Automated support for diagnosis and repair

Alrajeh, Dalal; Kramer, Jeff; Russo, Alessandra; Uchitel, Sebastián

doi:10.1145/2658986

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…The semantics of cloud modeling languages often reflects English prose [5]. This results in a shortage of formal solutions for synthesizing requirements across different layers of cloud applications, creating a gap with respect to advanced tools for automated synthesis of requirements arising from business goals and user scenarios outside the cloud context [6,7]. DevOps and continuous delivery (CD) also call for extension to existing formalization methods to simplify decomposition, synthesis and refinement of requirements up to the orchestration layer.…”

Section: Requirements Formalizationmentioning

confidence: 99%

RADON: rational decomposition and orchestration for serverless computing

Casale

Artač

Heuvel

et al. 2019

SICS Softw.-Inensiv. Cyber-Phys. Syst.

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Emerging serverless computing technologies, such as function as a service (FaaS), enable developers to virtualize the internal logic of an application, simplifying the management of cloud-native services and allowing cost savings through billing and scaling at the level of individual functions. Serverless computing is therefore rapidly shifting the attention of software vendors to the challenge of developing cloud applications deployable on FaaS platforms. In this vision paper, we present the research agenda of the RADON project (http://radon-h2020.eu), which aims to develop a model-driven DevOps framework for creating and managing applications based on serverless computing. RADON applications will consist of fine-grained and independent microservices that can efficiently and optimally exploit FaaS and container technologies. Our methodology strives to tackle complexity in designing such applications, including the solution of optimal decomposition, the reuse of serverless functions as well as the abstraction and actuation of event processing chains, while avoiding cloud vendor lock-in through models.

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Section: Requirements Formalizationmentioning

confidence: 99%

RADON: rational decomposition and orchestration for serverless computing

Casale

Artač

Heuvel

et al. 2019

SICS Softw.-Inensiv. Cyber-Phys. Syst.

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“…Our approach presents a new dimension to the framework described in [5], the integration of model checking with constraint-driven learning. Various instantiations of this framework have been developed (e.g., [2,3,4,6, 1] but all have been concerned with local repairs; they do not address the problem of propagating repairs through a specification.…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…Then for each obstacle O to G of M in D, a finite set of obstruction traces Σ O are obtained from a model satisfying D and O. We generate these by verifying {D, O} |= 2(C ; ΘT ), in the spirit of [5]. To achieve the behaviour preservation requirement expressed in Sec.…”

Section: Approach Overviewmentioning

confidence: 99%

Risk-driven revision of requirements models

Alrajeh

Lamsweerde

Kramer

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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Requirements incompleteness is often the result of unanticipated adverse conditions which prevent the software and its environment from behaving as expected. These conditions represent risks that can cause severe software failures. The identification and resolution of such risks is therefore a crucial step towards requirements completeness. Obstacle analysis is a goal-driven form of risk analysis that aims at detecting missing conditions that can obstruct goals from being satisfied in a given domain, and resolving them. This paper proposes an approach for automatically revising goals that may be under-specified or (partially) wrong to resolve obstructions in a given domain. The approach deploys a learning-based revision methodology in which obstructed goals in a goal model are iteratively revised from traces exemplifying obstruction and non-obstruction occurrences. Our revision methodology computes domain-consistent, obstruction-free revisions that are automatically propagated to other goals in the model in order to preserve the correctness of goal models whilst guaranteeing minimal change to the original model. We present the formal foundations of our learning-based approach, and show that it preserves the properties of our formal framework. We validate it against the benchmarking case study of the London Ambulance Service.

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“…Figure 9 illustrates the various artifacts produced at each step of the method. Problems (1) are progressively decomposed into other problem cases (2). Given the problem case structure, the organizing system can be used for querying and retrieving problems cases that are the most relevant.…”

Section: A Verification Organizing Systemmentioning

confidence: 99%

“…Our research work is focused on methods and tools intended to ease the verification process, especially the diagnosis activities. Generally speaking, research addressing model-checking and diagnosis issues [2,4,6,14,19] are faced with the same difficulties. First, diagnosing the cause of abnormalities suffers from too detailed observations.…”

Section: Introductionmentioning

confidence: 99%

Domain-Oriented Verification Management

Leildé

Ribaud

Teodorov

et al. 2018

Model and Data Engineering

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V. Basili stated twenty years ago that a software organization that manages quality should have a corporate infrastructure that links together and transcends the single projects by capitalizing on successes and learning from failures. For critical systems design, the verification tasks play a crucial role; when an unexpected situation is detected, the engineer analyzes the cause, performing a diagnosis activity. To improve the quality of the design, diagnosis information have to be managed through a well-defined method and with a suitable system. In this paper we present how a Verification Organizing System together with a problem-oriented method could achieve these issues. The key aspect of the approach is to follow a step-wise building of the solution, reusing known problems that are relevant for the system under study.

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Automated support for diagnosis and repair

Abstract: Model checking and logic-based learning together deliver automated support, especially in adaptive and autonomous systems.

Cited by 15 publications

References 23 publications

RADON: rational decomposition and orchestration for serverless computing

RADON: rational decomposition and orchestration for serverless computing

Risk-driven revision of requirements models

Domain-Oriented Verification Management

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