CN110825627B - Adaptive random test case generation method based on grid area density - Google Patents
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- G06F11/36—Preventing errors by testing or debugging software
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Abstract
The invention provides an adaptive random test case generation method based on grid area density, and belongs to the technical field of software automation test. Comprising the following steps: step 1, obtaining an input domain range of a program; step 2, randomly generating a first test case and executing the first test case in a program to be tested to check whether a failure area is hit or not; step 3, removing the area where the test case is located; step 4, dividing grids through the center points of the areas; step 5, screening out better according to the number of blank areaskBlank areas, randomly generateds*kSelecting a candidate case from the candidate cases, and selecting the next test case; and step 6, executing the test case, judging whether the hit fails, if so, returning to the size of the tested case set, otherwise, continuing to execute 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 the effectiveness of the proposed method is verified by comparing with the most classical adaptive random test method in the field.
Description
Technical Field
The invention belongs to the technical field of software automation test, and relates to an adaptive random test case generation method based on grid area density.
Background
At present, random Testing (RT) is one of Testing technologies with great potential and being widely focused on in the technical field of software automation Testing, and is widely applied to practical Testing activities such as Java programs, unix tool sets, windows GUI application software and the like. As a classical black box test method, random test can automatically generate test cases under the condition that only program input domain information is known, but the representativeness of a test case set is generally poor, and the failure detection capability is limited. In order to improve the detection effect of random test, chen et al propose an adaptive random test (Adaptive Random Testing, ART) method, which not only ensures the randomness of the use case generation, but also improves the uniform distribution degree of the use case in the input space. The proposal of an ART method (Fixed-Sized-filtered-Set ART, FSCS-ART) of a Fixed-scale Candidate Set, which combines the calculation of the distance between the use cases and the selection of the Candidate use cases, is one of the typical methods in the field, but as the dimension increases, the edge effect of the FSCS-ART gradually appears, the detection effect is obviously degraded, and the cost of the distance calculation between the use cases gradually increases. In order to avoid computation between cases, chen et al combined with the area division concept, proposed a binary ART (B-ART) and a random ART (ART by Random Partition, RP-ART), which can obtain significantly better detection results than random tests at a smaller time cost, but have a larger improvement in failure detection capability than FSCS-ART.
In order to further improve the failure detection effect and reduce the calculation cost, the input space is divided through the grid center, a blank subarea set with better local part is selected by combining the area density index, a distance calculation candidate rule of FSCS-ART is introduced, and finally the best candidate use case is selected as the next test use case, and the steps are sequentially circulated until the 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 by Density of Gird Region, ART-DGR) based on grid area 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 an adaptive random test case generation method based on grid area density, which has the following technical scheme:
step 1, defining a named storage data structure, 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 willtExecuting 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, removing from ERtJudging whether the region is an empty set or not, and executing specific operation according to the result;
step 4, grid division is carried out through the center points of all the current areas, all the newly added blank areas are put into a blank area set, and area information is updated;
step 5, screening out better according to the number range of the blank areaskEach blank region is randomly generated for each blank region as a candidate region setsSelecting the best candidate case from the candidate cases as the next test case;
and step 6, executing the test case in the program to be tested, judging whether the invalid region is hit, if so, returning to the tested case set, otherwise, continuing to execute the steps 2 to 4.
Further, the specific steps of the step 1 are as follows:
step 1.1, firstly, initializing a stored related data structure, which comprises the following 5 parts: test case set TSThe candidate case set CS, the temporary candidate area set TCR, the candidate area set CR and the blank area set ER additionally comprise 3 relevant basic attributes which are respectively an input domain D, a failure domain F and a dimensiond；
Step 1.2, adding the input domain D into the blank region set ER, and acquiring input domain information.
Further, the specific steps of the step 2 are as follows:
step 2.1, randomly generating a first test case in the input field DtAnd willtAdding the test case set TS into the test case set TS;
step 2.2, willtExecuting in the program to be tested, judgingtWhether the failure 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 the miss, executing the related operation of step 3.
Further, the specific steps of the step 3 are as follows:
step 3.1 removal from ERtThe method comprises the steps of (1) locating an area and judging whether ER is an empty set or not;
step 3.2, if the operation is empty, executing the related operation of step 4;
and 3.3, if the operation is not empty, directly executing the related operation of the step 5.
Further, the specific steps of the step 4 are as follows:
step 4.1, grid division is carried out on the center points of all the areas at present, all newly added blank areas are put into ER, and input domain information is updated;
and 4.2, repositioning the test cases in the TS, and updating the case index array.
