CN110825627A - Adaptive random test case generation method based on grid area density - Google Patents
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Abstract
The invention provides a method for generating an adaptive random test case based on grid area density, and belongs to the technical field of software automated testing. The method comprises the following steps: step 1, obtaining an input domain range of a program; step 2, randomly generating a first test case, executing the first test case in a program to be tested, and checking whether a failure area is hit; step 3, removing the area where the test case is located; step 4, carrying out grid division through the center point of the region; step 5, screening out better blank areas according to the number of the blank areaskA blank area, randomly generateds*kSelecting a next test case from the candidate cases; and 6, executing the test case and judging whether the hit fails, returning the size of the tested case set if the hit fails, and otherwise, continuously executing the steps 3 to 5. The method has better failure detection effect capability under the condition of ensuring the consistency of the program to be detected, and verifies the effectiveness of the proposed method by comparing with the most classical adaptability random test method in the field.
Description
Technical Field
The invention belongs to the technical field of software automated testing, and relates to a method for generating an adaptive random test case based on grid area density.
Background
At present, in the technical field of software automation Testing, Random Testing (RT) is one of Testing technologies with great potential and wide attention, and it has been widely applied to actual Testing activities such as Java programs, Unix toolsets, Windows GUI application software, and the like. As a classic black box test method, random testing can automatically generate test cases under the condition that only program input domain information is known, but the representativeness of the test case set is generally poor, and the failure detection capability is limited. In order to improve the detection effect of the random test, Chen et al propose an Adaptive Random Testing (ART) method, which not only ensures the randomness of the generation of the use cases, but also improves the uniform distribution degree of the use cases in the input space. The proposal of the Fixed-Sized Candidate Set ART (FSCS-ART) combines the distance calculation between the use cases and the selection of the Candidate use cases, which is one of the typical methods in the field, but with the increase of the dimension, the edge effect of the FSCS-ART gradually appears, the detection effect is obviously degraded, and the distance calculation overhead between the use cases is gradually increased. In order to avoid calculation among use cases, Chen et al propose a binary ART (B-ART) and a Random divisional ART (RP-ART) in combination with a region division idea, and although the two methods can obtain a detection effect remarkably superior to a Random test at a small time cost, the failure detection capability of the two methods still has a larger promotion space compared with FSCS-ART.
In order to further improve the failure detection effect and reduce the calculation cost, an input space is divided through a grid center, a blank sub-area set with a better local part is selected by combining with a region density index, a distance calculation candidate rule of FSCS-ART is introduced, the best candidate case is finally selected as the next test case, and the steps are sequentially circulated until a program fault is detected.
Disclosure of Invention
In order to improve the failure detection capability of the adaptive random test method, the invention provides an adaptive random test case generation method (ART-DGR) based on grid Region Density, so that the screened test case has stronger representativeness, and the test method is ensured to have high failure detection capability, low cost consumption and wider applicability.
The invention provides a method for generating an adaptive random test case based on grid area density, which adopts the following technical scheme:
step 4, carrying out grid division through the central points of all the current areas, putting all newly added blank areas into a blank area set, and updating area information;
step 5, screening out better blank areas according to the number range of the blank areaskA blank areaAs a candidate region set, randomly generated for each blank regionsSelecting the best candidate case from the candidate cases as the next test case;
and 6, executing the test case in the program to be tested, judging whether the failure area is hit, returning the tested case set if the failure area is hit, and otherwise, continuously executing the steps 2 to 4.
Further, the step 1 comprises the following steps:
step 1.1, firstly, initializing a stored related data structure, which comprises the following 5 parts: the test case set TS, the candidate case set CS, the temporary candidate region set TCR, the candidate region set CR and the blank region set ER comprise 3 related basic attributes which are respectively an input domain D, a failure domain F and a dimensiond；
And step 1.2, adding the input domain D into the blank domain set ER, and acquiring the information of the input domain.
Further, the step 2 comprises the following steps:
step 2.1, randomly generating a first test case in the input domain DtAnd will betAdding the test sample into a test case set TS;
step 2.2, mixingtExecuting in the program to be tested, judgingtWhether the domain F is hit;
step 2.3, if hit, returning the size of the test case set TS, and ending the method;
step 2.4, if not hit, then step 3 correlation is performed.
