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Structural testing criteria are mandated in many software development standards and guidelines. The process of generating test-data to achieve 100 % coverage of a given structural coverage metric is labour intensive and expensive. This paper presents an approach to automate the generation of such test-data. The test-data generation is based on the application of a dynamic optimisation-based search for the required test-data. The same approach can be be generalised to solve other test-data generation problems. Three such applications are discussed { boundary value analysis, assertion/run-time exception testing and component re-use testing. Aprototype tool-set has been developed to facilitate the automatic generation of test-data for these structural testing problems. The results of preliminary experiments using this technique and the prototype tool-set are presented and show the e ciency and e ectiveness of this approach. 1

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One of the major costs in a software project is the construction of test-data. This paper outlines a generalised test-case data generation framework based on optimisation techniques. This framework can incorporate a number of testing criteria unifying both functional and non-function testing. Application of the optimisation based approach are given for worst-case execution time, specification conformance, structural coverage and exception condition testing. The results of a number of small example case Software testing is an expensive process, typically consuming at least 50 % of the total costs involved in developing software [1]. Automation of the testing process is desirable both to reduce the development costs and also to improve the quality of (or at least confidence in) software. While automation of the testing process – the maintenance and execution of tests – is becoming commercially wide spread, the

The efficient and effective generation of test-data from high-level models is of crucial importance in advanced modern software engineering. Empirical studies have shown that mutation testing is highly effective. This paper describes how search-based automatic test-data generation methods can be used to find mutation adequate test-sets for Matlab/Simulink models. Categories and Subject Descriptors

Fault-based testing is often advocated to overcome limitations of other testing approaches; however it is also recognized as being expensive. On the other hand, evolutionary algorithms have been proved suitable for reducing the cost of data generation in the context of coverage based testing. In this paper, we propose a new evolutionary approach based on ant colony optimization for automatic test input data generation in the context of mutation testing to reduce the cost of such a test strategy. In our approach the ant colony optimization algorithm is enhanced by a probability density estimation technique. We compare our proposal with other evolutionary algorithms, e.g., Genetic Algorithm. Our preliminary results on JAVA testbeds show that our approach performed significantly better than other alternatives.

One of the major difficulties in software testing is the automatic generation of test data that satisfy a given adequacy criterion. This paper presents an automatic test data generation technique that uses a genetic algorithm (GA), which is guided by the data flow dependencies in the program, to search for test data to cover its def-use associations. The GA conducts its search by constructing new test data from previously generated test data that are evaluated as effective test data. The approach can be used in test data generation for programs with/without loops and procedures. The proposed GA accepts as input an instrumented version of the program to be tested, the list of defuse associations to be covered, the number of input variables, and the domain and precision of each input variable. The algorithm produces a set of test cases, the set of def-use associations covered by each test case, and a list of uncovered defuse associations, if any. In the parent selection process, the GA uses one of two methods: the roulette wheel method or a proposed method, called the random selection method, according to the user choice. Finally, the paper presents the results of the experiments that have been carried out to evaluate the effectiveness of the proposed GA compared to the random testing technique, and to compare the proposed random selection method to the roulette wheel method.

This paper presents the results of a three year research program to develop an automated test-data generation framework to support the testing of safety-critical software systems. The generality of the framework comes from the exploitation of domain independent search techniques, allowing new test criteria to be addressed by constructing functions that quantify the suitability of test-data against the test-criteria. The paper presents four applications of the framework — specification falsification testing, structural testing, exception condition testing and worst-case execution time testing. The results of three industrial scale case-studies are also presented to show that the framework offers useful support in the development safety-critical software systems. 1

The exception handling code of a system is in general the least documented, tested and understood part, since exceptions are expected to occur only rarely. This paper presents a technique for automatically generating test-data to test exceptions. The approach is based on the application of a dynamic global optimisation based search for the required test-data. The authors' work has focused on test-data generation for safety-critical systems. Such systems must be free from anomalous and uncontrolled behaviour. Typically, it is easier to prove the absence of any exceptions than it is to prove that the exception handling is safe. A process for integrating automated testing with exception freeness proofs is presented as a way forward for tackling the special needs of safety critical systems. An evaluation shows the application of the technique to a commercial aircraft engine controller system as part of a proof of exception freeness. 1 Introduction A failure occurs when software is preven...