We’re excited to announce that Ezio Bartocci, Hongkai Chen, Zeyu Zhang, Shouvik Roy, Scott A. Smolka, Scott D. Stoller, and Shan Lin have won the EMSOFT Best Paper Award at Embedded Systems Week 2025 for their paper “Cumulative-Time Signal Temporal Logic”!

Signal Temporal Logic (STL) is used to express timing requirements in cyber-physical systems, such as microgrids and medical devices; however, it cannot capture how long a condition remains true over time. To address this gap, the authors introduce Cumulative-Time Signal Temporal Logic (CT-STL), which adds an operator that measures the total duration a property holds within a given interval. The authors formally define how CT-STL works, both in terms of true/false logic and in measuring how strongly conditions hold, and prove these definitions are mathematically reliable. They also developed a monitoring algorithm and demonstrated CT-STL’s effectiveness through real-world examples.

Embedded Systems Week (ESWEEK) is the leading international event dedicated to every aspect of hardware and software design for intelligent, connected computing systems. Uniting three major conferences—CASES, CODES+ISSS, and EMSOFT, along with the MEMOCODE symposium —ESWEEK offers attendees a comprehensive view of the latest advances and innovations in embedded systems research and development, featuring numerous workshops, tutorials, and educational sessions.

Congratulations to Ezio, Hongkai Chen, Zeyu Zhang, Shouvik Roy, Scott A. Smolka, Scott D. Stoller, and Shan Linon this outstanding achievement!

Abstract

Signal Temporal Logic (STL) is a widely adopted specification language in cyber-physical systems for expressing critical temporal requirements, such as safety conditions and response time. However, STL’s expressivity is not sufficient to capture the cumulative duration during which a property holds within an interval of time. To overcome this limitation, we introduce Cumulative-Time Signal Temporal Logic (CT-STL) that operates over discrete-time signals and extends STL with a new cumulative-time operator. This operator compares the sum of all time steps for which its nested formula is true with a threshold. We present both a qualitative and a quantitative (robustness) semantics for CT-STL and prove both their soundness and completeness properties. We provide an efficient online monitoring algorithm for both semantics. Finally, we show the applicability of CT-STL in two case studies: specifying and monitoring cumulative temporal requirements for a microgrid and an artificial pancreas.

About Ezio Bartocci

Ezio Bartocci is a Professor in the Research Unit Cyber-Physical Systems at TU Wien Informatics, where he leads the Trustworthy Cyber-Physical Systems (TrustCPS) group. His research focuses on developing formal methods and computational tools that ensure the safety, security, energy-efficiency, and correctness of AI-based CPS with a strong emphasis on sustainability.

Ezio earned his BSc in Computer Science, his MSc in Bioinformatics, and his PhD in Information Science and Complex Systems from the University of Camerino, Italy. From 2010-2012, he conducted postdoctoral research at Stony Brook University (NY, US), contributing to the American NSF-funded CMACS project (coordinated by the Turing-Award Winner Prof. Edmund Clarke) on computational cardiac dynamics. He joined TU Wien Informatics as a University Assistant in 2012, became a tenure-track Assistant Professor in 2015, an Associate Professor in 2019, and was appointed a Full Professor in 2020.

He serves as Vice-Chair of the Marie Skłodowska-Curie COFUND doctoral programme LogiCS@TUWien and as Coordinator of the TrustCPS Special Interest Group within the TU Wien Cybersecurity Center (CySec). Since 2024, he has been Head of the Doctoral College on Trustworthy Autonomous Cyber-Physical Systems, and since 2025, Research Focus Coordinator for Computer Engineering at the Faculty of Informatics, TU Wien. His research has been recognized with several Best Paper Awards, including at EMSOFT 2025, QEST 2022, ADHS 2021, and RV 2011, as well as the Radhia Cousot Young Researcher Best Paper Award (SAS 2022) and the EASST Best Software Science Award (ETAPS 2022).

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