Using CP in Automatic Test Generation for ABB Robotics’ Paint Control System

Mossige, Morten; Gotlieb, Arnaud; Meling, Hein

doi:10.1007/978-3-319-10428-7_6

Cited by 13 publications

(4 citation statements)

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“…To achieve this goal, collections of real-world and industry-based data and scenarios-which in our opinion should be conducted by industrial practitioners-are required for generation test cases, as mentioned by Cleland-Huang et al (2018). In this light, automatic test generation (Nebut et al 2006) could complement the automated testing of variant-rich systems as a cost-efficient and reliable solution, as discussed by Mossige et al (2014).…”

Section: Variability-related Challengesmentioning

confidence: 99%

Software variability in service robotics

et al. 2022

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Robots artificially replicate human capabilities thanks to their software, the main embodiment of intelligence. However, engineering robotics software has become increasingly challenging. Developers need expertise from different disciplines as well as they are faced with heterogeneous hardware and uncertain operating environments. To this end, the software needs to be variable—to customize robots for different customers, hardware, and operating environments. However, variability adds substantial complexity and needs to be managed—yet, ad hoc practices prevail in the robotics domain, challenging effective software reuse, maintenance, and evolution. To improve the situation, we need to enhance our empirical understanding of variability in robotics. We present a multiple-case study on software variability in the vibrant and challenging domain of service robotics. We investigated drivers, practices, methods, and challenges of variability from industrial companies building service robots. We analyzed the state-of-the-practice and the state-of-the-art—the former via an experience report and eleven interviews with two service robotics companies; the latter via a systematic literature review. We triangulated from these sources, reporting observations with actionable recommendations for researchers, tool providers, and practitioners. We formulated hypotheses trying to explain our observations, and also compared the state-of-the-art from the literature with the-state-of-the-practice we observed in our cases. We learned that the level of abstraction in robotics software needs to be raised for simplifying variability management and software integration, while keeping a sufficient level of customization to boost efficiency and effectiveness in their robots’ operation. Planning and realizing variability for specific requirements and implementing robust abstractions permit robotic applications to operate robustly in dynamic environments, which are often only partially known and controllable. With this aim, our companies use a number of mechanisms, some of them based on formalisms used to specify robotic behavior, such as finite-state machines and behavior trees. To foster software reuse, the service robotics domain will greatly benefit from having software components—completely decoupled from hardware—with harmonized and standardized interfaces, and organized in an ecosystem shared among various companies.

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Section: Variability-related Challengesmentioning

confidence: 99%

Software variability in service robotics

et al. 2022

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“…Furthermore, an estimation of the test case durations on the available agents has to be provided. This can either be gathered from historical execution data and then (over-)estimated to account for differences in execution machines, or, for some kinds to test suites, they are fixed and can be precisely given [26], e.g. for robotic applications where the duration is determined by the movement of the robot.…”

Section: Implementation and Exploitationmentioning

confidence: 99%

“…We generated a benchmark library containing 840 OTS instances. 10 The library is structured by data collected from three different real-world test suites, provided by our industrial partners: a test suite for video conferencing systems (VCS) [24], a test suite for integrated painting systems (IPS) [26], and a test suite for a mobile application called TV-everywhere.…”

Section: Experimental Artifactsmentioning

confidence: 99%

Time-Aware Test Case Execution Scheduling for Cyber-Physical Systems

Mossige

Gotlieb

Spieker

et al. 2017

Lecture Notes in Computer Science

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Testing cyber-physical systems involves the execution of test cases on target-machines equipped with the latest release of a software control system. When testing industrial robots, it is common that the target machines need to share some common resources, e.g., costly hardware devices, and so there is a need to schedule test case execution on the target machines, accounting for these shared resources. With a large number of such tests executed on a regular basis, this scheduling becomes difficult to manage manually. In fact, with manual test execution planning and scheduling, some robots may remain unoccupied for long periods of time and some test cases may not be executed. This paper introduces TC-Sched, a time-aware method for automated test case execution scheduling. TC-Sched uses Constraint Programming to schedule tests to run on multiple machines constrained by the tests' access to shared resources, such as measurement or networking devices. The CP model is written in SICStus Prolog and uses the Cumulatives global constraint. Given a set of test cases, a set of machines, and a set of shared resources, TC-Sched produces an execution schedule where each test is executed once with minimal time between when a source code change is committed and the test results are reported to the developer. Experiments reveal that TC-Sched can schedule 500 test cases over 100 machines in less than 4 minutes for 99.5% of the instances. In addition, TC-Sched largely outperforms simpler methods based on a greedy algorithm and is suitable for deployment on industrial robot testing.

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“…We used a combination of constraint propagation with different filtering consistencies and dedicated search heuristics. With these techniques it was possible to propose a cost‐effective solution for test case generation for validating the control systems of ABB's painting robots (Mossige, Gotlieb, and Meling 2015; Mossige, Gotliev, and Meling 2014a, 2014b). Currently, these AI techniques are commonly supported by modern CP solvers that ease their adoption in industrial contexts.…”

mentioning

confidence: 99%