CN116401678B - Construction and extraction method of automobile information security test case - Google Patents
Construction and extraction method of automobile information security test case Download PDFInfo
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- 238000000605 extraction Methods 0.000 title claims abstract description 15
- 238000010276 construction Methods 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 49
- 230000011218 segmentation Effects 0.000 claims description 31
- 238000004364 calculation method Methods 0.000 claims description 8
- 108091026890 Coding region Proteins 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 238000011076 safety test Methods 0.000 claims 1
- 238000013500 data storage Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000002776 aggregation Effects 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3344—Query execution using natural language analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/34—Browsing; Visualisation therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/03—Indexing scheme relating to G06F21/50, monitoring users, programs or devices to maintain the integrity of platforms
- G06F2221/034—Test or assess a computer or a system
Abstract
The invention provides a construction and extraction method of automobile information security test cases, which is used for constructing a test case knowledge graph aiming at a plurality of test cases in a test case set and the atomic operation of each test case; the method comprises the steps of performing an encoding method on atomic operations in a test case knowledge graph, and constructing a test case execution path in an encoding method; the invention can improve the data storage efficiency of the big data test case and the extraction efficiency of the test case execution step.
Description
Technical Field
The invention relates to the technical field of data structures and organizations, in particular to a method for constructing and extracting automobile information security test cases.
Background
Along with the popularization of strong marks and push marks in the aspect of automobile information safety in China, more and more automobile enterprises are in increasing demands on information safety detection of automobiles at home. Based on the requirement, a whole vehicle test bed system is built, a plurality of test cases of automobile information safety can be integrated in the system, a tester tests according to the test cases, the automobile information safety detection flow can be more standard, the detection result is more accurate, and the automobile can be more fit with the national standard in the aspect of information safety. The invention provides a method for generating test cases, which plays an important role in the whole system, and better organizes and extracts the test cases after the test cases are continuously increased.
Disclosure of Invention
Aiming at the increasing number of current test cases, in order to realize better construction, extraction and management of the test cases, a construction and extraction method of the automobile information security test cases is provided, and the management method specifically comprises the following steps:
s1, acquiring each piece of test case data in a test case library, wherein each piece of test case data comprises a first atomic operation set { X1, X2 …, xn }; wherein n is the number of first atomic operations in the first atomic operation set corresponding to the test case data, and n is more than 1;
the data format of the first atomic operation is: the name of the test case, the step number and the text description of the test step;
s2, merging the first atomic operation sets in each piece of test case data in the step S1 to form a first atomic operation total set { X1, X2 …, xm }; the m is the sum of the first atomic operation number of the test case data;
s3, performing first clustering operation on the first atomic operation total set in the step S2 to form a plurality of first clustering sets;
s4, acquiring a plurality of first cluster sets in the step S3, and performing de-duplication processing on a plurality of first atomic operations in each first cluster set to form a plurality of second cluster sets;
S5, acquiring a plurality of second clustering sets in the step S4, and performing second clustering operation on each second clustering set to form a plurality of third clustering sets;
s6, performing atomic segmentation on the first atomic operation in the third clustering sets according to the third clustering sets in the step S5 to form a second atomic operation total set;
s7, constructing a test case knowledge graph according to the second atomic operation total set in the step S6;
s8, extracting the test cases according to the test case knowledge graph in the step S7.
Further, the step S3 performs a first clustering operation on the first atomic operation total set in the step S2 to form a plurality of first clustering sets, and specifically includes the following steps:
s31, obtaining a relevance value between each first atomic operation;
s32, clustering a plurality of first atomic operations in the total set of the first atomic operations by adopting a K-mes clustering method according to the relevance value among the first atomic operations in the step S31 to form a plurality of first clustering sets;
the first clustering sets each store a plurality of first atomic operations.
Further, the step S31 specifically includes the following steps:
s311, acquiring first atomic operations Xi and first atomic operations Xj in a first atomic operation total set, and calculating a semantic similarity value R of the first atomic operations Xi and the first atomic operations Xj by using a word2vec method ij ；
S312, obtaining step number B in the first atomic operation Xi xi And the name N of the test case xi The method comprises the steps of carrying out a first treatment on the surface of the Step number B of the first atomic operation Xj is obtained simultaneously xj And the name N of the test case xj ；
S313 step number B according to the first atomic operation Xi xi And step numbers of the first atomic operation Xj judge whether Xi and Xj have a previous step number and a subsequent step number;
when the first atomic operation Xi and the first atomic operation Xj have the previous step number, acquiring a first atomic operation X (i-1) corresponding to the previous step number of the first atomic operation Xi, and acquiring a first atomic operation X (j-1) corresponding to the previous step number of the first atomic operation Xj; calculating semantic similarity value R of first atomic operation X (i-1) and first atomic operation X (j-1) by word2vec method （i-1）(j-1) ；
When the first atomic operation Xi and the first atomic operation Xj have the subsequent step numbers, acquiring a first atomic operation X (i+1) corresponding to the previous step number of the first atomic operation Xi, and acquiring a first atomic operation X (j+1) corresponding to the previous step number of the first atomic operation Xj; using woThe rd2vec method calculates the semantic similarity value R of the first atomic operation X (i+1) and the first atomic operation X (j+1) （i+1）(j+1) ；
When the first atomic operation Xi and the first atomic operation Xj both have the previous step number and the next step number, a word2vec method is adopted to calculate the semantic similarity value R of the first atomic operation X (i-1) and the first atomic operation X (j-1) （i-1）(j-1) And calculating the semantic similarity value R of the first atomic operation X (i+1) and the first atomic operation X (j+1) by using word2vec method （i+1）(j+1) ；
S314, test case name N according to first atomic operation Xi xi And test case name N for first atomic operation Xj xj Acquiring test case name N xi The number Np of the first atomic operations in the corresponding first atomic operation set is used for obtaining the name N of the test case xj The number Nq of the first atomic operations in the corresponding first atomic operation set;
when np=nq, the word2vec method is adopted to calculate the name N of the test case xi And test case name N xi Semantic similarity value R of (2) pq ；
S315, according to the semantic similarity value R ij 、R （i-1）(j-1) 、R （i+1）(j+1) 、R pq Calculating the association degree value of the first atomic operation Xi and the first atomic operation Xj;
s316, repeatedly executing the steps S311 to S315 until the calculation of any two first atomic operation association degree values in the first atomic operation total set is completed.
