Towards Constraint Logic Programming over Strings for Test Data Generation
This addresses the need for high-quality test data in software development, particularly when existing data is insufficient or restricted, though it is incremental as it applies an existing method (CLP) to a new domain (string constraints).
The paper tackled the problem of generating diverse and secure test data for software testing by evaluating constraint logic programming (CLP) for string-based test data generation, resulting in a prototypical CLP solver that successfully generated IBAN numbers and calendar dates as case studies.
In order to properly test software, test data of a certain quality is needed. However, useful test data is often unavailable: Existing or hand-crafted data might not be diverse enough to enable desired test cases. Furthermore, using production data might be prohibited due to security or privacy concerns or other regulations. At the same time, existing tools for test data generation are often limited. In this paper, we evaluate to what extent constraint logic programming can be used to generate test data, focussing on strings in particular. To do so, we introduce a prototypical CLP solver over string constraints. As case studies, we use it to generate IBAN numbers and calender dates.