This paper describes a new approach to testing that uses combinatorial designs to generate tests that cover the pairwise, triple, or n-way combinations of a system's test parameters. These are the parameters that determine the system's test scenarios. Examples are system configuration parameters, user inputs and other external events. We implemented this new method in the AETG system. The AETG system uses new combinatorial algorithms to generate test sets that cover all valid n-way parameter combinations. The size of an AETG test set grows logarithmically in the number of test parameters. This allows testers to define test models with dozens of parameters. The AETG system is used in a variety of applications for unit, system, and interoperability testing. It has generated both high-level test plans and detailed test cases. In several applications, it greatly reduced the cost of test plan development.

The combinatorial design ethod substantially reduces testing costs. The authors describe an application in which the method reduced test plan development from one month to less than a week. In several experiments, the method demonstrated good code coverage and fault detection ability.

Model-based testing is a new and evolving technique for generating a suite of test cases from requirements. Testers using this approach concentrate on a data model and generation infrastructure instead of hand-crafting individual tests. Several relatively small studies have demonstrated how combinatorial test generation techniques allow testers to achieve broad coverage of the input domain with a small number of tests. We have conducted several relatively large projects in which we applied these techniques to systems with millions of lines of code. Given the complexity of testing, the modelbased testing approach was used in conjunction with test automation harnesses. Since no large empirical study has been conducted to measure efficacy of this new approach, we report on our experience with developing tools and methods in support of model-based testing. The four case studies presented here offer details and results of applying combinatorial test-generation techniques on a large scale to diverse applications. Based on the four projects, we offer our insights into what works in practice and our thoughts about obstacles to transferring this technology into testing organizations.

No abstract

BackgroundThis paper has two goals. First, we explore the feasibility of conducting online expert panels to facilitate consensus finding among a large number of geographically distributed stakeholders. Second, we test the replicability of panel findings across four panels of different size.MethodWe engaged 119 panelists in an iterative process to identify definitional features of Continuous Quality Improvement (CQI). We conducted four parallel online panels of different size through three one-week phases by using the RAND's ExpertLens process. In Phase I, participants rated potentially definitional CQI features. In Phase II, they discussed rating results online, using asynchronous, anonymous discussion boards. In Phase III, panelists re-rated Phase I features and reported on their experiences as participants.Results66% of invited experts participated in all three phases. 62% of Phase I participants contributed to Phase II discussions and 87% of them completed Phase III. Panel disagreement, measured by the mean absolute deviation from the median (MAD-M), decreased after group feedback and discussion in 36 out of 43 judgments about CQI features. Agreement between the four panels after Phase III was fair (four-way kappa = 0.36); they agreed on the status of five out of eleven CQI features. Results of the post-completion survey suggest that participants were generally satisfied with the online process. Compared to participants in smaller panels, those in larger panels were more likely to agree that they had debated each others' view points.ConclusionIt is feasible to conduct online expert panels intended to facilitate consensus finding among geographically distributed participants. The online approach may be practical for engaging large and diverse groups of stakeholders around a range of health services research topics and can help conduct multiple parallel panels to test for the reproducibility of panel conclusions.