Further, the step S315 specifically includes:
Wherein G is ij A relevance value for the first atomic operation Xi and the first atomic operation Xj;is a weight value.
Further, the step S4 obtains a plurality of first cluster sets in the step S3, and performs deduplication processing on a plurality of first atomic operations in each first cluster set to form a plurality of second cluster sets, which specifically includes the following steps:
s41, acquiring one first cluster set S in a plurality of first cluster sets in S3, deleting one of the two first atomic operations when the semantic similarity value between the two first atomic operations in the first cluster set S is more than 95%, and adding the deleted test case name of the first atomic operation to the test case name of the reserved first atomic operation to finally form a second cluster set;
s42, repeatedly executing the step S41 until each first cluster set in the plurality of first cluster sets is subjected to de-duplication processing, so as to form a plurality of second cluster sets.
Further, the step S5 obtains a plurality of second cluster sets in the step S4, and performs a second clustering operation on each second cluster set to form a plurality of third cluster sets, which specifically includes the following steps:
S51, aiming at each second clustering set, performing second clustering operation on the first atomic operations in the second clustering set by adopting a K-means method according to semantic similarity values among the first atomic operations in the second clustering set;
s52, repeatedly executing the step S51 until each second cluster set in the plurality of second cluster sets completes second clustering operation to form a plurality of third cluster sets.
Further, the step S6 performs atomic segmentation on the first atomic operations in the plurality of third cluster sets according to the plurality of third cluster sets in the step S5 to form a second atomic operation total set, and specifically includes:
s61, selecting a third clustering set from a plurality of third clustering sets;
s62, recording the two first atomic operations in the segmentation data table when the semantic similarity value of the two first atomic operations in the selected third clustering set is greater than 80%;
s63, repeatedly executing the step S62 until any two first atomic operations meeting the condition that the semantic similarity value is more than 80% in the selected third clustering set are recorded in a segmentation data table;
s64, feeding the segmentation data table obtained in the step S63 back to a user, segmenting the data table according to a plurality of first atomic operations recorded in the segmentation data table by the user to form a second atomic operation set, and merging the plurality of first atomic operations according to segmentation results;
S65, repeatedly executing the steps S61 to S64 until all third class sets are processed, and completing the combination of all first atomic operations and forming a plurality of second atomic operation sets;
s66, combining the plurality of second atomic operation sets formed in the step S65 to form a second atomic operation total set; combining all the first atomic operations combined in the step S66 to form a combined first atomic operation total set;
s67, coding each first atomic operation in the first atomic operation total set, and continuing to code each second atomic operation in the second atomic operation total set according to the coding sequence after the first atomic operation coding is completed;
the code is a three-bit hexadecimal value.
Further, the step S7 constructs a test case knowledge graph according to the second atomic operation total set in the step S6, and specifically includes the following steps:
s71, acquiring a test case Ci from a plurality of test cases in a test case library, and taking the name of the test case Ci as a root node of a test case knowledge graph;
s72, searching all first atomic operations of which the names of the test cases belong to in the first atomic operation total set formed in the step S67, taking the searched first atomic operations as intermediate nodes, and connecting the intermediate nodes to root nodes of the test case knowledge graph corresponding to the names of the test cases Ci;
S73, searching all second atomic operations of which the test case names are equal to the test case Ci in a second atomic operation set, taking the searched second atomic operations as leaf nodes, and connecting the leaf nodes to intermediate nodes corresponding to the upper first atomic operations associated with the second atomic operations;
s74, constructing codes of execution paths of the test cases Ci according to execution steps of a plurality of first atomic operations in the test cases Ci, and writing the codes of the execution paths into root nodes in a test case knowledge graph corresponding to the test cases Ci;
s75, repeatedly executing the steps S71 to S74 until the construction of all test cases in the test case library in the knowledge graph is completed.
S76, deleting the test case names and step numbers in each first atomic operation in the test case knowledge graph, and only reserving codes corresponding to the first atomic operation and test step text description; and deleting the test case name, the step number and the related upper first atomic operation in each second atomic operation in the test case knowledge graph, and only reserving the corresponding codes and the text description of the test steps.
