CN108399127B - Class integration test sequence generation method - Google Patents
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Abstract
The invention provides a generation method of a class integration test sequence, which comprises the following steps: 1) acquiring all classes and the relations between the classes from a source code of an object-oriented system; 2) obtaining a class priority table from a class diagram in a UML design document for an object-oriented system; 3) and automatically generating the class integration test sequence through a genetic algorithm. And repeating the process to finally obtain a group of optimal (the test cost for constructing the test pile is minimum) class test sequence results. The invention solves the problems that the initial population has no constraint condition in the class integration test sequence problem, the overall quality of the initial population is poor, the convergence speed and the optimization result are influenced, and the optimization effect is not accurate due to the one-sidedness and the irrationality of the individual evaluation standard to a certain extent. The method not only improves the overall quality of the population and accelerates the convergence speed, but also strengthens the optimizing capability of the genetic algorithm, improves the optimizing accuracy, further improves the testing efficiency and precision, and can better meet the actual needs.
Description
Technical Field
The invention belongs to the technical field of software testing, and particularly relates to a method for generating a class test sequence in an integrated test.
Background
The object-oriented program has no obvious hierarchical module structure like a process-oriented program, the connection between objects is in a message transmission mode, a message can cause chain reaction to further form a method calling chain, and a static structure for reflecting the calling relation is an intricate and complex mesh structure. Therefore, where to start the test and how to determine an integrated test sequence is a matter of further research. The test sequence of the classes is related to the time for finding the software defects and the test cost, so that the determination of the integrated test sequence of the classes becomes an important research problem in the integrated test.
The existing work mainly measures the quality of a class integrated test sequence according to the cost of a test pile. Generating a class test sequence requires breaking some inter-class dependencies until there are no loops. The process of breaking the dependency relationship between classes means that a test pile needs to be constructed, and certain cost is spent. Therefore, when eliminating all loops formed by inter-class dependencies, the smaller the cost spent to construct a test pile, the better. However, the cost of the test pile cannot be directly measured or estimated, and certain indexes are required. At present, the cost index of the test pile is mainly evaluated from two aspects: and integrating the overall complexity of the constructed test piles or the number of the constructed test piles in the test sequence generation process.
The problem of generation of class-integrated test sequences is essentially an optimization problem, and is also an NP-complete (difficult) problem. Existing solutions include graph theory based methods and search based methods. The method based on graph theory cannot be applied to large-scale programs and only can be used for small-scale programs due to self limitations. Existing search-based methods include genetic algorithm-based methods, simulated annealing algorithm-based methods, particle swarm algorithm-based methods, ant colony algorithm-based methods, and NSGA-II. The generation of the optimal class integration test sequence needs to be transformed for many times, and the simulated annealing algorithm can approach the smooth distribution at will only when the transformation number is at least the square of the solution space scale. For the generation problem of class integrated test sequences, the solution space size is a factorial of the number of classes, so the size of the number of transformations approximates to the exponential of the number of classes. If any approximate approximation mode is adopted to generate the optimal class integration test sequence which converges to the whole situation, the execution time of the simulated annealing algorithm is increased in an exponential mode. For the system to be tested with a large number of classes, the running time is difficult to bear, so that the algorithm is not feasible. In the NP problem, a more desirable solution can be converged on in a limited time. If the number of the classes is too large, the formed class sequences grow in an exponential relationship, the scale of a solution space is greatly increased, and the solution is easy to fall into a local optimal solution when a particle swarm optimization algorithm is used for solving. The adoption of the ant colony algorithm cannot guarantee that the optimal solution of the problem is found, but only can guarantee that a better solution or an acceptable solution of the problem is found. The genetic algorithm has better robustness, and can obtain a more ideal solution in the NP problem in a convergence mode within a limited time. The selection of the fitness function directly influences the convergence speed of the genetic algorithm and whether the optimal solution can be found. Existing schemes often use the overall complexity of a test pile as a fitness function to evaluate the quality of an individual (class integrated test sequence). However, the overall complexity of the constructed test piles or the number of the test piles serving as the fitness function is one-sided and inaccurate, which results in poor optimization effect and restricts the generation of the optimal solution to a certain extent, so that the existing method for generating the class test sequence by using the overall complexity of the test piles as the fitness function cannot meet the actual requirement, and two indexes of the overall complexity of the constructed test piles and the number of the test piles should be comprehensively considered, so that a reasonable fitness function needs to be designed to evaluate the quality of the class integrated test sequence.
