Automated Software Test Data Generation Research Articles

Automated Software Test Data Generation With Generative Adversarial Networks

With the rapid increase of software scale and complexity, the cost of traditional software testing methods will increase faster than the scale of software. In order to improve test efficiency, it is particularly important to automatically generate high-quality test cases. This paper introduces a framework for automatic test data generation based on the generative adversarial network (GAN). GAN is employed to train a generative model over execution path information to learn the behavior of the software. Then we can use the trained generative model to produce new test data, and select the test data that can improve the branch coverage according to our proposed selection strategy. Compared to prior work, our proposed method is able to handle programs under test with large-scale branches without analyzing branch expressions. In the experiment, we exhibit the performance of our method by using two modules in GNU Scientific Library. In particular, we consider the application of our method in two testing scenarios; unit testing and integration testing, and conduct a series of experiments to compare the performance of three types of GAN models. Results indicate that the WGAN-GP shows the best performance in our framework. Compared with the random testing method, the WGAN-GP based framework improves the test coverage of five functions out of the seven in the unit testing.

Fitness for Solving SMCP Using Evolutionary Algorithm

Search based software engineering is a paradigm with prime focus to apply search techniques and meta-heuristics for solving various software engineering open NP hard problems. Evolutionary meta-heuristics techniques are already proven to provide optimized solutions to other software engineering problems like automated software test data generation, project estimation, class responsibility assignment to name few. Software module clustering is such an open problem of software engineering that cannot be solved in definite manner. For a given module dependency graph of software system, there exists large number of possible partitions. Identifying a good partition to cluster all modules of software system is an exhaustive search that cannot be carried out in finite manner. With search based techniques applying evolutionary algorithms, an optimized solution can be identified with evaluating goodness of a given partitions. Identifying a fitness function that can direct the search towards optimal solution is very critical. This paper discusses various types of fitness applicable to solve software module clustering problem.

Automated Software Test Data Generation Using Improved Search Procedure

Automated software test data generation is a complex and one of the most challenging task. Generation of test data manually is tedious and error-prone. The base algorithm, which has been improved in this paper involves redundant and un-necessary execution cycles. In this paper, we have proposed an efficient search procedure that reduces the time complexity of test data generation significantly. Our experiments conducted on varying sizes of source code showing ratio of execution time of automated test data generation using Basic Search Procedure and our Improved Search Procedure conclude that, with the increase of source code size, test data generation takes place in lesser time using our improved search procedure.

Application of meta-heuristic algorithms for automated software test data generation

The quality of a developed software system depends on factors such as reliability, maintainability, scalability etc. To deliver reliable software, it is necessary that it is defect free. In order to achieve this objective, the software system needs to be thoroughly tested. Since exhaustive testing is not possible, it is necessary to identify suitable test data that helps in improving the quality of a software product. Manual generation of test data is laborious and time consuming due to the presence of a huge number of decision nodes in a program. Meta-heuristic algorithms can help in such scenario by generating optimal test data automatically, from a very large search space of candidate solutions. In this paper, an attempt is made to automatically generate test data using five meta-heuristic algorithms. The proposed approach is applied for a case study bank ATM. Results show that meta-heuristics are efficient in generating optimal test data.

Automated Software Test Data Generation: Direction of Research

In this paper we are giving an overview of automatic test data generation. The basic objective of this paper is to acquire the basic concepts related to automated test data generation research. The different implementation techniques are described with their relative merits and demerits. The future challenges and problems of test data generation are explained. Finally we describe the area where more focus is required for making automatic test data generation more effective in industry.

Automated software test data generation based on immune genetic algorithm

A kind of software test data automated generation method based on Immune Genetic Algorithms(IGA)was proposed.Immune operators including vaccine extraction,vaccination and immune selection were introduced into the traditional genetic algorithm.The experimental results show that this algorithm is more effective than the traditional one.

Generating test data for distributed software using the chaining approach

Software testing is very labour-intensive and expensive, and accounts for a significant proportion of software system development cost. If the testing process could be automated, the cost of developing software could be significantly reduced. Currently, most of the existing test data generators are for sequential programs. One of the major problems in dynamic testing of distributed software is reproducible program execution. Distributed programs often make non-deterministic selections of communications events, e.g. the select statement in Ada. Thus, a test case for the distributed program is defined as a set of input data plus a sequence of events that is used to resolve any non-deterministic choices that are possible during program execution. In this paper, we present a chaining approach for automated software test data generation for distributed software. This approach builds on current theory of existing execution-oriented test data generation. The goal is to find program input and a rendezvous sequence on which the selected statement is executed. The chaining approach uses program dependency analysis to identify statements that affect the execution of the selected statement. These statements are then used to guide the test data generation process. The experiments have shown that the chaining approach may significantly improve the chances of generating test data as compared to the existing methods.

The chaining approach for software test data generation

Software testing is very labor intensive and expensive and accounts for a significant portion of software system development cost. If the testing process could be automated, the cost of developing software could be significantly reduced. Test data generation in program testing is the process of identifying a set of test data that satisfies a selected testing criterion, such as statement coverage and branch coverage. In this article we present a chaining approach for automated software test data generation which builds on the current theory of execution-oriented test data generation. In the chaining approach, test data are derived based on the actual execution of the program under test. For many programs, the execution of the selected statement may require prior execution of some other statements. The existing methods of test data generation may not efficiently generate test data for these types of programs because they only use control flow information of a program during the search process. The chaining approach uses data dependence analysis to guide the search process, i.e., data dependence analysis automatically identifies statements that affect the execution of the selected statement. The chaining approach uses these statements to form a sequence of statements that is to be executed prior to the execution of the selected statement. The experiments have shown that the chaining approach may significantly improve the chances of finding test data as compared to the existing methods of automated test data generation.

Automated software test data generation

An alternative approach to test-data generation based on actual execution of the program under test, function-minimization methods and dynamic data-flow analysis is presented. Test data are developed for the program using actual values of input variables. When the program is executed, the program execution flow is monitored. If during program execution an undesirable execution flow is observed then function-minimization search algorithms are used to automatically locate the values of input variables for which the selected path is traversed. In addition, dynamic data-flow analysis is used to determine those input variables responsible for the undesirable program behavior, significantly increasing the speed of the search process. The approach to generating test data is then extended to programs with dynamic data structures and a search method based on dynamic data-flow analysis and backtracking is presented. In the approach described, values of array indexes and pointers are known at each step of program execution; this information is used to overcome difficulties of array and pointer handling. >