Further, the step S74 constructs an execution path code of the test case Ci according to the execution steps of the plurality of first atomic operations in the test case Ci, and writes the execution path code into a root node in the test case knowledge graph corresponding to the test case Xi, which specifically includes:
S741. set k=1;
s742, acquiring a first atomic operation set { X1, X2 … Xk, … Xu } of the test case Ci; wherein Xk is the kth first atomic operation, namely the kth operation step of the test case; u is the total number of first atomic operations included in the test case Ci, i.e., the total number of operation steps included in the test case Ci;
s743, aiming at the first atomic operation Xk, acquiring the code B of the first atomic operation k Judging whether a connected leaf node exists in the intermediate node corresponding to the first atomic operation; if so, finding the test case name in the second atomic operation corresponding to the connected leaf nodes and the test in step S742A second atomic operation of the same name of the test case Ci and adding the code of the second atomic operation to B k Afterwards;
if not, add the encoding of the first atomic operation X (k+1) to B 1 Afterwards;
setting k=k+1;
s744, repeating the step S743 until all first atomic operations in the test case Ci are processed to form an execution path code;
s745, writing the execution path code into a root node in the test case knowledge graph corresponding to the test case Xi.
Further, the step S8 extracts the test case according to the test case knowledge graph in the step S7, and specifically includes the following steps:
S81, acquiring an execution path code stored in a root node corresponding to a test case in a test case knowledge graph;
s82, dividing the execution path code in the step S81 according to the unit of three bits;
s83, acquiring a three-bit code, and searching a first atomic operation corresponding to the three-bit code in the test case knowledge graph;
s84, judging whether a node corresponding to the first atomic operation has a lower node, if so, searching for the next three-bit code in the lower node;
s85, repeating the steps S83 to S84 from the first three-bit code according to the sequence of the path codes which are segmented in the step S82 until the node position corresponding to the last three-bit code in the test case knowledge graph is obtained, and completing the extraction of the test case.
The beneficial effects of the invention are as follows:
1. in the invention, the first atomic operation in the test case is subjected to the aggregation treatment by adopting a twice clustering method, so that the accuracy of the segmentation of the test case operation steps is improved; meanwhile, the efficiency of data storage of operation steps in the test case can be further improved;
2. the first clustering method in the invention adoptsSemantic similarity value R ij 、R （i-1）(j-1) 、R （i+1）(j+1) 、R pq And the calculation of the first atomic operation association degree is carried out, so that the accuracy of the calculation of the first atomic operation association degree is improved, and the accuracy of the first atomic operation clustering is improved.
3. According to the invention, the storage of the execution path of the test case is stored in the coding mode, so that a user can quickly complete the extraction of the test case steps according to the coding, and the construction of the test case and the execution efficiency of an extraction system are improved.
4. The method for constructing the targeted test case knowledge graph greatly reduces the storage efficiency of the data of the large-data test case and improves the data extraction efficiency of the test case execution step.
The foregoing description is only an overview of the present invention, and is intended to be more clearly understood as the present invention, as it is embodied in the following description, and is intended to be more clearly understood as the following description of the preferred embodiments, given in detail, of the present invention, along with other objects, features and advantages of the present invention.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flow chart of a method for constructing and extracting an automobile information security test case.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the description of the present invention, unless explicitly stated and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, connected, detachably connected, or integrated; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention provides a construction and extraction method of an automobile information security test case, and the management method specifically comprises the following steps:
s1, acquiring each piece of test case data in a test case library, wherein each piece of test case data comprises a first atomic operation set { X1, X2 …, xn }; wherein n is the number of first atomic operations in the first atomic operation set corresponding to the test case data, and n is more than 1;
the data format of the first atomic operation is: the name of the test case, the step number and the text description of the test step;
for example, the test case name is "authenticity and integrity check test case", which includes the following test steps:
step 1: connecting equipment to be tested, and designating adb wifi connection;
step 2: inputting a remote control system security log directory address, and extracting an original log folder;
step 3: transmitting forged remote control instructions or remote control instructions for falsifying part of parameters to the system by using the designated authority;
step 4: extracting the log file again under the audit directory address, comparing with the original log file, and listing the newly added log file and the log file information with the newly added content;
Step 5: the modified log file or all log files are analyzed.
The first atomic operation set corresponding to the 'authenticity and integrity check test case' is { X1, X2 …, X5 }, wherein the first atomic operation X1 corresponds to step 1; the first atomic operation X5 corresponds to step 5.
The data format of the first atomic operation X1 is: the method comprises the steps of verifying the authenticity and the integrity, checking test cases, 1, connecting equipment to be tested, and designating adb wifi connection.
S2, merging the first atomic operation sets in each piece of test case data in the step S1 to form a first atomic operation total set { X1, X2 …, xm }; the m is the sum of the first atomic operation number of the test case data;
s3, performing first clustering operation on the first atomic operation total set in the step S2 to form a plurality of first clustering sets;
specifically, the step S3 performs a first clustering operation on the first atomic operation total set in the step S2 to form a plurality of first clustering sets, and specifically includes the following steps:
s31, obtaining a relevance value between each first atomic operation;
specifically, the step S31 specifically includes the following steps:
s311, acquiring first atomic operations Xi and first atomic operations Xj in a first atomic operation total set, and calculating a semantic similarity value R of the first atomic operations Xi and the first atomic operations Xj by using a word2vec method ij ；
The semantic similarity value R of the first atomic operation Xi and the first atomic operation Xj ij According to the test step text description of the first atomic operation Xi and the test step text description of the first atomic operation Xj, a word2vec method is adopted for calculation.