Disclosure of Invention
The invention provides a method for generating a class integration test sequence based on a genetic algorithm, which aims at solving the problem of poor optimization effect of the prior art that the class integration test sequence is difficult to determine due to the characteristics of large scale, complex inter-class relation and the like of an object-oriented program.
The invention is realized according to the following technical scheme:
a class integration test sequence generation method comprises the following steps:
the data collection module is used for acquiring the information of the classes and the relationship between the classes and acquiring a class priority table; and
the class integration test sequence generation module is realized as follows:
a, reading all classes and relationship information among the classes in the data collection module;
step B, reading a class priority table in the data collection module,
and step C, automatically generating a class integration test sequence through a genetic algorithm.
Preferably, the step C is implemented by the following steps:
the method comprises the following steps: encoding the class;
step two: randomly generating initial population P with the quantity 2-3 times of the number of the classes under the constraint of the priority information given by the class priority table;
step three: constructing a fitness function for evaluating the quality of the individual;
step four: calculating the fitness value of each individual in the population P, and taking the weighted average of the overall complexity of the test piles and the total number of the test piles as an individual evaluation standard;
step five: endowing different selection probabilities Ps to the individuals according to the fitness value of each individual, and selecting 2-3 times of individuals from the initial population P in a roulette mode to generate a new population P';
step six: according to the input crossover probability Pc, enabling the parent individuals in the population P' to carry out pairwise crossover, carrying out validity check on the crossed individuals, retaining the legal individuals, and discarding the illegal individuals;
step seven: carrying out gene segment rearrangement on parent individuals according to the input mutation probability Pm, and carrying out validity check on the generated new individuals;
step eight: and repeating the fourth step to the seventh step for multiple times, wherein the finally obtained class test sequence result is the optimal solution, and the optimal test sequence enables the test cost for constructing the test pile to be minimum.
Preferably, in the third step, when a fitness function for evaluating the quality of an individual is constructed, two indexes, namely the total complexity for constructing the test piles and the number of the constructed test piles, are comprehensively considered;
the calculation of the overall complexity of the test pile needs to be based on the complexity of all constructed single test piles, and when the complexity of the single test pile is measured, an objective entropy weight method is adopted to determine the weight of the attribute complexity and the method complexity.
Preferably, in step three, the fitness function is expressed as follows:
wherein, Fitness represents a Fitness function, and the right side of the equation represents the weighted average of the total complexity of the test piles and the total number of the test piles; OCplx and NStub are the total complexity of the test piles and the total number of the test piles spent on generating a class integration test sequence, respectively; wOCplxAnd WNStubThe weights of the total complexity of the test piles and the total number of the test piles are respectively taken as values of [0, 1%]And satisfy WOCplx+WNStub＝1。
Preferably, in step five,
wherein, Fi represents the fitness value of the ith individual, and N represents the number of individuals in the population.
Preferably, in the sixth step, the cross probability Pc is 0.5.
Preferably, in the seventh step, the variation probability Pm is 1/G;
wherein G is the number of genes, i.e., the number of classes, in an individual.
Preferably, the data collection module obtains the classes and the relationship information between the classes as follows:
all class information, triples describing the relationships between classes, and inter-class coupling information are obtained from the source code of the object-oriented system.
Preferably, all the class information, the triple describing the relationship between the classes and the inter-class coupling information are obtained from the source code of the program to be tested by an open source code analysis tool SOOT; all the class information, the triple for describing the relationship between the classes and the coupling information between the classes are respectively stored in the class information file and the triple information file for describing the relationship between the classes.
Preferably, the data collection module obtains the class priority table by specifically implementing the following steps:
the class priority table is obtained from class diagrams in a UML design document for an object-oriented system.
The invention has the beneficial effects that:
(1) the problem of generating class integration test sequences is NP problem, and the current method mainly comprises a graph theory-based method and a search-based method. The method based on the graph theory cannot be applied to a large program due to self limitation, and can only solve the generation problem of the class integration test sequence of a small program. The existing search-based method is slow in convergence to the optimal solution and has a large distance from practical application. The invention solves the problem of generating the class integration test sequence by using the coding, generating initial population, designing the fitness function, evaluating individuals, selecting, crossing, varying and the like in the genetic algorithm to continuously evolve the population through the class integration test sequence generation technology based on the genetic algorithm, and gradually generating the optimal solution.
