TTCN-3 Quality Engineering: Using Learning Techniques to Evaluate Metric Sets

Calculation and optimization of thresholds for sets of software metrics

“…In the following, we discuss the advantages of our method compared to two other techniques: 1) correlation based methods; 2) the brute-force risk minimization approach presented by Werner et al (2007).…”

“…In contrast, the approach defined in this article combines the metric set selection with the threshold optimization and finds a globally optimal value. The method to optimize metric sets presented by Werner et al (2007) is in principle the same as the one presented in this article. They considered all combinations of metrics up to a desired maximum size.…”

“…However, the work by Werner et al (2007) showed that a similar approach worked with TTCN-3 test suites, i.e., software written in a Domain Specif ic Language (DSL) in a non-open-source environment.…”

“…A paper similar to this work, but using a less sophisticated approach for the optimization of metric sets for TTCN-3 is presented by Werner et al (2007). However, the machine learning methodology used in this work is more mature and the case studies analyze it in a wider setting, i.e.…”

“…To this aim, a machine learning algorithm is used to define an approach for the calculation of thresholds for a metric set. In a previous work (Werner et al 2007), we used relatively simple brute-force approach for the calculation of threshold values for a metric set for the Testing and Test Control Notation (TTCN-3) (ETSI 2007;Grabowski et al 2003). However, such a brute force approach has scalability problems and is therefore infeasible for larger metric sets.…”