S312, obtaining step number B in the first atomic operation Xi xi And the name N of the test case xi The method comprises the steps of carrying out a first treatment on the surface of the Step number B of the first atomic operation Xj is obtained simultaneously xj And the name N of the test case xj ；
S313 step number B according to the first atomic operation Xi xi And the step number of the first atomic operation Xj to determine whether there is a previous step number and a next step number for Xi and XjStep numbering;
taking the example of "authenticity and integrity check test case" which contains 5 steps in total, the first atomic operation X3 has a previous step number 2 and a subsequent step number 4.
When the first atomic operation Xi and the first atomic operation Xj have the previous step number, acquiring a first atomic operation X (i-1) corresponding to the previous step number of the first atomic operation Xi, and acquiring a first atomic operation X (j-1) corresponding to the previous step number of the first atomic operation Xj; calculating semantic similarity value R of first atomic operation X (i-1) and first atomic operation X (j-1) by word2vec method （i-1）(j-1) ；
When the first atomic operation Xi and the first atomic operation Xj have the subsequent step numbers, acquiring a first atomic operation X (i+1) corresponding to the previous step number of the first atomic operation Xi, and acquiring a first atomic operation X (j+1) corresponding to the previous step number of the first atomic operation Xj; calculating semantic similarity value R of first atomic operation X (i+1) and first atomic operation X (j+1) by using word2vec method （i+1）(j+1) ；
When the first atomic operation Xi and the first atomic operation Xj both have the previous step number and the next step number, a word2vec method is adopted to calculate the semantic similarity value R of the first atomic operation X (i-1) and the first atomic operation X (j-1) （i-1）(j-1) And calculating the semantic similarity value R of the first atomic operation X (i+1) and the first atomic operation X (j+1) by using word2vec method （i+1）(j+1) ；
S314, test case name N according to first atomic operation Xi xi And test case name N for first atomic operation Xj xj Acquiring test case name N xi The number Np of the first atomic operations in the corresponding first atomic operation set is used for obtaining the name N of the test case xj The number Nq of the first atomic operations in the corresponding first atomic operation set;
taking "authenticity and integrity check test cases" as an example, the number of first atomic operations in the corresponding first atomic operation set is 5.
When np=nq, the word2vec method is adopted to calculate the name N of the test case xi And test case name N xi Semantic similarity value R of (2) pq ；
S315, according to the semantic similarity value R ij 、R （i-1）(j-1) 、R （i+1）(j+1) 、R pq Calculating the association degree value of the first atomic operation Xi and the first atomic operation Xj;
the specific address, the step S315 specifically includes:
wherein G is ij A relevance value for the first atomic operation Xi and the first atomic operation Xj;is a weight value.
Wherein the method comprises the steps of。
When R is ij 、R （i-1）(j-1) 、R （i+1）(j+1) 、R pq Setting R when the numerical value does not exist ij 、R （i-1）(j-1) 、R （i+1）(j+1) 、R pq The values are all 0.
S316, repeatedly executing the steps S311 to S315 until the calculation of any two first atomic operation association degree values in the first atomic operation total set is completed.
S32, clustering a plurality of first atomic operations in the total set of the first atomic operations by adopting a K-mes clustering method according to the relevance value among the first atomic operations in the step S31 to form a plurality of first clustering sets;
the first clustering sets each store a plurality of first atomic operations.
S4, acquiring a plurality of first cluster sets in the step S3, and performing de-duplication processing on a plurality of first atomic operations in each first cluster set to form a plurality of second cluster sets;
specifically, the step S4 obtains a plurality of first cluster sets in the step S3, performs deduplication processing on a plurality of first atomic operations in each first cluster set, and forms a plurality of second cluster sets, and specifically includes the following steps:
S41, acquiring one first cluster set S in a plurality of first cluster sets in S3, deleting one of the two first atomic operations when the semantic similarity value between the two first atomic operations in the first cluster set S is more than 95%, and adding the deleted test case name of the first atomic operation to the test case name of the reserved first atomic operation to finally form a second cluster set;
for example, in the first step of "the test case for authenticity and integrity check" and "the test for remote control instruction control", that is, the first atomic operations X1 are "connect to the device under test", and make adb wifi connection ", then one of the two first atomic operations is deleted, for example, the first atomic operation of the test case for" the test case for remote control instruction control "is deleted, the first atomic operation of the test case for authenticity and integrity check" is reserved, and the "remote control instruction control test" is added to the "name of the test case to which the first atomic operation of the test case for authenticity and integrity check" belongs ".
S42, repeatedly executing the step S41 until each first cluster set in the plurality of first cluster sets is subjected to de-duplication processing, so as to form a plurality of second cluster sets.
S5, acquiring a plurality of second clustering sets in the step S4, and performing second clustering operation on each second clustering set to form a plurality of third clustering sets;
specifically, the step S5 obtains a plurality of second cluster sets in step S4, and performs a second clustering operation on each second cluster set to form a plurality of third cluster sets, and specifically includes the following steps:
s51, aiming at each second clustering set, performing second clustering operation on the first atomic operations in the second clustering set by adopting a K-means method according to semantic similarity values among the first atomic operations in the second clustering set;
s52, repeatedly executing the step S51 until each second cluster set in the plurality of second cluster sets completes second clustering operation to form a plurality of third cluster sets.
Wherein each third collection of categories stores a plurality of first atomic operations.