(2) The invention combines the related parameter configuration, applies the genetic algorithm to sequence the classes of the system to be tested, and provides a scientific scheme for the testers to carry out the integrated test on the system. The generation technology of the class integration test sequence based on the genetic algorithm considers the priority information of the class when generating the initial population, generates the initial population under the constraint of the class priority, overcomes the problems that the prior initial population has no constraint condition, has low individual fitness value and poor overall quality, influences convergence speed and optimization results, does not lose randomness, improves the overall quality of the population and enhances the optimization capability of the genetic algorithm.
(3) The invention comprehensively considers the total complexity of the constructed test piles and the number of the constructed test piles, and takes the two indexes as the individual quality evaluation standard, thereby breaking through the limitation that the prior art does not allow to delete strong dependency relations such as inheritance and aggregation, and overcoming the problems that the optimization effect is not accurate enough and the generation of the optimal solution is restricted to a certain extent because the total complexity of the constructed test piles or the number of the constructed test piles are taken as the individual evaluation standard, being more reasonable and meeting the actual requirements.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a flow chart of the class and its associated information acquisition in the data collection module of FIG. 1;
FIG. 3 is a flow chart of the acquisition of a class priority table in the data collection module of FIG. 1;
FIG. 4 is a flow chart of an implementation of the class integration test sequence generation module of FIG. 1 using a genetic algorithm.
Detailed Description
The invention is further described by the following specific embodiments with reference to the attached drawings.
As shown in FIG. 1, the present invention comprises two parts, a data collection module and a class integration test sequence generation module using genetic algorithm, wherein:
a data collection module: the method is used for acquiring the class and the relation information between the classes and acquiring the class priority list. The module is used as input information of the class integration test sequence generation module, provides basic information and priority information for the class integration test sequence generation module, and is used as constraint information for generating an initial population in the class integration test sequence generation module.
The class integration test sequence generation module: and reading all classes, inter-class relations, inter-class coupling information and priority information in the data collection module. Inputting parameters N, I, Pc, Pm and N set when generating a test sequence to represent the size of a population, I represents iteration times, Pc represents cross probability, Pm represents variation probability, calling a genetic algorithm, and outputting the result which is the optimal class integration test sequence.
As shown in fig. 2, the data collection module (acquisition class and related information) in the present invention is specifically implemented as follows: all class information, triples describing relationships between classes, and inter-class coupling information (including attribute complexity and method complexity) are obtained from source code of an object-oriented system.
As shown in fig. 3, the data collection module (acquisition class priority table) of the present invention is implemented as follows: the class priority table is obtained from class diagrams in a UML design document for an object-oriented system.
As shown in FIG. 4, the specific implementation of the class integration test sequence generation module using genetic algorithm in the present invention is as follows:
1) reading all classes and the relation information among the classes in the data collection module;
2) reading a class priority table in the data collection module,
3) the method comprises the steps of automatically generating a class integration test sequence through a genetic algorithm, wherein the method comprises eight steps of coding, generating an initial population, constructing a fitness function, evaluating individuals, selecting operation, crossing operation and mutation operation and the like, and is shown in figure 4.
The specific implementation process of the class integration test sequence generation module is as follows:
the method comprises the following steps: encoding a class: a set of labeled classes represents an individual, such as a class cluster { A, B, C }, one possible individual is a (B, C, A) sequence, and one possible population is { (B C A), (B A C), … };
step two: inputting parameters N, I, Pc and Pm set when generating a test sequence, reading class information and priority information thereof in a data collection module, and generating an initial population P (a group of class test sequences) with the scale of N (N is 2-3 times of the number of classes) under the constraint of class priority;
step three: constructing a fitness function for evaluating the quality of an individual, wherein the construction method comprises the following steps:
an individual represents a class integrated test sequence, and the fitness function representation method is shown as formula (1). Wherein, Fitness represents the Fitness function, and the right side of the equation represents the weighted average of the total complexity of the test piles and the total number of the test piles.
Wherein OCplx and NStub are the total complexity of the test piles and the total number of the test piles spent on generating a class integration test sequence, respectively; wOCplxAnd WNStubThe weights of the total complexity of the test piles and the total number of the test piles are respectively taken as values of [0, 1%]And satisfy WOCplx+WNStub1, the invention takes WOCplx＝WNStub＝0.5。
Formula (II)(1) In (1)
A normalized form representing the overall complexity ocplx (o) of the test peg taken to generate a class integration test sequence o, satisfying equation (2):
wherein, OCplxmin＝Min{OCplx(ok) K is 1,2max＝Max{OCplx(ok) K is 1,2, the.
