@inproceedings {malik2023:chess,
	title = {CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering},
	journal = {18th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2023)},
	year = {2023},
	month = {05/2023},
	publisher = {IEEE},
	abstract = {There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems due to their adoption in critical and highly dynamic environments. However, there is a lack of systematic evaluation methods for self-adaptive and self-healing systems. We proposed CHESS, a novel approach to address this gap by evaluating self-adaptive and self-healing systems through fault injection based on chaos engineering (CE).The artifact presented in this paper provides an extensive overview of the use of CHESS through two microservice-based case studies: a smart office case study and an existing demo application called Yelb. It comes with a managing system service, a self-monitoring service, as well as five fault injection scenarios covering infrastructure faults and functional faults. Each of these components can be easily extended or replaced to adopt the CHESS approach to a new case study, help explore its promises and limitations, and identify directions for future research.},
	keywords = {artifact, Chaos Engineering, Evaluation, Resilience, Self-Healing},
	author = {Malik, Sehrish and Moeen Ali {Naqvi} and Moonen, Leon}
}
@techreport {malik2023:chess:arxiv,
	title = {CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering},
	year = {2023},
	month = {03/2023},
	publisher = {arXiv},
	abstract = {There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems due to their adoption in critical and highly dynamic environments. However, there is a lack of systematic evaluation methods for self-adaptive and self-healing systems. We proposed CHESS, a novel approach to address this gap by evaluating self-adaptive and self-healing systems through fault injection based on chaos engineering (CE).The artifact presented in this paper provides an extensive overview of the use of CHESS through two microservice-based case studies: a smart office case study and an existing demo application called Yelb. It comes with a managing system service, a self-monitoring service, as well as five fault injection scenarios covering infrastructure faults and functional faults. Each of these components can be easily extended or replaced to adopt the CHESS approach to a new case study, help explore its promises and limitations, and identify directions for future research.},
	keywords = {artifact, Chaos Engineering, Evaluation, Resilience, Self-Healing},
	doi = {10.48550/arXiv.2303.07283},
	url = {https://arxiv.org/abs/2303.07283},
	author = {Malik, Sehrish and Moeen Ali {Naqvi} and Moonen, Leon}
}
@techreport {43170,
	title = {Using Fault Injection to Generate Labeled Anomaly Datasets for Improving Fault Tolerance in Microservice-based Distributed Systems},
	year = {2023},
	publisher = {Simula Research Laboratory},
	address = {Oslo, Norway},
	abstract = {The complex nature of the microservices infrastructure, coupled with the shared and\&nbsp;dynamic environment of the cloud, poses challenges for fault management, making it more\&nbsp;challenging than other types of systems. This paper proposes a novel approach, SynFI,\&nbsp;for generating automated synthetic labeled datasets that address the challenges of effectively monitoring highly distributed microservice systems and making them robust and faulttolerant. SynFI builds on chaos engineering principles, systematically exposing the system\&nbsp;to faults and recording observability data under normal and faulty conditions using monitoring tools. Our approach extracts the fault labels and data metrics from the collected\&nbsp;data and maps them onto a synthetic labeled dataset that includes accurate fault labels. We\&nbsp;analyze the collected data through data quality analysis and test the prediction accuracy for\&nbsp;anomaly detection of the dataset using six popular classification algorithms. Overall, the\&nbsp;paper contributes to developing self-adaptive and self-healing microservices-based systems,\&nbsp;leading to improvements in administration, allocation of microservices across servers, and\&nbsp;quality attributes like reliability, scalability, and resilience.},
	author = {Malik, Sehrish and Moeen Ali {Naqvi} and Moonen, Leon}
}
@inproceedings {naqvi2022:evaluating,
	title = {On Evaluating Self-Adaptive and Self-Healing Systems using Chaos Engineering},
	journal = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)},
	number = {arXiv:2208.13227 [cs.SE]},
	year = {2022},
	month = {11/2022},
	pages = {1-10},
	publisher = {IEEE},
