In software testing, it is often desirable to find test inputs that exercise specific program features. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, many attempts have been made to automate the process. Random test data generation consists of generating test inputs at random, in the hope that they will exercise the desired softwar...

Software testing is an expensive, yet essential stage in all software development models, thus there is a great effort from the research community to facilitate or even automate this step. Although much of the testing process is automated by modern software development environments (e.g., test execution, monitoring), the selection of test data remains generally a manual process. In this paper w...

SQL queries are widely used in critical enterprise applications. Complex queries using mutlitable joins and aggregations are difficult to specifiy correctly. Programmers sometimes make mistakes in writing SQL queries, for example using a wrong join operator, a wrong relational operator in a where-clause predicate, etc. We call such variations of the query as ‘mutants’ of the original query. A m...

In order to reduce the high cost of manual software testing and at the same time to increase the reliability of the testing processes researchers and practitioners have tried to automate it. One of the most important components in a testing environment is an automatic test data generator — a system that automatically generates test data for a given program. Through the years several attempts in...

Software testing is an indispensible part of software development. It increases the confidence of programmer and user in the reliability and accuracy of software. However, it is a laborious and time-consuming task. Almost half of the software development resources spend on testing the software. Automatic software testing can substantially reduce the cost of development of software. Further exha...

The Godzilla automatic test data generator is an integrated collection of tools that implements a relatively new test data generation method, constraint-based testing, that is based on mutation analysis. Constraint-based testing integrates mutation analysis with several other testing techniques, including statement coverage, branch coverage, domain perturbation and symbolic evaluation. Because ...

This paper presents a technique for automatically generating test-data to test exceptions. The approach is based on the application of a dynamic global optimization 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 ab...

Functional testing of software dedicated for hybrid embedded systems should start at the early development phase and requires analysis of discrete and continuous signals, where timing constraints play a significant role. Test data generation is done manually nowadays, though it should be automated to the highest possible extent. Hereby, a concept for testing at the model level is introduced. Fi...

During software development, it is often necessary to access real customer data in order to validate requirements and performance thoroughly. However, company and legal policies often restrict access to such sensitive information. Without real data, developers have to either create their own customized test data manually or rely on standardized benchmarks. While the first tends to lack scalabil...

Search-based optimization methods have been used for software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to application of meta-heuristic optimization methods to generate test data that cover the code space of a program. Automatic test data generation that can cover all the paths of software is known as a major cha...