Further, the specific steps of the step 3 are as follows:
step 3.1, removal from ERtJudging whether ER is an empty set or not in the area;
step 3.2, if the operation is empty, the relevant operation of the step 4 is executed;
and 3.3, if the data is not empty, directly executing the relevant operation of the step 5.
Further, the specific steps of the step 4 are as follows:
step 4.1, performing grid division on the central points of all the current areas, putting all newly added blank areas into an ER, and updating input area information;
and 4.2, repositioning the test cases in the TS, and updating the case index array.
Further, the step 5 comprises the following steps:
step 5.1, counting the number of all blank areas, and judging whether the number of the blank areas is more than 5d；
Step 5.2, if greater than 5dThen randomly select 5dPutting the blank area into a temporary candidate area set TCR and screening out better 2 according to an area density indexdPutting the blank area into a candidate area set CR, and directly executing the step 5.6;
step 5.3, if not, judging whether the number of the blank areas is more than 2 againd；
Step 5.4, if greater than 2dThen put all blank regions into the temporary candidate region set TCR, and select better 2 according to the region density indexdPutting the blank regions into a candidate region set CR;
step 5.5, if not greater than 2dDirectly putting all blank regions into the candidate region set CR;
step 5.6, randomly generating each blank area in CRsPutting the candidate cases into a candidate case set CS;
step 5.7, calculating the nearest neighbor distance from each candidate case in the CS to the tested case in the neighbor areadistSelectingdistThe candidate case with the largest value is used as the next test caset。
Further, the step 6 comprises the following steps:
step 6.1, mixingtAdding the TS into a program to be tested, and judging whether a failure area is hit or not;
step 6.2, if not, executing the relevant operation of the step 3;
and 6.3, if the test case set TS is hit, returning the size of the test case set TS, and ending the method.
Compared with the prior art, the invention has the beneficial effects that:
1. an adaptive random test case generation method (ART-DGR) based on grid region density can enable a test case to quickly construct a position index through a grid center division strategy, and is convenient for searching a neighbor region index. It combines the region density as a comprehensive index to select the blank regions with better local area as candidate regions, and generates randomly the regions with elastic variations（s=10/d，sThe whole) candidate cases are extracted upwards, and then the best test case is selected from the candidate cases to be executed in a program, so that the selected test case has strong representativeness, and the failure detection capability of the adaptive random test method is greatly improved.
2. An adaptive random test case generation method (ART-DGR) based on grid region density can be well applied to software program testing, program failure can be quickly detected, and an ideal test effect can be obtained with low cost consumption.
3. Compared with the first proposed FSCS-ART method, the failure detection effect of the grid region density-based adaptive random test case generation method (ART-DGR) is obviously improved, and in the aspect of operation efficiency, the Euclidean distance from a candidate case to each tested case is not required to be calculated, and only the Euclidean distance from the candidate case to the existing test case in the neighbor region is required to be calculated, so that the efficiency is obviously accelerated.
Drawings
FIG. 1 is a flowchart of a test case generation method of FSCS-ART.
FIG. 2 is a flow chart of a method for adaptive random test case generation based on grid region density.
FIG. 3 is a specific illustration of an adaptive random test case generation method based on grid area density.
FIG. 4 is a graph showing the trend of F-ratio values of the present method and FSCS-ART method in a 2-dimensional block model for 5000 simulation experiments.
FIG. 5 is a graph showing the trend of F-ratio values of the present method and FSCS-ART method in a 3-dimensional block model for 5000 simulation experiments.
FIG. 6 is a graph showing the trend of F-ratio values of the present method and FSCS-ART method in a 4-dimensional block model for 5000 simulation experiments.
FIG. 7 is a graph showing the trend of F-ratio values of the present method and FSCS-ART method in a 5-dimensional block model for 5000 simulation experiments.
Detailed Description
In order to clearly understand the technical content of the adaptive random test case generation method based on grid area density, the invention is further described with reference to the drawings and specific embodiments, and it should be noted that the embodiments are described for facilitating understanding of the invention without any limitation.