Further, the specific steps of the step 5 are as follows:
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 5dPlacing the blank areas into a temporary candidate area set TCR and screening according to area density indexesSelect better 2dPutting the blank areas into a candidate area set CR, and directly executing step 5.6;
step 5.3, if not greater than 5d, judging whether the number of the blank areas is greater than 2 againd；
Step 5.4 if greater than 2dPlacing all blank areas into a temporary candidate area set TCR, and screening out better 2 according to the area density indexdPlacing the blank areas into a candidate area set CR;
step 5.5 if not greater than 2dAll blank areas are directly put into a candidate area set CR;
step 5.6, randomly generating for each blank region in the CRsThe candidate cases are put into a candidate case set CS;
step 5.7, calculating the nearest neighbor distance from each candidate use case in CS to the measured use case in the neighbor regiondistSelectingdistThe candidate case with the largest value is taken as the next test caset。
Further, the specific steps of the step 6 are as follows:
step 6.1, thetAdding the data into TS and executing the data in the program to be tested, and judging whether the data hit a failure area;
step 6.2, if the miss, executing the related operation of step 3;
and 6.3, if the test result 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. the adaptive random test case generation method (ART-DGR) based on the grid region density can enable the test case to quickly construct a position index through a grid center division strategy, and is convenient for searching neighbor region indexes. The method combines the comprehensive index of area density to firstly select blank areas with better local part as candidate areas, and randomly generates elastic change in the candidate areass（s=10/d，sRounding up) the candidate cases, and then re-selecting the best test case from the candidate cases to execute the program so that the selected test case is executed in the programThe test case has strong representativeness, and the failure detection capability of the adaptive random test method is greatly improved.
2. The adaptive random test case generation method (ART-DGR) based on the grid area density can be well applied to software program test, program failure is detected rapidly, and an ideal test effect is obtained by lower cost consumption.
3. Compared with the FSCS-ART method which is put forward for the first time, the failure detection effect of the adaptive random test case generation method (ART-DGR) based on the grid area density is obviously improved, and in the aspect of operation efficiency, the Euclidean distance from the candidate case to each tested case is not needed to be calculated, and only the Euclidean distance from the candidate case to the existing test case in the neighbor area is needed to be calculated, so that the efficiency is obviously improved.
Drawings
FIG. 1 is a flow chart of a test case generation method of FSCS-ART.
FIG. 2 is a flow chart of an adaptive random test case generation method based on grid area density.
FIG. 3 is a specific illustration of an adaptive random test case generation method based on grid area density.
FIG. 4 is a plot of F-ratio trend for the present method versus FSCS-ART method when 5000 simulation experiments were performed in 2-dimensional block mode.
FIG. 5 is a plot of F-ratio trend for the present method versus FSCS-ART method when 5000 simulation experiments were performed in 3-dimensional block mode.
FIG. 6 is a plot of F-ratio trend for the present method versus FSCS-ART method for 5000 simulation experiments in 4-dimensional block mode.
FIG. 7 is a plot of F-ratio trend for the present method versus FSCS-ART method when 5000 simulation experiments were performed in 5-dimensional block mode.
Detailed Description
In order to more clearly understand the technical content of the adaptive random test case generating method based on the grid area density, the present invention is further described below with reference to the accompanying drawings and specific embodiments, and it should be noted that the embodiments described and given are intended to facilitate understanding of the present invention, and are not limited in any way.
The flow chart of the adaptive random test case generation method based on the grid area density is shown in fig. 1. Defining a named storage data structure, initializing, setting an input domain, and adding the input domain into a blank region set ER; second, randomly generating the first test case in the input domaintThen willtExecuting the test in the program to be tested to check if it hits the invalid region, if it hits the invalid regionFDirectly returning to the size of the test case set TS, exiting the test process, and otherwise, continuing to execute; third step, remove from ERtJudging whether ER is an empty set in the region, if so, executing the fourth step, otherwise, directly executing the fifth step; fourth, grid division is carried out through the center points of all the current areas, all newly added blank areas are put into a blank area set, and area information is updated; fifthly, counting the number of all blank areas, and judging whether the number of the blank areas is larger than 5d（dDimension) if greater than 5dThen randomly select 5dPlacing the blank areas into a temporary candidate area set TCR and screening out better 2 according to an area density indexdPutting the blank areas into the candidate area set CR, and if the number of the blank areas is not more than 5d, judging whether the number of the blank areas is more than 2 againdIf it is greater than 2dPlacing all blank areas into a temporary candidate area set TCR, and screening out better 2 according to the area density indexdThe blank regions are placed in the candidate region set CR if not greater than 2dAll blank areas are directly put into a candidate area set CR, and each blank area in the CR is randomly generatedsThe candidate cases are put into a candidate case set CS, the nearest neighbor distance dist between each candidate case in the CS and the tested case in the neighbor region is calculated, and the candidate case with the largest dist value is selected as the next test casetThe method comprises the steps of carrying out a first treatment on the surface of the The sixth step is totAdding the data into TS and executing in the program to be tested, judging whether the invalid region is hit or not, if not, continuing executing the third step of related operationIf hit, the size of the test case set TS is returned, and the method is finished.