Wherein, the total complexity OCplx () of the test stub taken to generate a test sequence is calculated as formula (3):
wherein t represents the number of the relationships between classes, and the test pile complexity SCplx (i, j) represents the coupling degree between two classes i, j having a dependency relationship, i.e. the complexity of a single test pile, which can be calculated by a standardized weighted geometric mean, as shown in formula (4):
wherein, A (i, j) and M (i, j) are the attribute complexity and method complexity obtained in the data collection module respectively; wAAnd WMWeights for attribute complexity and method complexity, respectively, are taken to be [0,1 ]]And W isA+WM＝1。
For more objectivity, the invention adopts an entropy weight method to determine WAAnd WMSpecific values of (a). The method comprises the following specific steps:
supposing that M inter-class relations (the value of M is determined according to a system to be tested) and n evaluation indexes are provided, the invention has 2 evaluation indexes, namely attribute complexity A (i, j) and method complexity M (i, j), and then n is 2; construct aThe standard matrix R ═ (R)xy)m*nAs shown in equation (5):
wherein m represents the number of inter-class relationships in the system under test, rxyAn evaluation value indicating the relationship between the x-th class in the y-th index, where y is 1 or 2, and r is 1x1Influence of attribute complexity representing the relation between the xth classes; when y is 2, rx2The influence of the method complexity of the relation between the x-th classes is expressed, x is more than or equal to 1 and less than or equal to m;
then, the specific gravity p of the index value of the x-th inter-class relationship under the y-th (y is 1,2) index is calculatedxy：
Information entropy e of the y indexjAs shown in equation (7).
Finally, the weight of the y index is calculated as shown in equation (8):
where W1 represents the weight W of the attribute complexity index when y is 1AWhen y is 2, W2 represents the weight W of the method complexity indexM。
In formula (1)
A normalized form representing the total number of test stakes NStub taken to generate a class integration test sequence o is represented by equation (9):
wherein, NStubmin＝Min{NStub(ok) K is 1,2max＝Max{NStub(ok) K is 1,2, the.
Step four: calculating the fitness value of each individual in the population P, and taking the fitness function constructed in the step three as an evaluation standard, namely, taking the weighted average of the total complexity of the test piles and the total number of the test piles as a calculation method of the individual fitness value;
step five: assigning different selection probabilities Ps (the calculation method is shown in the following formula, wherein Fi represents the fitness value of the ith individual, and N represents the number of individuals in the population) to the individuals according to the fitness value of each individual, and selecting 2-3 times of the number of the individuals from the initial population P in a roulette mode to generate a new population P';
step six: according to the input crossover probability Pc (0.5), enabling parent individuals in the population P' to cross pairwise, carrying out validity check on the crossed individuals, keeping valid individuals, and discarding invalid individuals;
step seven: carrying out gene segment rearrangement on parent individuals according to the input mutation probability Pm (1/G is taken, G is the number of genes in an individual, namely the number of classes), and carrying out validity test on the generated new individuals;
step eight: and repeating the fourth step to the seventh step for 100 times, wherein the obtained class integration test sequence is the optimal solution.
The following gives the application of the generic integrated test sequence generation method of the present invention to the specific case:
we performed experiments on three systems, JBoss, jrootdraw and MyBatis. The details of these three systems are shown in table 1.
TABLE 1 subject information
a http://www.jboss.org/jbossas/downloads.html–subsystem used:management of JBoss(version 6.0.0M5):is a Java application server；
b http://sourceforge.net/projects/jhotdraw/–package used:org.jhotdraw.draw of JHotDraw(version 7.5.1):framework for the creation ofdrawing editors；
c http://code.google.com/p/mybatis/downloads/list:data mapperframework that makes easier the use a relational database with OOapplication；
The three sites above give the source of the three systems JBoss, jrootdraw and MyBatis.
The operating system used for the experiment is Windows 8, the CPU is core i5 dual core (2.6GHz), and the physical memory is 4G. The time required to run to generate the class test sequence is shown in table 2. Compared with a class integrated test sequence generation method taking the overall complexity of the test pile as the fitness, the method can find the optimal class integrated test sequence more quickly under the same population quantity.