The flow chart of the adaptive random test case generation method based on the grid area density is shown in fig. 1. Firstly, defining a data structure of naming storage, carrying out initialization operation, setting an input domain, and adding the input domain into a blank region set ER; second, randomly generating a first test case in the input domaintThen will betExecuting the test in the program to be tested, checking whether the program hits the failure area, and if the program hits the failure areaFDirectly returning to the size of the test case set TS, and quitting the test process, otherwise, continuing to execute; third, removing it from ERtJudging whether ER is an empty set or not in the area, if so, executing the fourth step, and if not, directly executing the fifth step; fourthly, carrying out grid division through the central points of all the current areas, putting all newly added blank areas into a blank area set, and updating area information; fifthly, counting the number of all blank areas and judging whether the number of the blank areas is more than 5d（dDimension) if it is larger than 5dThen randomly select 5dPutting the blank area into a temporary candidate area set TCR and screening out better 2 according to an area density indexdPutting the blank regions into the candidate region set CR, and if not more than 5d, judging whether the number of the blank regions is more than 2 againdIf it is greater than 2dThen all blank regions are put into the temporary candidate region set TCR, again based onBetter 2 is screened out by the regional density indexdPutting the blank region into the candidate region set CR if not more than 2dDirectly putting all blank regions into the candidate region set CR, and randomly generating each blank region in the CRsThe candidate cases are put into a candidate case set CS, the nearest neighbor distance dist from each candidate case in the CS to the tested case in the neighbor area is calculated, and the candidate case with the largest dist value is selected as the next test caset(ii) a The sixth step is totAnd adding the TS into the TS and executing the TS in the program to be tested, judging whether the failure area is hit, if not, continuing to execute the relevant operation of the third step, and if so, returning the size of the TS of the test case set, and ending the method.
The initialization operation performed in the first step is used as a starting step of the implementation example to explain the implementation process of the method of the present invention.
Referring to fig. 3, a process of an adaptive random test case generation method based on grid area density is as follows:
taking a two-dimensional input field as an example, in fig. 3(a), first a first test case is randomly generated in the input field, and this input field is regarded as a first blank area. Thus, this blank area is occupied and there are no other blank areas, in which case 4 sub-areas are obtained by meshing the middle point of the input area, as shown in FIG. 3(b), since the number of blank areas is 3 after meshing, in this simple example we will take 2dSet to 1 (actually 4), 5dSet to 5 (actually 10) and s to 2 for ease of understanding. Because the number of the blank areas is more than 1, the three blank areas are put into the TCR, a better area is screened out according to the area density index, and the calculation formula is as follows: the region density index = M (B + 1), M is the minimum value of Manhattan distance from the current region to the neighbor existing test case region, B is a balance proportionality coefficient, and the calculation formula is as follows:
，
therefore, areaAnd area
All of the area density indexes of (1), and the areas
Is 2, of course, the region
Put into CR, in FIG. 3(c), we randomly generate 2 candidate test cases, and calculate the nearest neighbor distance from each to the existing test case in its neighbor region, which is respectivelydist 1Anddist 2and finally selectingdistHigh value candidate casec 1As the next test casetIs that ist 2 As shown in fig. 3 (d). For the remaining two blank areas, selecting a better blank area according to the rule, then selecting formal test cases from the candidate cases according to the distance measurement rule, and sequentially determining the subsequent test casest 3 Andt 4 see fig. 3 (e).
In fig. 3(f), once there is no blank area, that is, all the unit areas are occupied by one tested case, each area is subjected to mesh division according to their center points, which is equivalent to that the division number of each dimension is 2 times that of the original, also called as fold division. After the meshing is completed, the existing test cases will be relocated to these new sub-regions. And then, the newly generated blank areas are used for generating subsequent test cases, and each blank area is ensured to select a formal test case to be executed on the program to be tested, so as to detect whether the program is invalid.
