SqSelect: Automatic assessment of Failed Error Propagation in state-based systems.

• Automatic assessment of the likelihood of Failed Error Propagation. • Recommendation of optimal parameters to improve testing. • Freely available tool fully supporting the theoretical framework. • Evaluation of the tool and framework over representative case studies. Current software systems are inherently complex and this fact strongly complicates, and makes more expensive, to validate them. Therefore, it is a must to provide methodologies, supported by tools, that can direct validation activities so that they focus on specific aspects of the system (e.g. its critical parts, common errors produced by developers, components that are expensive to fix after deployment, etc). Among the different validation techniques, testing is the most widely used. In this paper we focus on one of the main problems when testing systems with many components: the likelihood of Failed Error Propagation (FEP). FEP appears when we have faulty components such that their wrong behaviour is not revealed when isolatedly testing them but that might produce an error when they are combined with other components. Given a component, it is not possible to automatically assess the likelihood of FEP. However, previous work has shown that there is a strong correlation between the likelihood of FEP and an Information Theory notion called Squeeziness. Recent work has shown that it is possible to compute different values of Squeeziness (essentially, Squeeziness depends on a positive real value) and some of them are more suitable to estimate FEP. In this paper we present our tool SqSelect. Our tool receives either a specific system or its more important characteristics (number of states, maximum and minimum number of outgoing transitions from a state, size of the input and output alphabets) and returns interesting data that can help the tester to estimate the presence of FEP. In particular, our tool provides the most promising value(s) of the parameter associated with Squeeziness so that the likelihood of FEP can be more accurately estimated. In order to compute these values, our tool relies on an artificial neural network that has been extensively trained (compressing the information from around 250 , 000 systems and around 1 , 500 , 000 executions). [ABSTRACT FROM AUTHOR]