The process of carrying out the method according to the invention will now be described with the initial step of the implementation case being the initialization operation carried out in the first step.
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, which 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 through the midpoint of the input field, as shown in FIG. 3 (b), since after meshing, the number of blank areas is 3, we will in this simple example divide 2dSet to 1 (actually 4), 5dSet to 5 (actually 10), s set to 2, for ease of understanding. Because the number of blank areas is larger than 1, we put the three blank areas into the TCR, screen out the better area according to the area density index, the calculation formula is: region density index=m× (b+1), M is the minimum manhattan distance from the current region to the neighbor existing test case region, B is the equilibrium proportionality coefficient, and the calculation formula is:
，
therefore, the areaAnd area->The area density indices of (2) are all 1, and the area +.>2, of course, the region +.>Put into CR, in FIG. 3 (c), we randomly generate 2Candidate test cases are calculated, and the nearest neighbor distances between the candidate test cases and the existing test cases in the neighbor areas are respectivelydist 1 Anddist 2 finally selectdistCandidate use case with large valuec 1 As the next test casetNamelyt 2 As shown in fig. 3 (d). For the remaining two blank areas, selecting a better blank area according to the rule, then selecting a formal test case 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, i.e., all cell areas are occupied by one measured case, each area will be gridded according to their center points, corresponding to 2 times the original division number of each dimension, also referred to as multiple division. After meshing, the existing test cases will be relocated to these new sub-areas. Then, the newly generated blank areas are used for generating subsequent test cases, and one formal test case is selected in each blank area to be executed on the program to be tested, and whether the program fails or not is detected.
To verify the effectiveness of the method, the most classical ART method (FSCS-ART) of the fixed-template candidate set proposed in the field is compared, and the method is compared with the FSCS-ART method in a simulated experiment and a demonstration experiment analysis. Simulation experiment setting programdThe ranges of the input parameters being equidistant, i.e. the input space beingdThe space is maintained at equal intervals,dthe size of each dimension is set to be 2, 3, 4 and 5, and the input range of each dimension is set to be [ -5000,5000]. Failure rate in block failure modeθThe value range of (2) is set as follows: 0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002, 0.0001.
In order to better evaluate the effectiveness of the method, the F-ratio index is used for measuring 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 of random test (1 +.θ) Is a ratio of (2). Generally, the lower the ratio, the improvement of the method over random testing is illustratedThe effect is more obvious. For each failure rate value, 5000 experiments were repeated and the F-ratio value for each test method was counted. The specific simulation results are shown in table 2, table 3, table 4 and table 5, and the corresponding comparison trends are shown in fig. 4, fig. 5, fig. 6 and fig. 7.
In the procedure of the demonstration, 15 classical programs are selected and re-used by using JAVA language, and the detailed information of the test programs is shown in Table 6. They are derived from Numerical Recipes and ACM-gathered algorithms, respectively, and have been widely used in related methods for adaptive random testing. The actual program is implanted with an unequal number of faults, and the faults 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. Further, since the failure rate of each actual program is an approximate value, for comparison, the F-measure index will be used in the demonstration analysis as a measure of the detection effect of the method. For 15 actual procedures in the demonstration experiment, 5000 experiments were repeated and F-measure values of the respective methods were recorded, and the results are shown in Table 7.
The result shows that the failure detection capability of the method is obviously improved compared with the classical FSCS-ART method, and the relative advantages are obvious.