It should be noted that, the first clustering and the second clustering both adopt the K-means algorithm to perform clustering operation, and perform de-duplication operation after the first clustering, and then perform the second clustering, so that the clustering result is more accurate.
And for the first clustering: the first atomic operations are stored in the first atomic operation total set (the part of the first atomic operation between the step S1 and the step S2 is explained), each two first atomic operations in the first atomic operation total set have a relevance value, a first clustering is performed on the first atomic operations in the first atomic operation total set according to the relevance value (a conventional K-means algorithm is adopted), but in each clustering set formed after the first clustering, a first atomic operation with very high similarity exists, so that after the de-duplication processing is performed on each clustering set after the first clustering, a second clustering operation is performed, and still clustering is performed according to the relevance value.
S6, performing atomic segmentation on the first atomic operation in the third clustering sets according to the third clustering sets in the step S5 to form a second atomic operation total set;
specifically, the step S6 performs atomic segmentation on the first atomic operations in the plurality of third cluster sets according to the plurality of third cluster sets in the step S5 to form a second atomic operation total set, and specifically includes:
s61, selecting a third clustering set from a plurality of third clustering sets;
s62, recording the two first atomic operations in the segmentation data table when the semantic similarity value of the two first atomic operations in the selected third clustering set is greater than 80%;
for example, the third cluster set includes 6 first atomic operations, namely a, B, C, D, E, F, and when the semantic similarity value of a and B is greater than 80%, recording a and B into the segmentation data table;
continuing to execute, and recording the C and the D into a segmentation data table when the semantic similarity value of the C and the D is more than 80%;
continuing to execute, and recording E into the segmentation data table when the semantic similarity value of E and A is greater than 80%;
continuing to execute, when the semantic similarity value of any one of F and A, B, C, D, E is not more than 80%, not recording F into the segmentation data table.
S63, repeatedly executing the step S62 until any two first atomic operations meeting the condition that the semantic similarity value is more than 80% in the selected third clustering set are recorded in a segmentation data table;
s64, feeding the segmentation data table obtained in the step S63 back to a user, segmenting the data table according to a plurality of first atomic operations recorded in the segmentation data table by the user to form a second atomic operation set, and merging the plurality of first atomic operations according to segmentation results;
specifically, merging the names and step numbers of the test cases of the first atomic operations according to the text description of the test steps of the first atomic operations;
setting the data format of the second atomic operation as: the name of the test case, the step number, the text description of the test step and the associated upper first atomic operation;
for example, three first atomic operations are recorded in the segmentation data table, where the first atomic operations are respectively: "test case for authenticity and integrity check", 1, "connect device under test", specify adb wifi connection "; "remote control instruction control test", 1, "connect to the apparatus to be measured", formulate ssh connection "; "unauthorized test case prevention", 1, "connect device under test", formulate adb wifi connection ". The user performs segmentation on the three first atomic operations, modifies the two first atomic operations according to the segmentation result, and finally forms the following results:
Merging three first atomic operations into one first atomic operation: { "authenticity and integrity check test case", "remote control instruction control test", "unauthorized test case" }, {1, 1}, "connect device under test";
two second atomic operations are formed as a second set of atomic operations, respectively: { "Authenticity and integrity check test case", "anti-unauthorized test case" }, {1, 1}, "specified adb wifi connection", "connect device under test"; { "remote control instruction control test" }, {1}, "specify ssh connection", "connect device under test";
wherein the literal description of the associated superior first atomic operation is stored in the "associated superior first atomic operation" in the second atomic operation.
S65, repeatedly executing the steps S61 to S64 until all third class sets are processed, and completing the combination of all first atomic operations and forming a plurality of second atomic operation sets;
s66, combining the plurality of second atomic operation sets formed in the step S65 to form a second atomic operation total set; combining all the first atomic operations combined in the step S66 to form a combined first atomic operation total set;
S67, coding each first atomic operation in the first atomic operation total set, and continuing to code each second atomic operation in the second atomic operation total set according to the coding sequence after the first atomic operation coding is completed;
the code is a three-bit hexadecimal value.
For example, the first atomic operation aggregate includes three first atomic operations a, B, C; the second atomic operation total set includes two second atomic operations D, E, and then a, B, and C are respectively numbered as: 001,002,003, and D, E are numbered: 004,005.
S7, constructing a test case knowledge graph according to the second atomic operation total set in the step S6;
specifically, the step S7 constructs a test case knowledge graph according to the second atomic operation total set in the step S6, and specifically includes the following steps:
s71, acquiring a test case Ci from a plurality of test cases in a test case library, and taking the name of the test case Ci as a root node of a test case knowledge graph;
s72, searching all first atomic operations of which the names of the test cases belong to in the first atomic operation total set formed in the step S67, taking the searched first atomic operations as intermediate nodes, and connecting the intermediate nodes to root nodes of the test case knowledge graph corresponding to the names of the test cases Ci;
If the test case Ci is an "authenticity and integrity check test case", the first atomic operation set formed in step S67 finds that the first atomic operation { "authenticity and integrity check test case", "remote control instruction control test", "unauthorized test case" }, {1, 1}, the test case name "authenticity and integrity check test case" in the "connection test device" is equal to the name of the test case Ci, and the first atomic operation { "authenticity and integrity check test case", "remote control instruction control test", "unauthorized test case" }, {1, 1}, the "connection test device" is used as an intermediate node, and is connected to the root node of the test case knowledge graph corresponding to the name of the test case Ci.
