@article{19499,
  abstract     = {Quantum hardware is inherently fragile and noisy. We find that the accuracy of traditional quantum error correction algorithms can be improved depending on the hardware. Given different hardware specifications, we automatically synthesize hardware-optimal algorithms for parity correction, qubit resetting, and GHZ (Greenberger–Horne–Zeilinger) state preparation. Using stochastic techniques from computer science, our method presents a computational tool to compute exact accuracy guarantees and synthesize optimal algorithms that are often different from traditional ones. We also show that improvements can be gained with respect to the Qiskit transpiler as we compute the hardware-optimal qubit mapping for the GHZ state-preparation problem.},
  author       = {Muroya Lei, Stefanie and Chatterjee, Krishnendu and Henzinger, Thomas A},
  issn         = {1091-6490},
  journal      = {Proceedings of the National Academy of Sciences},
  number       = {12},
  publisher    = {National Academy of Sciences},
  title        = {{Hardware-optimal quantum algorithms}},
  doi          = {10.1073/pnas.2419273122},
  volume       = {122},
  year         = {2025},
}

@article{18169,
  abstract     = {As the complexity and criticality of software increase every year, so does the importance of runtime monitoring. Third-party and best-effort monitoring are especially valuable, yet under-explored areas of runtime monitoring. In this context, third-party monitoring means monitoring with a limited knowledge of the monitored software (as it has been developed by a third party). Best-effort monitoring keeps pace with the monitored software at the cost of possibly imprecise verdicts when keeping up with the monitored software would not be feasible. Most existing monitoring frameworks do not support the combination of third-party and best-effort monitoring because they either require the full access to the monitored code or the ability to process all observable events, or both.
We present a middleware framework, Vamos, for the runtime monitoring of software. Vamos is explicitly designed to support third-party and best-effort scenarios. The design goals of Vamos are (i) efficiency (tracing events with low overhead), (ii) flexibility (the ability to monitor a variety of different event channels, and to connect to a wide range of monitors), and (iii) ease-of-use. To achieve its goals, Vamos combines aspects of event broker and event recognition systems with aspects of stream processing systems.
We implemented a prototype toolchain for Vamos and conducted a set of experiments demonstrating the usability of the scheme. The results indicate that Vamos enables writing useful yet efficient monitors, and simplifies key aspects of setting up a monitoring system from scratch.},
  author       = {Chalupa, Marek and Mühlböck, Fabian and Muroya Lei, Stefanie and Henzinger, Thomas A},
  issn         = {0167-6423},
  journal      = {Science of Computer Programming},
  number       = {2},
  publisher    = {Elsevier},
  title        = {{VAMOS: Middleware for best-effort third-party monitoring}},
  doi          = {10.1016/j.scico.2024.103212},
  volume       = {240},
  year         = {2025},
}

@inproceedings{18175,
  abstract     = {Large-scale software repositories are a source of insights for software engineering. They offer an unmatched window into the software development process at scale. Their sheer number and size holds the promise of broadly applicable results. At the same time, that very size presents practical challenges for scaling tools and algorithms to millions of projects. A reasonable approach is to limit studies to representative samples of the population of interest. Broadly applicable conclusions can then be obtained by generalizing to the entire population. The contribution of this paper is a standardized experimental design methodology for choosing the inputs of studies working with large-scale repositories. We advocate for a methodology that clearly lays out what the population of interest is, how to sample it, and that fosters reproducibility. Along the way, we discourage researchers from using extrinsic attributes of projects such as stars, that measure some unclear notion of popularity.},
  author       = {Maj, Petr and Muroya Lei, Stefanie and Siek, Konrad and Di Grazia, Luca and Vitek, Jan},
  booktitle    = {38th European Conference on Object-Oriented Programming},
  isbn         = {9783959773416},
  issn         = {1868-8969},
  location     = {Vienna, Austria},
  publisher    = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
  title        = {{The fault in our stars: Designing reproducible large-scale code analysis experiments}},
  doi          = {10.4230/LIPIcs.ECOOP.2024.27},
  volume       = {313},
  year         = {2024},
}

@misc{12407,
  abstract     = {As the complexity and criticality of software increase every year, so does the importance of run-time monitoring. Third-party monitoring, with limited knowledge of the monitored software, and best-effort monitoring, which keeps pace with the monitored software, are especially valuable, yet underexplored areas of run-time monitoring. Most existing monitoring frameworks do not support their combination because they either require access to the monitored code for instrumentation purposes or the processing of all observed events, or both.

We present a middleware framework, VAMOS, for the run-time monitoring of software which is explicitly designed to support third-party and best-effort scenarios. The design goals of VAMOS are (i) efficiency (keeping pace at low overhead), (ii) flexibility (the ability to monitor black-box code through a variety of different event channels, and the connectability to monitors written in different specification languages), and (iii) ease-of-use. To achieve its goals, VAMOS combines aspects of event broker and event recognition systems with aspects of stream processing systems.

We implemented a prototype toolchain for VAMOS and conducted experiments including a case study of monitoring for data races. The results indicate that VAMOS enables writing useful yet efficient monitors, is compatible with a variety of event sources and monitor specifications, and simplifies key aspects of setting up a monitoring system from scratch.},
  author       = {Chalupa, Marek and Mühlböck, Fabian and Muroya Lei, Stefanie and Henzinger, Thomas A},
  issn         = {2664-1690},
  keywords     = {runtime monitoring, best effort, third party},
  pages        = {38},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{VAMOS: Middleware for Best-Effort Third-Party Monitoring}},
  doi          = {10.15479/AT:ISTA:12407},
  year         = {2023},
}

@inproceedings{12856,
  abstract     = {As the complexity and criticality of software increase every year, so does the importance of run-time monitoring. Third-party monitoring, with limited knowledge of the monitored software, and best-effort monitoring, which keeps pace with the monitored software, are especially valuable, yet underexplored areas of run-time monitoring. Most existing monitoring frameworks do not support their combination because they either require access to the monitored code for instrumentation purposes or the processing of all observed events, or both.

We present a middleware framework, VAMOS, for the run-time monitoring of software which is explicitly designed to support third-party and best-effort scenarios. The design goals of VAMOS are (i) efficiency (keeping pace at low overhead), (ii) flexibility (the ability to monitor black-box code through a variety of different event channels, and the connectability to monitors written in different specification languages), and (iii) ease-of-use. To achieve its goals, VAMOS combines aspects of event broker and event recognition systems with aspects of stream processing systems.
We implemented a prototype toolchain for VAMOS and conducted experiments including a case study of monitoring for data races. The results indicate that VAMOS enables writing useful yet efficient monitors, is compatible with a variety of event sources and monitor specifications, and simplifies key aspects of setting up a monitoring system from scratch.},
  author       = {Chalupa, Marek and Mühlböck, Fabian and Muroya Lei, Stefanie and Henzinger, Thomas A},
  booktitle    = {Fundamental Approaches to Software Engineering},
  isbn         = {9783031308253},
  issn         = {1611-3349},
  location     = {Paris, France},
  pages        = {260--281},
  publisher    = {Springer Nature},
  title        = {{Vamos: Middleware for best-effort third-party monitoring}},
  doi          = {10.1007/978-3-031-30826-0_15},
  volume       = {13991},
  year         = {2023},
}