TABLE 1 variable names and meanings thereof
| Variable name | Meaning of variable name |
| CS | Test case set |
| CR | Candidate region set |
| TCR | Temporary candidate region set |
| ER | Blank region set |
| D | Input field |
| F | Failure domain |
| d | Dimension(s) |
| M | Manhattan distance minimum value for candidate region to neighbor existing test case region |
| B | Balance ratio coefficient |
| c | Candidate use case |
| s | Number of candidate use cases |
| dist | Nearest neighbor distance of candidate use case to existing test of neighbor region |
| N neig | Number of candidate region neighbor regions |
| N total | Total number of current regions |
| N ntc | Number of test cases from candidate region to neighbor region |
| N tc | Current number of all test cases |
| θ | Failure rate of |
TABLE 2F-ratio for two 2-dimensional methods in Block failure mode (unit:%)
TABLE 3F-ratio of two 3-dimensional methods in block failure mode (unit:%)
TABLE 4F-ratio for two 4-dimensional methods in block failure mode (unit:%)
TABLE 5F-ratio for two 5-dimensional methods in block failure mode (unit:%)
TABLE 6 test program information in empirical analysis
TABLE 7F-measure values for two methods in actual procedure
| Procedure | FSCS-ART method | The method |
| 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 description is only for the purpose of clearly showing the embodiments of the present invention and is not intended to limit the scope of the present invention, but any modification, finish or the like will fall within the scope of the present invention without departing from the spirit and scope of the present invention.
Claims (5)
1. The method for generating the adaptive random test case based on the grid area density is characterized by comprising the following steps of:
step 1, defining a named storage data structure, 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 willtExecuting 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, removing from ERtJudging whether ER is an empty set or not in the region, and executing specific operation according to the result;
step 4, grid division is carried out through the center points of all the current areas, all the newly added blank areas are put into a blank area set, and area information is updated;
step 5, screening out better according to the number range of the blank areaskEach blank region is randomly generated for each blank region as a candidate region setsSelecting the best candidate case from the candidate cases as the next test case;
step 6, executing the test case in the program to be tested, judging whether the invalid region is hit, if so, returning to the size of the tested case set, otherwise, continuing to execute the steps 2 to 4;
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 area set TCR, the candidate area set CR and the blank area set ER additionally comprise 3 relevant basic attributes which are respectively an input domain D, a failure domain F and a dimensiond；
Step 1.2, adding an input domain D into a blank region set ER, and acquiring input domain information;
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 5dPlacing the blank areas into a temporary candidate area set TCR and screening out better 2 according to an area density indexdThe blank areas are put into a candidate area set CR, and step 5.6 is directly executed, wherein the area density index=m× (b+1), M is the minimum value of manhattan distance from the current area to the neighbor existing test case area, B is a balanced proportionality coefficient, and the calculation formula is:
；
step 5.3 if not greater than 5dJudging whether the number of the blank areas is larger than 2 againd；
Step 5.4 if greater than 2dPlacing all blank areas into a temporary candidate area set TCR, and screening out better 2 according to the area density indexdPlacing the blank areas into a candidate area set CR;
step 5.5 if not greater than 2dAll blank areas are directly put into a candidate area set CR;
step 5.6, randomly generating for each blank region in the CRsThe candidate cases are put into a candidate case set CS;
step 5.7, calculating the nearest neighbor distance from each candidate use case in CS to the measured use case in the neighbor regiondistSelectingdistThe candidate case with the largest value is taken as the next test caset。
2. The method for generating the adaptive random test case based on the grid area density according to claim 1, wherein the specific implementation of the step 2 comprises the following steps:
step 2.1, randomly generating a first test case in the input field DtAnd willtAdding the test case set TS into the test case set TS;
step 2.2, willtExecuting in the program to be tested, judgingtWhether the failure 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 the miss, executing the related operation of step 3.
3. The method for generating the adaptive random test case based on the grid area density according to claim 1, wherein the specific implementation of the step 3 comprises the following steps:
step 3.1 removal from ERtThe method comprises the steps of (1) locating an area and judging whether ER is an empty set or not;
step 3.2, if the operation is empty, executing the related operation of step 4;
and 3.3, if the operation is not empty, directly executing the related operation of the step 5.
4. The method for generating the adaptive random test case based on the grid area density according to claim 1, wherein the specific implementation of the step 4 comprises the following steps:
step 4.1, grid division is carried out on the center points of all the areas at present, all newly added blank areas are put into ER, and input domain information is updated;
and 4.2, repositioning the test cases in the TS, and updating the case index array.
5. The method for generating the adaptive random test case based on the grid area density according to claim 1, wherein the specific implementation of the step 6 comprises the following steps:
step 6.1, thetAdding the data into TS and executing the data in the program to be tested, and judging whether the data hit a failure area;
step 6.2, if the miss, executing the related operation of step 3;
and 6.3, if the test result is hit, returning the size of the test case set TS, and ending the method.
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