Automated Statistical Testing for Embedded Systems

Poore, Jesse H.; Lin, Lin; Eschbach, Robert; Bauer, Thomas W.

doi:10.1201/b11321-14

Cited by 18 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, testing large SPLs demand substantial effort, and effective reuse is a challenge. Model-Based Testing (MBT) approaches need to be adapted to the SPL domain (see [27] for a survey of existing approaches).…”

Section: Introductionmentioning

confidence: 99%

Validated Test Models for Software Product Lines: Featured Finite State Machines

Fragal

Simão

Mousavi

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Variants of the finite state machine (FSM) model have been extensively used to describe the behaviour of reactive systems. In particular, several model-based testing techniques have been developed to support test case generation and test case executions from FSMs. Most such techniques require several validation properties to hold for the underlying test models. In this paper, we propose an extension of the FSM test model for software product lines (SPLs), named featured finite state machine (FFSM). As the first step towards using FFSMs as test models, we define feature-oriented variants of basic test model validation criteria. We show how the high-level validation properties coincide with the necessary properties on the product FSMs. Moreover, we provide a mechanised tool prototype for checking the feature-oriented properties using satisfiability modulo theory (SMT) solver tools. We investigate the applicability of our approach by applying it to both randomly generated FFSMs as well as those from a realistic case study (the Body Comfort System). The results of our study show that for random FFSMs over 16 independent non-mandatory features, our technique provides substantial efficiency gains for the set of proposed validity checks.

show abstract

Section: Introductionmentioning

confidence: 99%