Constraint-Based Automated Generation of Test Data

Test data generation is one of the most technically challenging steps of testing software, but most commercial systems currently incorporate very little automation for this step. This paper presents results from a project that is trying to find ways to incorporate test data generation into practical test processes. The results include a new procedure for automatically generating test data that incorporates ideas from symbolic evaluation, constraint-based testing, and dynamic test data generation. It takes an initial set of values for each input, and dynamically ‘pushes’ the values through the control-flow graph of the program, modifying the sets of values as branches in the program are taken. The result is usually a set of values for each input parameter that has the property that any choice from the sets will cause the path to be traversed. This procedure uses new analysis techniques, offers improvements over previous research results in constraint-based testing, and combines several steps into one coherent process. The dynamic nature of this procedure yields several benefits. Moving through the control flow graph dynamically allows path constraints to be resolved immediately, which is more efficient both in space and time, and more often successful than constraint-based testing. This new procedure also incorporates an intelligent search technique based on bisection. The dynamic nature of this procedure also allows certain improvements to be made in the handling of arrays, loops, and expressions; language features that are traditionally difficult to handle in test data generation systems. The paper presents the test data generation procedure, examples to explain the working of the procedure, and results from a proof-of-concept implementation. Copyright © 1999 John Wiley & Sons, Ltd.

One of the ways of test data generation is using the path-oriented (path-wise) test data generator. This generator takes the program code and test adequacy criteria as input and generates the test data in order to satisfy the adequacy criteria. One of the problems of this generator in test data generation is the lack of attention to generating the diagnostic test data. In this paper a new approach has been proposed for path-oriented test data generation and by utilizing it, test data is generated automatically realizing the goal of discovering more important errors in the least amount of time. Since that some of the instructions of any programming language are more error-prone, so the paths that contain these instructions are selected for perform test data generation process. Then, the input domains of these paths variables are divided by a divide-and-conquer algorithm to the subdomains. A set of different subdomains is called hyper cuboids, and test data will be generated by these hyper cuboids. We apply our method in some programs, and compare it with some previous methods. Experimental results show proposed approach outperforms same previous approaches.

In software testing process, test data generation represents an important step for high quality software, even for mobile devices. As proposed in previous works, a potential source for random data generation is represented by the UI layout files that are used for almost all mobile platforms (Android, iOS, Windows Phone/Mobile). This paper continues the previous work and presents a test data generation system based on Android layout files. The test data generator uses DSL files as input and generates test data that conform to several testing principles. The generated test data could be stored in XML files or any format required by the testing frameworks.

In search-based structural testing, metaheuristic search techniques have been frequently used to automate the test data generation. In Genetic Algorithms (GAs) for example, test data are rewarded on the basis of an objective function that represents generally the number of statements or branches covered. However, owing to the wide diversity of possible test data values, it is hard to find the set of test data that can satisfy a specific coverage criterion. In this paper, we introduce the use of Novelty Search (NS) algorithm to the test data generation problem based on statement-covered criteria. We believe that such approach to test data generation is attractive because it allows the exploration of the huge space of test data within the input domain. In this approach, we seek to explore the search space without regard to any objectives. In fact, instead of having a fitness-based selection, we select test cases based on a novelty score showing how different they are compared to all other solutions evaluated so far.

At present, it is still impossible to accurately solve realistic problems of production planning and control (PPC) in an acceptable period of time. Therefore numerous heuristic procedures were developed to solve problems of PPC. The efficiency of these procedures is usually appraised by means of statistical analyses. The data necessary for the analyses must meet high requirements to obtain significant and generalizable results from the statistical analyses. A procedure for the generation of test data is presented in the article. The procedure is based on given characteristics. It stochastically produces PPC data of arbitrary complexity. The procedure for the generation of test data is based on a hierarchical concept. The user enters values of aggregated classification numbers which globally characterize the modeled production system and the amount of released production orders. The values of detailed classification numbers are stochastically generated from the aggregated classification numbers. These values form the input for the generation of the PPC data wanted. This generation is stochastically, too. The user may modify both classification numbers and PPC data in order to generate test data equivalent to known practical data or to acquire test data with slightly changing characteristics for analyses of parameter sensitivity.

Automated test data generation is a challenging problem for researchers in the area of software testing. Up until now, most of the work on test data generation is at unit level. Until level test data generation involves the execution of test path at unit level where interaction with other components is minimum. Test data generation for unit testing involves a single path and there is no usage of formal and actual parameters. The problem of automated test data generation becomes very challenging when we move to other levels of testing including integration testing and system level testing. At integration level, the variables are passed as arguments to other components and variables change their names; also multiple paths are executed from different components to ensure proper functionality. Recently evolutionary approaches have been proven a powerful tool for test data generation. In this paper, we have proposed a novel approach for test data generation for coupling based integration testing using particle swarm optimization. Up until now, there is no research for test data generation for coupling based integration testing using particle swarm optimization. Our approach takes the coupling path as input, containing different sub paths, and generates the test data using particle swarm optimization. We have also proposed architecture of tool for automation of our approach. In future, we will implement our proposed approach and will perform different experiments to prove its significance.

Automatic test data generation is required to generate test cases dynamically for a specific software program. Manual generation of test data is too tedious and a time consuming task. This paper proposes a technique using Intelligent Water Drop (IWD) for automatic generation of test data. Correctly generated test data helps in reducing the effort while testing the software. This paper discusses different algorithms based on IWD to generate test data and path coverage over Control Flow Graph. Test data is generated keeping in mind all of the programming constraints like “if,” “while,” “do while,” etc., available in the program.

