Passive testing continuously observes the system or system execution logs without any interference or instrumentation to test diverse combinations of functions, resulting in a more thorough evaluation over time. However, reaching a working solution to passive testing is not without challenges. While there have been some efforts to extract information from system requirements to create passive test cases, to our knowledge, no such efforts are mature enough to be applied in a real, industrial safety-critical context. Our passive testing approach uses the Timed – Easy Approach to Requirements Syntax (T-EARS) specification language and its accompanying tool-chain. This study reports challenges and solutions to introducing system-level passive testing for a vehicular safety-critical system through industrial data analysis, including 116 safety-related requirements. Our results show that passive testing using the T-EARS language and its tool-chain can be used for system-level testing in an industrial setting for 64% of the studied requirements. We identified several sources of false positive results and show how to tune test cases to reduce such false positives systematically. Finally, we show the requirement coverage achieved by a manual test session and that passive testing using T-EARS can find a set of injected faults that are considered hard to find with other test techniques.
Authors: Daniel Flemström, Henrik Jonsson, Eduard Paul Enoiu, Wasif Afzal
Title of the source: IEEE Conference on Software Testing, Validation and Verification 2021
Relevant pages: 351-361
Year: 2021More info
It is a well-recognized fact that a Cyber-Physical System (CPS) experiences uncertain (including unknown) situations during their operations. Some of such uncertainties could potentially lead to failures of CPS operations. Factors contribute to such uncertainties include 1) the intrinsically unpredictable physical environment of a CPS, 2) the use of communication networks continuously experiencing problems (e.g., slower connection than expected), and 3) the increasing use of machine learning algorithms in CPSs which introduce inherent uncertainties to these CPSs.
No matter how meticulously a CPS is designed and developed, it is impossible to predict all possible uncertain situations it will experience during its operation. Thus, there is a need for new methods for discovering and handling uncertain situations during the CPS operation to prevent it from failure. In this paper, we present our ideas on how digital twins, i.e., “live models” of CPSs can help in discovering and handling potentially unsafe situations during its operation.
We present the research challenges and potential solutions to develop, deploy, and operate such digital twins.
Authors: Shaukat Ali & Tao Yue, from Simula Research Laboratory, Norway