XIVT: eXcellence In Variant Testing

The XIVT project will deliver technological innovation in two areas.

In knowledge-based requirements analysis and selection the project will extract features and requirements, using machine learning techniques and map-reduce algorithms, and also identify features and rank by priority for testing. Furthermore, the project will automatically create high-level test cases using association rules and features, and combine software testing techniques with machine learning techniques to improve code security.

XIVT will contribute in the field of vulnerability detection by providing a cluster of software testing tools using different detection techniques, such as fuzzing/attack injection and static and dynamic analysis combined with machine learning techniques.
In test derivation and execution, the project will provide variability abstraction to ensure all features can be tested, the use of feature models and product line base models for test case generation to guarantee defined quality criteria, and a variant selection mechanism which determines instances and configuration parameter values to guarantee an optimal coverage for given testing efforts.

Further outcomes are a measure and methodology for assessing the quality of test suites for configurable products in terms of error-detection capabilities, a proposal for certification of software on the basis of the product family, which allows to maintain certificates for parts that have been previously validated, and a model checking procedure for selecting applicable test cases for specific products in the regression test of product lines.