In order to verify the effectiveness of the method, the ART method (FSCS-ART) of the most classical and first proposed fixed-scale candidate set in the field is compared, and the method is compared with the FSCS-ART method through simulation experiments and empirical experiment analysis. Simulation experiment deviceProcedure for settingdThe range of the input parameters is equidistant, i.e. the input space isdThe equidistant space is maintained and the distance between the two,dis set to be 2, 3, 4 and 5, and each dimension input range is set to be [ -5000,5000,5000]. In the bulk failure mode, failure rateθThe value ranges of (A) are set as follows: 0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002 and 0.0001.
In order to better evaluate the effectiveness of the method, an F-ratio index is adopted to measure the detection effect of the method. The F-ratio index is the F-measure value of the ART method and the F-measure theoretical value (1 ^ based on the random test)θ) The ratio of (a) to (b). Generally, the lower the ratio, the more significant the improvement of the method over the random test. For each failure rate value, 5000 experiments were repeated and the F-ratio value for each test method was counted. The results of the specific simulation experiments are shown in tables 2, 3, 4 and 5, and the corresponding comparative trends are shown in fig. 4, 5, 6 and 7.
In the demonstration link, 15 classical programs are selected and re-used by the method in the JAVA language, and the detailed information of the test programs is shown in Table 6. They are derived from algorithms collected by the Numerical responses and the ACM, respectively, and are widely applied to relevant methods of adaptive random testing at present. The above actual programs are all implanted with an unequal number of faults, which are common mutation operations including Arithmetic Operator Replacement (AOR), Relational Operator Replacement (ROR), variable replacement (SVR), Constant Replacement (CR), Statement Deletion (SDL), and the like. In addition, since the failure rate of each actual program is an approximate value, for convenience of comparison, the F-measure index is used as a measure of the detection effect of the method in the empirical analysis. For 15 actual procedures in the demonstration experiments, 5000 times of experiments were repeated and F-measure values of the respective methods were recorded, respectively, and the results are shown in Table 7.
The result shows that the failure detection capability of the method is remarkably improved compared with that of the classical FSCS-ART method, and the relative advantages are obvious.
TABLE 1 variable names and their meanings
| Name of variable | Meaning of variable name |
| CS | Test case set |
| CR | Set of candidate regions |
| TCR | Temporary candidate region set |
| ER | Set of blank regions |
| D | Input field |
| F | Failure domain |
| d | Dimension (d) of |
| M | Manhattan distance minimum value from candidate area to neighbor existing test case area |
| B | Coefficient of balance |
| c | Candidate use case |
| s | Number of candidate cases |
| dist | Nearest neighbor distance of candidate case to existing test in neighbor region |
| N neig | Number of neighbor regions of candidate region |
| N total | Total number of current area |
| N ntc | The number of test cases from the candidate area to the neighbor area |
| N tc | Number of all current test cases |
| θ | Failure rate |
TABLE 2F-ratio of two methods in 2 dimensions in the Block failure mode (Unit:%)
TABLE 3F-ratio of two 3-dimensional methods in Block failure mode (Unit:%)
TABLE 4F-ratio of 4-dimensional two methods in Block failure mode (Unit:%)
TABLE 5F-ratio of two 5-dimensional methods in Block failure mode (Unit:%)
TABLE 6 test program information in empirical analysis
TABLE 7F-measure values of two methods in the actual program
| Procedure for measuring the movement of a moving object | FSCS-ART method | Method for producing a composite material |
| airy | 796.20 | 763.36 |
| bessj0 | 446.36 | 420.48 |
| erfcc | 2865.97 | 2749.36 |
| probks | 5643.45 | 5250.49 |
| tanh | 311.03 | 290.07 |
| bessj | 441.32 | 392.93 |
| gammq | 1058.69 | 1031.82 |
| sncndn | 633.87 | 629.95 |
| binaryGCD | 592.73 | 543.71 |
| golden | 1584.71 | 1519.53 |
| plgndr | 1612.69 | 1538.62 |
| cel | 1577.30 | 1255.27 |
| el2 | 701.55 | 726.84 |
| perpendicular | 981.81 | 904.36 |
| calDay | 1290.77 | 1162.79 |
The foregoing is merely for the purpose of illustrating particular embodiments of the invention and is not to be construed as limiting the scope of the invention, as any modifications, alterations and the like may be made without departing from the spirit and scope of the invention.
