Industrial elevator systems are commonly used software systems in our daily lives, which operate in uncertain environments such as unpredictable passenger traffic, uncertain passenger attributes and behaviors, and hardware delays. Understanding and assessing the robustness of such systems under various uncertainties enable system designers to reason about uncertainties, especially those leading to low system robustness, and consequently improve their designs and implementations in terms of handling uncertainties. To this end, we present a comprehensive empirical study conducted with industrial elevator systems provided by our industrial partner Orona, which focuses on assessing the robustness of a dispatcher, i.e., a software component responsible for elevators’ optimal scheduling. In total, we studied 90 industrial dispatchers in our empirical study. Based on the experience gained from the study, we derived an uncertainty-aware robustness assessment method (named
UncerRobua
) comprising a set of guidelines on how to conduct the robustness assessment and a newly proposed ranking algorithm, for supporting the robustness assessment of industrial elevator systems against uncertainties.
Orona is a world-renowned elevators developer. During elevators' lives, their software continues to evolve, e.g., due to hardware obsolescence, requirements changes, vulnerabilities, and bug corrections. Such continuous evolution demands the continuous testing of industrial elevators with the minimum manual effort possible. To this end, we present a tool, whose core component is a domain-specific language (DSL) with which a user can specify test oracles at a higher level of abstraction and independent of a testing level. The DSL also supports specifying uncertainty-aware test oracles to test elevators under various uncertainties inherent in them. Finally, the DSL is also equipped with test oracle generation that generates test oracle code automatically at the different DevOps testing levels (i.e., Software and Hardware-in-the-Loop test levels, and in operation) to enable reuse of test oracles across these levels. We evaluated this DSL with an industrial elevators case study at Orona's site to specify and generate test oracles. The evaluation showed that the high expressiveness of the DSL permits the high-level definition of test oracles in our industrial context. Based on the industrial application, we discuss our experiences and lessons learned.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.