S73, searching all second atomic operations of which the test case names are equal to the test case Ci in a second atomic operation set, taking the searched second atomic operations as leaf nodes, and connecting the leaf nodes to intermediate nodes corresponding to the upper first atomic operations associated with the second atomic operations;
for example, if the test case Ci is an "authenticity and integrity check test case", the second atomic operation { "authenticity and integrity check test case", "anti-unauthorized test case" }, {1, 1}, "designated adb wifi connection", "test case name in the connection test device" is equal to the name of the test case Ci, and the second atomic operation { "authenticity and integrity check test case", "anti-unauthorized test case" }, {1, 1}, "designated adb wifi connection", "connection test device" is connected to the associated upper first atomic operation: { "test case for authenticity and integrity check", "remote control instruction control test", "test case for unauthorized prevention" }, {1, 1}, "connect device under test", corresponding intermediate node.
S74, constructing codes of execution paths of the test cases Ci according to execution steps of a plurality of first atomic operations in the test cases Ci, and writing the codes of the execution paths into root nodes in a test case knowledge graph corresponding to the test cases Ci;
specifically, the step S74 specifically includes:
s741. set k=1;
s742, acquiring a first atomic operation set { X1, X2 … Xk, … Xu } of the test case Ci; wherein Xk is the kth first atomic operation, namely the kth operation step of the test case; u is the total number of first atomic operations included in the test case Ci, i.e., the total number of operation steps included in the test case Ci;
s743, aiming at the first atomic operation Xk, acquiring the code B of the first atomic operation k Judging whether a connected leaf node exists in the intermediate node corresponding to the first atomic operation; if so, finding a second atomic operation whose test case name is the same as the test case Ci in step S742 in the second atomic operation corresponding to the plurality of connected leaf nodes, and adding the code of the second atomic operation to B k Afterwards;
if not, add the encoding of the first atomic operation X (k+1) to B 1 Afterwards;
setting k=k+1;
s744, repeating the step S743 until all first atomic operations in the test case Ci are processed to form an execution path code;
S745, writing the execution path code into a root node in the test case knowledge graph corresponding to the test case Xi.
S75, repeatedly executing the steps S71 to S74 until the construction of all test cases in the test case library in the knowledge graph is completed.
S76, deleting the test case names and step numbers in each first atomic operation in the test case knowledge graph, and only reserving codes corresponding to the first atomic operation and test step text description; and deleting the test case name, the step number and the related upper first atomic operation in each second atomic operation in the test case knowledge graph, and only reserving the corresponding codes and the text description of the test steps.
S8, extracting the test cases according to the test case knowledge graph in the step S7.
The specific address, the step S8 specifically includes:
s81, acquiring an execution path code stored in a root node corresponding to a test case in a test case knowledge graph;
s82, dividing the execution path code in the step S81 according to the unit of three bits;
see step S67, where the code is a three-bit hexadecimal value, so here the segmentation is performed in units of three bits, so that the searching of the nodes in the knowledge-graph can be performed according to the code.
For the knowledge graph, step S71 to step S74 are descriptions of detailed knowledge graph construction. In the knowledge graph, each node corresponds to a first atomic operation, and each node or each first atomic operation corresponds to a three-bit code. After the execution path codes are obtained from the root node, the execution path codes are segmented according to three bits, and then each node or each first atomic operation corresponding to the whole execution path according to the execution sequence can be obtained.
S83, acquiring a three-bit code, and searching a first atomic operation corresponding to the three-bit code in the test case knowledge graph;
s84, judging whether a node corresponding to the first atomic operation has a lower node, if so, searching for the next three-bit code in the lower node;
if not, searching a first atomic operation corresponding to the next three-bit code in the test case knowledge graph;
s85, repeating the steps S83 to S84 from the first three-bit code according to the sequence of the path codes which are segmented in the step S82 until the node position corresponding to the last three-bit code in the test case knowledge graph is obtained, and completing the extraction of the test case.
The beneficial effects of the invention are as follows:
1. in the invention, the first atomic operation in the test case is subjected to the aggregation treatment by adopting a twice clustering method, so that the accuracy of the segmentation of the test case operation steps is improved; meanwhile, the efficiency of data storage of operation steps in the test case can be further improved;
2. the first clustering method in the invention adopts a semantic similarity value R ij 、R （i-1）(j-1) 、R （i+1）(j+1) 、R pq And the calculation of the first atomic operation association degree is carried out, so that the accuracy of the calculation of the first atomic operation association degree is improved, and the accuracy of the first atomic operation clustering is improved.
3. According to the invention, the storage of the execution path of the test case is stored in the coding mode, so that a user can quickly complete the extraction of the test case steps according to the coding, and the construction of the test case and the execution efficiency of an extraction system are improved.