Mutation testing is a powerful software testing technique. However, it is difficult to obtain test data for killing a large number of mutants, especially for hard-to-kill mutants. Mutant branch is formed by an original statement of a program under test and its mutated statement. The true branch of a mutant branch is covered by a test datum, suggesting the corresponding mutant is killed under the criterion of weak mutation testing. This article focuses on efficiently generating test data for a large number of mutant branches. When generating test data using a search-based method, the size of the search domain is a determining factor affecting the search performance. The key observation is that only partial input variables affect whether a mutant will be killed, so the search domain can be reduced by deleting irrelevant variables. Along this line, we first group the mutant branches based on their relevant input variables, followed by formulating a multi-task optimization model of test data generation for the grouped mutant branches, in which the relevant input variables are taken as the decision variables. Finally, a multi-population genetic algorithm with individual sharing is employed to generate test data by multi-tasking. The experiments based on eight programs of various sizes show that removing related variable helps reduce the search domain, and the efficiency of test data generation by grouping and multitasking is improved.

The main task of software testing is the generation of test data with high quality in restricted time. But for software with uncertainties, such as randomness, existing methods of generating test data often lose their effectiveness. For software with randomness, a test datum might cover the test target in one run, but obtains different results in the next run. Therefore, special testing methods must be exploited for this kind of software. In order to test software with randomness, we first propose a novel test adequacy criterion, and then build a mathematical model for generating test data according to the new criterion. Finally, we present a method of solving the optimization model using a set-based genetic algorithm. We apply the proposed method to test 12 programs, and compare with traditional genetic algorithm and the random method. From the experimental results we can see that, the proposed adequacy criterion is available for the software with randomness and the proposed algorithm can effectively generate test data.

In this paper we have suggested a new method for test data generation using dynamic forward slicing algorithm. Separating the suspicious parts of code from the original program will make the process of test data generation easier. In forward dynamic slicing, the slices produced are much smaller than the original program and the search space for testing is considerably reduced. In this paper, we discuss the forward algorithm and the advantages of using forward dynamic algorithm for test data generation. The test cases are generated by analyzing the constraints present in the slices constructed from forward dynamic slicing algorithm. In the proposed method, the program for which the test data to be generated is analyzed. Forward slicing of the program is done with respect to all the variables whose value is changed during program execution. Slices obtained are then verified for statements which define certain constraints for test data generation. This helps to clearly define the limits of test data and remove the ambiguities about the value of the test data which is to be generated. The test data is then generated based on the constraints. In the proposed method, since forward algorithm is used, the execution traces need not be recorded and analyzing the constraints present in the slices will help in fault localization and debugging. This is one of the most important advantages of our method.

Software testing refers to a process that improves the quality of software systems through bug detection, but software testing is one of the time and cost-consuming stages in software development. Hence, software test automation is regarded as a solution, which can facilitate heavy and laborious tasks of testing. Problem: Automatic generation of data with maximum coverage of program branches is regarded as an NP-complete optimization problem. Several heuristic algorithms have been proposed for this problem. Failure to maximise branch coverage, the poor success rate in optimal test data generation, and low stable results are the major demerits of the previous methods. Goal: Enhancing the branch coverage rate of the generated test data, enhancing the success rate in generating the test data with maximum coverage, and enhancing the stability and speed criteria are the main goals of this study. Method: In this study, a combination of grey wolf optimization algorithm and genetic algorithm have been used to automatically generate optimal test data. The proposed hybrid method (Fuzuli <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) tries to generate test data with maximum branch coverage at the software source code level. Results: The results obtained from the proposed algorithm were compared with those of the following algorithms: Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).The results obtained from running a wide range of tests on standard benchmark programs showed that the proposed algorithm outperforms other algorithms with an average coverage of %99.98, a success rate of %99.97, and an average output of 2.86.

Automatic test data generation from a software specification prepares test cases for software developers before their code development phase. Having test cases before coding, helps the developers to control their code to conform to the specification. In state-based specifications, paths from the initial state to the final state may be varied, this is called parallel paths. In order to generate test data which cover all behaviors in the specification, test data for each parallel path should be generated. This paper proposes an enhanced genetic algorithm(GA)-based approach to resolve the parallel paths from the UML state machine diagram. The proposed approach is improved from our previous study which uses a GA based test data generation method for only one path. The approach identifies parallel paths to cover all transitions. GA evolves a number of suitable test data sets - one test data set for each parallel path. The best test data for each parallel path are picked and used for calculating the overall coverage test data. The experimental results show improved coverage results with the enhanced approach for a number of case studies with parallel paths.

4736375 Apparatus for fast generation of large quantities of test data words in a test facility

Application of Negative Selection Algorithm (NSA) for test data generation of path testing

Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output.The Harmony Search (HS) algorithm is based on the improvisation process of music.In comparison to other algorithms, the HSA has gain popularity and superiority in the field of evolutionary computation.When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization can be done by adjusting the input pitches and generate the perfect harmony.The test case generation process is used to identify test cases with resources and also identifies critical domain requirements.In this paper, the role of Harmony search meta-heuristic search technique is analyzed in generating random test data and optimized those test data.Test data are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM through Harmony search.It is observed that this algorithm generates suitable test cases as well as test data and gives brief details about the Harmony search method.It is used for test data generation and optimization.