	abstract = {With the growing adoption of self-adaptive systems in various domains, there is an increasing need for strategies to assess their correct behavior. In particular self-healing systems, which aim to provide resilience and fault-tolerance, often deal with unanticipated failures in critical and highly dynamic environments. Their reactive and complex behavior makes it challenging to assess if these systems execute according to the desired goals. Recently, several studies have expressed concern about the lack of systematic evaluation methods for self-healing behavior.In this paper, we propose CHESS, an approach for the systematic evaluation of self-adaptive and self-healing systems that builds on chaos engineering. Chaos engineering is a methodology for subjecting a system to unexpected conditions and scenarios. It has shown great promise in helping developers build resilient microservice architectures and cyber-physical systems. CHESS turns this idea around by using chaos engineering to evaluate how well a self-healing system can withstand such perturbations. We investigate the viability of this approach through an exploratory study on a self-healing smart office environment. The study helps us explore the promises and limitations of the approach, as well as identify directions where additional work is needed. We conclude with a summary of lessons learned.},
	keywords = {Chaos Engineering, Evaluation, Exploratory study, Resilience, Self-Healing},
	isbn = {978-1-6654-7137-4},
	doi = {10.1109/ACSOS55765.2022.00018},
	url = {https://ieeexplore.ieee.org/document/9935013},
	author = {Moeen Ali {Naqvi} and Malik, Sehrish and Astekin, Merve and Moonen, Leon}
}
@techreport {naqvi2022:evaluating:arxiv,
	title = {On Evaluating Self-Adaptive and Self-Healing Systems using Chaos Engineering},
	number = {arXiv:2208.13227},
	year = {2022},
	pages = {1-10},
	publisher = {arXiv},
	abstract = {With the growing adoption of self-adaptive systems in various domains, there is an increasing need for strategies to assess their correct behavior. In particular self-healing systems, which aim to provide resilience and fault-tolerance, often deal with unanticipated failures in critical and highly dynamic environments. Their reactive and complex behavior makes it challenging to assess if these systems execute according to the desired goals. Recently, several studies have expressed concern about the lack of systematic evaluation methods for self-healing behavior.In this paper, we propose CHESS, an approach for the systematic evaluation of self-adaptive and self-healing systems that builds on chaos engineering. Chaos engineering is a methodology for subjecting a system to unexpected conditions and scenarios. It has shown great promise in helping developers build resilient microservice architectures and cyber-physical systems. CHESS turns this idea around by using chaos engineering to evaluate how well a self-healing system can withstand such perturbations. We investigate the viability of this approach through an exploratory study on a self-healing smart office environment. The study helps us explore the promises and limitations of the approach, as well as identify directions where additional work is needed. We conclude with a summary of lessons learned.},
	keywords = {Chaos Engineering, Evaluation, Exploratory study, Resilience, Self-Healing},
	doi = {10.48550/arXiv.2208.13227},
	url = {https://arxiv.org/abs/2208.13227},
	author = {Moeen Ali {Naqvi} and Malik, Sehrish and Astekin, Merve and Moonen, Leon}
}
@inproceedings {naqvi:2021:adaptive,
	title = {Adaptive Immunity for Software: Towards Autonomous Self-healing Systems},
	journal = { 28th IEEE International Conference on  Software Analysis, Evolution and Reengineering (SANER)},
	year = {2021},
	pages = {521-525},
	publisher = {IEEE},
	abstract = {Testing and code reviews are known techniques to improve the qualityand robustness of software.\&nbsp; Unfortunately, the complexity of modernsoftware systems makes it impossible to anticipate all possibleproblems that can occur at runtime, which limits what issues canbe found using testing and reviews.\&nbsp; Thus, it is of interest toconsider autonomous self-healing software systems, which canautomatically detect, diagnose, and contain unanticipated problemsat runtime.\&nbsp; Most research in this area has adopted a model-drivenapproach, where actual behavior is checked against a model specifyingthe intended behavior, and a controller takes action when the systembehaves outside of the specification.\&nbsp; However, it is not easy todevelop these specifications, nor to keep them up-to-date as thesystem evolves.\&nbsp; We pose that, with the recent advances in machinelearning, such models may be learned by observing the system.Moreover, we argue that artificial immune systems (AISs) areparticularly well-suited for building self-healing systems, becauseof their anomaly detection and diagnosis capabilities.\&nbsp; We presentthe state-of-the-art in self-healing systems and in AISs, surveyingsome of the research directions that have been considered up tonow.\&nbsp; To help advance the state-of-the-art, we develop a researchagenda for building self-healing software systems using AISs,identifying required foundations, and promising research directions.},
	keywords = {Anomaly detection, artificial immune systems, dependability, fault containment, runtime diagnosis, Self-Healing},
	isbn = {978-1-7281-9630-5},
	doi = {10.1109/SANER50967.2021.00058},
	url = {https://ieeexplore.ieee.org/abstract/document/9425979},
	author = {Moeen Ali {Naqvi} and Astekin, Merve and Malik, Sehrish and Moonen, Leon}
}