4. The method for constructing the targeted test case knowledge graph greatly reduces the storage efficiency of the data of the large-data test case and improves the data extraction efficiency of the test case execution step.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. The construction and extraction method of the automobile information safety test case is characterized by comprising the following steps:
s1, acquiring test case data in a test case library, wherein each piece of test case data comprises a first atomic operation set { X1, X2 …, xn }; wherein n is the number of first atomic operations in the first atomic operation set corresponding to the test case data, and n is more than 1;
the data format of the first atomic operation is: the name of the test case, the step number and the text description of the test step;
s2, merging the first atomic operation sets in each piece of test case data in the step S1 to form a first atomic operation total set { X1, X2 …, xm }; the m is the sum of the first atomic operation number of the test case data;
s3, performing first clustering operation on the first atomic operation total set in the step S2 to form a plurality of first clustering sets;
s4, acquiring a plurality of first cluster sets in the step S3, and performing de-duplication processing on a plurality of first atomic operations in each first cluster set to form a plurality of second cluster sets;
s5, acquiring a plurality of second clustering sets in the step S4, and performing second clustering operation on each second clustering set to form a plurality of third clustering sets;
S6, performing atomic segmentation on the first atomic operation in the third clustering sets according to the third clustering sets in the step S5 to form a second atomic operation total set;
s7, constructing a test case knowledge graph according to the second atomic operation total set in the step S6;
s8, extracting test cases according to the test case knowledge graph in the step S7;
the step S6 is performed according to the plurality of third cluster sets in the step S5, and atomic segmentation is performed on the first atomic operations in the plurality of third cluster sets to form a second atomic operation total set, and specifically includes:
s61, selecting a third clustering set from a plurality of third clustering sets;
s62, recording the two first atomic operations in the segmentation data table when the semantic similarity value of the two first atomic operations in the selected third clustering set is greater than 80%;
s63, repeatedly executing the step S62 until any two first atomic operations meeting the condition that the semantic similarity value is more than 80% in the selected third clustering set are recorded in a segmentation data table;
s64, feeding the segmentation data table obtained in the step S63 back to a user, segmenting the data table according to a plurality of first atomic operations recorded in the segmentation data table by the user to form a second atomic operation set, and merging the plurality of first atomic operations according to segmentation results;
S65, repeatedly executing the steps S61 to S64 until all third class sets are processed, and completing the combination of all first atomic operations and forming a plurality of second atomic operation sets;
s66, combining the plurality of second atomic operation sets formed in the step S65 to form a second atomic operation total set; combining all the first atomic operations combined in the step S66 to form a combined first atomic operation total set;
s67, coding each first atomic operation in the first atomic operation total set, and continuing to code each second atomic operation in the second atomic operation total set according to the coding sequence after the first atomic operation coding is completed;
the code is a three-bit hexadecimal value;
the step S7 is to construct a test case knowledge graph according to the second atomic operation total set in the step S6, and specifically comprises the following steps:
s71, acquiring a test case Ci from a plurality of test cases in a test case library, and taking the name of the test case Ci as a root node of a test case knowledge graph;
s72, searching all first atomic operations of which the names of the test cases belong to in the first atomic operation total set formed in the step S67, taking the searched first atomic operations as intermediate nodes, and connecting the intermediate nodes to root nodes of the test case knowledge graph corresponding to the names of the test cases Ci;
S73, searching all second atomic operations of which the test case names are equal to the test case Ci in a second atomic operation set, taking the searched second atomic operations as leaf nodes, and connecting the leaf nodes to intermediate nodes corresponding to the upper first atomic operations associated with the second atomic operations;
s74, constructing codes of execution paths of the test cases Ci according to execution steps of a plurality of first atomic operations in the test cases Ci, and writing the codes of the execution paths into root nodes in a test case knowledge graph corresponding to the test cases Ci;
s75, repeatedly executing the steps S71 to S74 until the construction of all test cases in the test case library in the knowledge graph is completed;
s76, deleting the test case names and step numbers in each first atomic operation in the test case knowledge graph, and only reserving codes corresponding to the first atomic operation and test step text description; and deleting the test case name, the step number and the related upper first atomic operation in each second atomic operation in the test case knowledge graph, and only reserving the corresponding codes and the text description of the test steps.
2. The method for constructing and extracting the automobile information security test case according to claim 1, wherein the step S3 performs a first clustering operation on the first atomic operation collection in the step S2 to form a plurality of first clustering sets, and specifically includes the following steps:
S31, obtaining a relevance value between each first atomic operation;
s32, clustering a plurality of first atomic operations in the total set of the first atomic operations by adopting a K-mes clustering method according to the relevance value among the first atomic operations in the step S31 to form a plurality of first clustering sets;
the first clustering sets each store a plurality of first atomic operations.
