missing sample' i Is an input complete data sample value, y' i Is an output value, r sd Is empirical Risk, h' sg Is structural risk, e' sgjj The remaining attribute value of the missing sample;
the nonlinear mapping model of the missing attribute and other attributes of the missing sample is as follows:
wherein, f' xbn Is the center of the basis function, σ' xv Is the variance of the radial basis function, jk' jkkl Is a variance of a gaussian function and is,x p is the p-th input sample, o ″) poi The number of all data attribute samples;
the calculation formula of the connection weight of the neuron between the hidden layer and the output layer is as follows:
wherein, d' dfj Is hidden layer node center, k' dh Weight vectors for hidden layer nodes>
Is the actual output, g ", of the j" th output node of the network corresponding to the input sample fgk An interval mapped for the data sample;
the formula for repairing missing data is:
wherein, σ' dfkk To call the function newrb, p' cbm Structure information of the current missing data.
8. An integrated test terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of an integrated test method according to any of claims 2-7.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of an integration testing method according to any one of claims 2 to 7.
10. An electronic device, comprising: at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to: the steps of implementing an integration testing method according to any one of claims 2 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117435505A (en) * | 2023-12-04 | 2024-01-23 | 南京易迪森信息技术有限公司 | Visual generation method of performance test script |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9454659B1 (en) * | 2014-08-15 | 2016-09-27 | Securisea, Inc. | Software vulnerabilities detection system and methods |
| CN107835982A (en) * | 2015-05-04 | 2018-03-23 | 赛义德·卡姆兰·哈桑 | Method and apparatus for management security in a computer network |
| CN108874637A (en) * | 2017-05-09 | 2018-11-23 | 北京京东尚科信息技术有限公司 | A kind of method of pressure test, system, electronic equipment and readable storage medium storing program for executing |
| US20200045073A1 (en) * | 2018-08-02 | 2020-02-06 | Rohde & Schwarz Gmbh & Co. Kg | Test system and method for identifying security vulnerabilities of a device under test |
| CN112182588A (en) * | 2020-10-22 | 2021-01-05 | 中国人民解放军国防科技大学 | Operating system vulnerability analysis and detection method and system based on threat intelligence |
| CN113656314A (en) * | 2021-08-19 | 2021-11-16 | 工银科技有限公司 | Pressure test processing method and device |
| CN114817022A (en) * | 2022-04-19 | 2022-07-29 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Railway electronic payment platform test method, system, equipment and storage medium |
| CN115033469A (en) * | 2022-06-20 | 2022-09-09 | 江苏银承网络科技股份有限公司 | Website system performance test method and device, equipment and storage medium |
-
2022
- 2022-12-31 CN CN202211741214.5A patent/CN115905023A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9454659B1 (en) * | 2014-08-15 | 2016-09-27 | Securisea, Inc. | Software vulnerabilities detection system and methods |
| CN107835982A (en) * | 2015-05-04 | 2018-03-23 | 赛义德·卡姆兰·哈桑 | Method and apparatus for management security in a computer network |
| CN108874637A (en) * | 2017-05-09 | 2018-11-23 | 北京京东尚科信息技术有限公司 | A kind of method of pressure test, system, electronic equipment and readable storage medium storing program for executing |
| US20200045073A1 (en) * | 2018-08-02 | 2020-02-06 | Rohde & Schwarz Gmbh & Co. Kg | Test system and method for identifying security vulnerabilities of a device under test |
| CN112182588A (en) * | 2020-10-22 | 2021-01-05 | 中国人民解放军国防科技大学 | Operating system vulnerability analysis and detection method and system based on threat intelligence |
| CN113656314A (en) * | 2021-08-19 | 2021-11-16 | 工银科技有限公司 | Pressure test processing method and device |
| CN114817022A (en) * | 2022-04-19 | 2022-07-29 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Railway electronic payment platform test method, system, equipment and storage medium |
| CN115033469A (en) * | 2022-06-20 | 2022-09-09 | 江苏银承网络科技股份有限公司 | Website system performance test method and device, equipment and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| 张利武: "基于UML活动图的并发软件测试方法研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》, no. 5, pages 1 - 12 * |
| 李晓南: "基于数据综合分析的软件安全漏洞静态检测平台设计与实现", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》, no. 12, pages 1 - 17 * |
| 金哲凡: "高并发环境下网络信息缺失数据修复方法仿真", 计算机仿真, vol. 34, no. 9, pages 1 - 10 * |
| 马宁: "《云计算关键技术》", 电子科技大学出版社, pages: 1 - 5 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117435505A (en) * | 2023-12-04 | 2024-01-23 | 南京易迪森信息技术有限公司 | Visual generation method of performance test script |
| CN117435505B (en) * | 2023-12-04 | 2024-03-15 | 南京易迪森信息技术有限公司 | Visual generation method of performance test script |
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