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Increasing the robustness of software components through automated testing

In the 6th issue of Signal + Draht (6/2021) an approach is presented, that utilises new technologies from the areas of IT security and machine learning to make software testing more efficient and future-proof, especially in the railroad industry.

Development and test teams are currently faced with the challenge of having to provide proof of software correctness ever more quickly, even though the catalogue of requirements is constantly increasing. In addition, they must take into account increasingly higher quality targets and comply with them, especially with regard to robustness and reliability. One current topic within this context is “cybersecurity” which places new demands on development projects driven by the increasing use of networking. The topic of cybersecurity has been driven by the development of the legal framework both at the European and national levels and in connection with the protection of critical infrastructures.

Benjamin Mensing, Matthias Rathing, Florian Haux and PD Dr.-Ing. Lars Schnieder present an approach that utilises new technologies from the areas of IT security and machine learning to make software testing more efficient and future-proof, especially in the railroad industry. In their approach, “intelligent fuzzers” are used to this end and tested within the context of verification in the environment of a SIL4 ETCS (European Train Control System) software component from a Radio Block Center (RBC). The focus here is on working out the added value of a fuzzer integrated into an existing verification process with as little additional effort as possible.

If you are interested in the complete article and the results, please send us an e-mail to pr(at)ese.de. Please mention the title of the article.