3. The method for constructing and extracting the automobile information security test case according to claim 2, wherein the step S31 specifically includes the following steps:
s311, acquiring first atomic operations Xi and first atomic operations Xj in a first atomic operation total set, and calculating a semantic similarity value R of the first atomic operations Xi and the first atomic operations Xj by using a word2vec method ij ；
S312, obtaining step number B in the first atomic operation Xi xi And the name N of the test case xi The method comprises the steps of carrying out a first treatment on the surface of the Step number B of the first atomic operation Xj is obtained simultaneously xj And the name N of the test case xj ；
S313 step number B according to the first atomic operation Xi xi And step numbers of the first atomic operation Xj judge whether Xi and Xj have a previous step number and a subsequent step number;
when the first atomic operation Xi and the first atomic operation Xj have the previous step number, acquiring a first atomic operation X (i-1) corresponding to the previous step number of the first atomic operation Xi, and acquiring a first atomic operation X (j-1) corresponding to the previous step number of the first atomic operation Xj; calculating semantic similarity value R of first atomic operation X (i-1) and first atomic operation X (j-1) by word2vec method (i-1)(j-1) ；
When the first atomic operation Xi and the first atomic operation Xj have the subsequent step numbers, acquiring a first atomic operation X (i+1) corresponding to the previous step number of the first atomic operation Xi, and acquiring a first atomic operation X (j+1) corresponding to the previous step number of the first atomic operation Xj; calculating semantic similarity value R of first atomic operation X (i+1) and first atomic operation X (j+1) by using word2vec method (i+1)(j+1) ；
When the first atomic operation Xi and the first atomic operation Xj both have the previous step number and the next step number, a word2vec method is adopted to calculate the semantic similarity value R of the first atomic operation X (i-1) and the first atomic operation X (j-1) (i-1)(j-1) And calculating the semantic similarity value R of the first atomic operation X (i+1) and the first atomic operation X (j+1) by using word2vec method (i+1)(j+1) ；
S314, test case name N according to first atomic operation Xi xi And test case name N for first atomic operation Xj xj Acquiring test case name N xi The number Np of the first atomic operations in the corresponding first atomic operation set is used for obtaining the name N of the test case xj The number Nq of the first atomic operations in the corresponding first atomic operation set;
when np=nq, the word2vec method is adopted to calculate the name N of the test case xi And test case name N xi Semantic similarity value R of (2) pq ；
S315, according to the semantic similarity value R ij 、R (i-1)(j-1) 、R (i+1)(j+1) 、R pq Calculating the association degree value of the first atomic operation Xi and the first atomic operation Xj;
s316, repeatedly executing the steps S311 to S315 until the calculation of any two first atomic operation association degree values in the first atomic operation total set is completed.
4. The method for constructing and extracting an automobile information security test case according to claim 3, wherein the step S315 specifically includes:
G ij ＝λ 1 R ij +λ 2 R (i-1)(j-1) +λ 3 R (i+1)(j+1) +λ 4 R pq
wherein G is ij A relevance value for the first atomic operation Xi and the first atomic operation Xj; lambda (lambda) 1 、λ 2 、λ 3 、λ 4 Is a weight value.
5. The method for constructing and extracting the automobile information security test case according to claim 1, wherein the step S4 is characterized in that a plurality of first cluster sets in the step S3 are obtained, and a plurality of first atomic operations in each first cluster set are subjected to deduplication processing to form a plurality of second cluster sets, and specifically includes the following steps:
s41, acquiring one first cluster set S in a plurality of first cluster sets in S3, deleting one of the two first atomic operations when the semantic similarity value between the two first atomic operations in the first cluster set S is more than 95%, and adding the deleted test case name of the first atomic operation to the test case name of the reserved first atomic operation to finally form a second cluster set;
S42, repeatedly executing the step S41 until each first cluster set in the plurality of first cluster sets is subjected to de-duplication processing, so as to form a plurality of second cluster sets.
6. The method for constructing and extracting the automobile information security test case according to claim 1, wherein the step S5 is characterized in that a plurality of second cluster sets in the step S4 are obtained, and a second clustering operation is performed for each second cluster set to form a plurality of third cluster sets, and specifically includes the following steps:
s51, aiming at each second clustering set, performing second clustering operation on the first atomic operations in the second clustering set by adopting a K-means method according to semantic similarity values among the first atomic operations in the second clustering set;
s52, repeatedly executing the step S51 until each second cluster set in the plurality of second cluster sets completes second clustering operation to form a plurality of third cluster sets.
7. The method for constructing and extracting an automotive information security test case according to claim 1, wherein the step S74 constructs an execution path code of the test case Ci according to the execution steps of the first atomic operations in the test case Ci, and writes the execution path code into a root node in a test case knowledge graph corresponding to the test case Xi, and specifically includes:
S741. set k=1;
s742, acquiring a first atomic operation set { X1, X2 … Xk, … Xu } of the test case Ci; wherein Xk is the kth first atomic operation, namely the kth operation step of the test case; u is the total number of first atomic operations included in the test case Ci, i.e., the total number of operation steps included in the test case Ci;
s743, aiming at the first atomic operation Xk, acquiring the code B of the first atomic operation k Judging whether a connected leaf node exists in the intermediate node corresponding to the first atomic operation; if so, finding a second atomic operation whose test case name is the same as the test case Ci in step S742 in the second atomic operation corresponding to the plurality of connected leaf nodes, and adding the code of the second atomic operation to B k Afterwards;
if not, add the encoding of the first atomic operation X (k+1) to B 1 Afterwards;
setting k=k+1;
s744, repeating the step S743 until all first atomic operations in the test case Ci are processed to form an execution path code;
s745, writing the execution path code into a root node in the test case knowledge graph corresponding to the test case Xi.
8. The method for constructing and extracting the automobile information security test case according to claim 7, wherein the step S8 extracts the test case according to the test case knowledge graph in the step S7, and specifically includes the following steps:
S81, acquiring an execution path code stored in a root node corresponding to a test case in a test case knowledge graph;
s82, dividing the execution path code in the step S81 according to the unit of three bits;
s83, acquiring a three-bit code, and searching a first atomic operation corresponding to the three-bit code in the test case knowledge graph;
s84, judging whether a node corresponding to the first atomic operation has a lower node, if so, searching for the next three-bit code in the lower node;
s85, repeating the steps S83 to S84 from the first three-bit code according to the sequence of the path codes which are segmented in the step S82 until the node position corresponding to the last three-bit code in the test case knowledge graph is obtained, and completing the extraction of the test case.
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