Claims (7)
1. A method for generating an adaptive random test case based on grid area density is characterized by comprising the following steps:
step 1, defining a data structure of naming storage, carrying out initialization operation, setting an input domain, and adding the input domain into a blank region set ER;
step 2, randomly generating a first test case in the input domaintThen will betExecuting the test in the program to be tested, checking whether the program hits the failure area, and executing related operation according to the test result;
step 3, removal from ERtJudging whether ER is an empty set or not in the area, and executing specific operation according to the result;
step 4, carrying out grid division through the central points of all the current areas, putting all newly added blank areas into a blank area set, and updating area information;
step 5, screening out better blank areas according to the number range of the blank areaskThe blank regions are used as candidate region sets and are randomly generated for each blank regionsSelecting the best candidate case as the next test case
And 6, executing the test case in the program to be tested, judging whether the failure area is hit, if so, returning the size of the tested case set, otherwise, continuously executing the steps 2 to 4.
2. The method for generating the adaptive random test case based on the grid area density as claimed in claim 1, wherein the specific implementation of the step 1 comprises the following steps:
step 1.1, firstly, initializing a stored related data structure, which comprises the following 5 parts: the test case set TS, the candidate case set CS, the temporary candidate region set TCR, the candidate region set CR and the blank region set ER comprise 3 related basic attributes which are respectively an input domain D, a failure domain F and a dimensiond；
And step 1.2, adding the input domain D into the blank domain set ER, and acquiring the information of the input domain.
3. The method for generating the adaptive random test case based on the grid area density as claimed in claim 1, wherein the step 2 is implemented by the following steps:
step 2.1, randomly generating a first test case in the input domain DtAnd will betAdding the test sample into a test case set TS;
step 2.2, mixingtExecuting in the program to be tested, judgingtWhether the domain F is hit;
step 2.3, if hit, returning the size of the test case set TS, and ending the method;
step 2.4, if not hit, then step 3 correlation is performed.
4. The method for generating the adaptive random test case based on the grid area density as claimed in claim 1, wherein the specific implementation of the step 3 comprises the following steps:
step 3.1, removal from ERtJudging whether ER is an empty set or not in the area;
step 3.2, if the operation is empty, the relevant operation of the step 4 is executed;
and 3.3, if the data is not empty, directly executing the relevant operation of the step 5.
5. The method for generating the adaptive random test case based on the grid area density as claimed in claim 1, wherein the step 4 is implemented by the following steps:
step 4.1, performing grid division on the central points of all the current areas, putting all newly added blank areas into an ER, and updating input area information;
and 4.2, repositioning the test cases in the TS, and updating the case index array.
6. The method for generating the adaptive random test case based on the grid area density as claimed in claim 1, wherein the specific implementation of the step 5 comprises the following steps:
step 5.1, counting the number of all blank areas, and judging whether the number of the blank areas is more than 5d；
Step 5.2, if greater than 5dThen randomly select 5dPutting the blank area into a temporary candidate area set TCR and screening out better 2 according to an area density indexdPutting the blank area into a candidate area set CR, and directly executing the step 5.6;
step 5.3, if not, judging whether the number of the blank areas is more than 2 againd；
Step 5.4, if greater than 2dThen put all blank regions into the temporary candidate region set TCR, and select better 2 according to the region density indexdPutting the blank regions into a candidate region set CR;
step 5.5, if not greater than 2dDirectly putting all blank regions into the candidate region set CR;
step 5.6, randomly generating each blank area in CRsPutting the candidate cases into a candidate case set CS;
step 5.7, calculating the nearest neighbor distance from each candidate case in the CS to the tested case in the neighbor areadistSelectingdistThe candidate case with the largest value is used as the next test caset。
7. The method for generating the adaptive random test case based on the grid area density as claimed in claim 1, wherein the specific implementation of the step 6 comprises the following steps:
step 6.1, mixingtAdding the TS into a program to be tested, and judging whether a failure area is hit or not;
step 6.2, if not, executing the relevant operation of the step 3;
and 6.3, if the test case set TS is hit, returning the size of the test case set TS, and ending the method.
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