TABLE 2 run time (milliseconds)
Compared with the prior art:
the invention solves the problems that the initial population has no constraint condition in the class integration test sequence problem, the overall quality of the initial population is poor, the convergence speed and the optimization result are influenced, and the optimization effect is not accurate due to the one-sidedness and the irrationality of the individual evaluation standard to a certain extent. The method not only improves the overall quality of the population and accelerates the convergence speed, but also strengthens the optimizing capability of the genetic algorithm, improves the optimizing accuracy, further improves the testing efficiency and precision, and can better meet the actual needs.
The above is only a preferred embodiment of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (9)
1. A class integration test sequence generation method is characterized by comprising the following steps:
the data collection module is used for acquiring the information of the classes and the relationship between the classes and acquiring a class priority table; and
the class integration test sequence generation module is realized as follows:
a, reading all classes and relationship information among the classes in the data collection module;
b, reading a class priority table in the data collection module;
step C, automatically generating a class integration test sequence through a genetic algorithm;
the step C is realized by the following specific steps:
the method comprises the following steps: encoding the class;
step two: randomly generating initial population P with the quantity 2-3 times of the number of the classes under the constraint of the priority information given by the class priority table;
step three: constructing a fitness function for evaluating the quality of the individual;
step four: calculating the fitness value of each individual in the population P, and taking the weighted average of the overall complexity of the test piles and the total number of the test piles as an individual evaluation standard;
step five: endowing different selection probabilities Ps to the individuals according to the fitness value of each individual, and selecting 2-3 times of individuals from the initial population P in a roulette mode to generate a new population P';
step six: according to the input crossover probability Pc, enabling the parent individuals in the population P' to carry out pairwise crossover, carrying out validity check on the crossed individuals, retaining the legal individuals, and discarding the illegal individuals;
step seven: carrying out gene segment rearrangement on parent individuals according to the input mutation probability Pm, and carrying out validity check on the generated new individuals;
step eight: and repeating the fourth step to the seventh step for multiple times, wherein the finally obtained class test sequence result is the optimal solution, and the optimal test sequence enables the test cost for constructing the test pile to be minimum.
2. The class integration test sequence generation method of claim 1, wherein in step three, when constructing a fitness function for evaluating the quality of an individual, two indexes of the total complexity spent on constructing test piles and the number of constructed test piles are considered comprehensively;
the calculation of the overall complexity of the test pile needs to be based on the complexity of all constructed single test piles, and when the complexity of the single test pile is measured, an objective entropy weight method is adopted to determine the weight of the attribute complexity and the method complexity.
3. The method for generating class integration test sequences according to claim 1, wherein in step three, the fitness function is expressed as follows:
wherein, Fitness represents a Fitness function, and the right side of the equation represents the weighted average of the total complexity of the test piles and the total number of the test piles; OCplx and NStub are the total complexity of the test piles and the total number of the test piles spent on generating a class integration test sequence, respectively; wOCplxAnd WNStubThe weights of the total complexity of the test piles and the total number of the test piles are respectively taken as values of [0, 1%]And satisfy WOCplx+WNStub＝1。
4. The class integration test sequence generation method of claim 1, wherein in step five,
wherein, Fi represents the fitness value of the ith individual, and N represents the number of individuals in the population.
5. The class integration test sequence generation method of claim 1, wherein: in the sixth step, the cross probability Pc is 0.5.
6. The class integration test sequence generation method of claim 1, wherein: in the seventh step, the variation probability Pm is 1/G;
wherein G is the number of genes, i.e., the number of classes, in an individual.
7. The method for generating a class integration test sequence according to claim 1, wherein the data collection module obtains the class and the relationship information between the classes as follows:
all class information, triples describing the relationships between classes, and inter-class coupling information are obtained from the source code of the object-oriented system.
8. The class integration test sequence generation method of claim 7, wherein: all the class information, the triples describing the inter-class relationship and the inter-class coupling information are acquired from the source code of the program to be tested through an open source code analysis tool SOOT;
all the class information, the triple for describing the relationship between the classes and the coupling information between the classes are respectively stored in the class information file and the triple information file for describing the relationship between the classes.
9. The method of claim 1, wherein the data collection module obtains the class priority table by implementing the following steps:
the class priority table is obtained from class diagrams in a UML design document for an object-oriented system.
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