Strong higher order mutation-based test data generation

This paper introduces SHOM, a mutation-based test data generation approach that combines Dynamic Symbolic Execution and Search Based Software Testing. SHOM targets strong mutation adequacy and is capable of killing both first and higher order mutants. We report the results of an empirical study using 17 programs, including production industrial code from ABB and Daimler and open source code as well as previously studied subjects. SHOM achieved higher strong mutation adequacy than two recent mutation-based test data generation approaches, killing between 8% and 38% of those mutants left unkilled by the best performing previous approach. © 2011 ACM

Mapeo sistemático y estudio de caso sobre técnicas de generación automática de pruebas

Incluye bibliografía.El trabajo tiene como objetivo explorar la generación automática de casos de prueba a través de la identificación de las técnicas y sus problemas e investigar su utilidad práctica. Se realizó un mapeo sistemático de la literatura, extendiendo dos trabajos relacionados a la tesis para conocer las técnicas y los problemas investigados. A partir de este mapeo se realizó un estudio de caso con herramientas de generación para evaluar su eficacia con respecto a la detección de defectos

Higher Order Mutation Testing

Mutation testing is a fault-based software testing technique that has been studied widely for over three decades. To date, work in this field has focused largely on first order mutants because it is believed that higher order mutation testing is too computationally expensive to be practical. This thesis argues that some higher order mutants are potentially better able to simulate real world faults and to reveal insights into programming bugs than the restricted class of first order mutants. This thesis proposes a higher order mutation testing paradigm which combines valuable higher order mutants and non-trivial first order mutants together for mutation testing. To overcome the exponential increase in the number of higher order mutants a search process that seeks fit mutants (both first and higher order) from the space of all possible mutants is proposed. A fault-based higher order mutant classification scheme is introduced. Based on different types of fault interactions, this approach classifies higher order mutants into four categories: expected, worsening, fault masking and fault shifting. A search-based approach is then proposed for locating subsuming and strongly subsuming higher order mutants. These mutants are a subset of fault mask and fault shift classes of higher order mutants that are more difficult to kill than their constituent first order mutants. Finally, a hybrid test data generation approach is introduced, which combines the dynamic symbolic execution and search based software testing approaches to generate strongly adequate test data to kill first and higher order mutants