A Formal Framework for Specifying and Verifying Microservices Based Process Flows

Camilli, Matteo; Bellettini, Carlo; Capra, Lorenzo; Monga, Mattia

doi:10.1007/978-3-319-74781-1_14

Cited by 17 publications

(20 citation statements)

References 24 publications

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“…The IUQ problem is recently drawing increasing attention, because uncertainty quantification of a model and its inference from the true system response(s) are crucial for engineering reliable systems. As an example, consider the category of service-based systems [21] characterized by a workflow of interactions with distributed components (e.g., web-services or microservices) owned by multiple third-party providers with different quality of service (QoS) attributes Â, such as reliability, performance and cost. In this context, both functional and non-functional aspects of the final system depend on the ability of the external services to comply with the design-time assumptions during the early design-time specification phases.…”

Section: Problem Statementmentioning

confidence: 99%

“…To alert the squad, the TAS workflow invokes the handle emergency activity of the emergency service. The TAS is an example of a wide category of service-based systems (e.g., e-commerce, e-health, online banking and taxi-hailing) characterized by a workflow of interactions with distributed components (e.g., web-services or microservices) owned by multiple third-party providers with different QoS attributes, such as reliability, performance and cost as described by Camilli et al [21]. As anticipated in Section 2, the overall functional and non-functional quality attributes of the final system also depend on the capability of the external services to comply with the assumptions made at design-time during the specification of the system.…”

Section: A Running Example: the Tele Assistance Systemmentioning

confidence: 99%

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Model‐based hypothesis testing of uncertain software systems

Camilli

Gargantini

Scandurra

2020

Software Testing Verif & Rel

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Nowadays, there exists an increasing demand for reliable software systems able to fulfill their requirements in different operational environments and to cope with uncertainty that can be introduced both at design-time and at runtime because of the lack of control over third-party system components and complex interactions among software, hardware infrastructures and physical phenomena. This article addresses the problem of the discrepancy between measured data at runtime and the design-time formal specification by using an inverse uncertainty quantification approach. Namely, we introduce a methodology called METRIC and its supporting toolchain to quantify and mitigate software system uncertainty during testing by combining (on-the-fly) model-based testing and Bayesian inference. Our approach connects probabilistic input/output conformance theory with statistical hypothesis testing in order to assess if the behaviour of the system under test corresponds to its probabilistic formal specification provided in terms of a Markov decision process. An uncertainty-aware model-based test case generation strategy is used as a means to collect evidence from software components affected by sources of uncertainty. Test results serve as input to a Bayesian inference process that updates beliefs on model parameters encoding uncertain quality attributes of the system under test. This article describes our approach from both theoretical and practical perspectives. An extensive empirical evaluation activity has been conducted in order to assess the cost-effectiveness of our approach. We show that, under same effort constraints, our uncertainty-aware testing strategy increases the accuracy of the uncertainty quantification process up to 50 times with respect to traditional model-based testing methods. of uncertainty away because of insufficient understanding and unavailability of adequate methods and techniques. Today, endowing conventional software engineering methodologies with techniques and practices able to model, quantify, and manage uncertainty explicitly is becoming increasingly crucial [1,2]. In particular, techniques that explicitly consider uncertainty in software testing is an emerging research direction.This article focuses on the problem of uncertainty quantification and introduces a methodology able to deal with it by means of a model-based hypothesis testing approach. Namely, we use statistical hypothesis testing to reduce the discrepancy between delivered products and initial design-time uncertainty assumptions. As described by Broy at al.[3], model-based testing (MBT) is a software testing technique where runtime behaviour of a software system under test (SUT) is checked against predictions made by a model description of the system's behaviour. In our approach, the design-time model description contains uncertain aspects explicitly specified by means of probability. Uncertainty must be mitigated accounting for evidence during the actual system's execution. Thus, we make use of (on-the-fly) MBT as a means to extrac...

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Section: Problem Statementmentioning

confidence: 99%

Section: A Running Example: the Tele Assistance Systemmentioning

confidence: 99%

Model‐based hypothesis testing of uncertain software systems

Camilli

Gargantini

Scandurra

2020

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

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“…Recently, web-service verification has been a subject of interest to researchers as it is a means to prove that services implementation complies with the rules and policies that service designers define at a business level. It is still a non-trivial task to verify distributed systems without a coordination module (a gateway in traditional SOA terms), but the advancements of formal methods are promising even if still incomplete and costly, as shown by Camilli et al [2] and Panda et al [11]. For such verification, a formal specification is a necessary but insufficient condition.…”

Section: Services In Business and Itmentioning

confidence: 99%

Co-design of Business and IT Services - A Tool-Supported Approach

Pirelli

Nessler

Wegmann

2019

Lecture Notes in Computer Science

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Service modeling is an important step in designing serviceoriented systems. There are multiple levels of design because service science includes both the business rationale and the IT implementation of the services. As business and IT perspectives differ, the modeling techniques are different, and often the respective modeling languages are disconnected or ad-hoc. We propose a new service-modeling approach for connecting the business modeling and the web service modeling by presenting these two perspectives in a single model. We present a multistage modeling process for capturing different perspectives and creating models iteratively by working with levels of abstraction from higher to lower. The model is then used as an input in order to generate a REST API specification in the OpenAPI format to feed the next stages of the service life-cycle.

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“…Furthermore, aspects such as messaging, communication protocols, which are commonly used in concurrent or distributed systems, can be difficult to model with the language primitives of timed-automata [22,21]. Moreover, TB nets are supported by powerful off-the-shelf open source software tools able to perform simulation, model checking [23,24,25,26], model-based testing, and runtime verification [27,28].…”

Section: Time Basic Petri Netsmentioning

confidence: 99%

Zone-based formal specification and timing analysis of real-time self-adaptive systems

Camilli

Gargantini

Scandurra

2018

Science of Computer Programming

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Self-adaptive software systems are able to autonomously adapt their behavior at run-time to react to internal dynamics and to uncertain and changing environment conditions. Formal specification and verification of self-adaptive systems are tasks generally very difficult to carry out, especially when involving time constraints. In this case, in fact, the system correctness depends also on the time associated with events.This article introduces the Zone-based Time Basic Petri nets specification formalism. The formalism adopts timed adaptation models to specify self-adaptive behavior with temporal constraints, and relies on a zone-based modeling approach to support separation of concerns. Zones identified during the modeling phase can be then used as modules either in isolation, to verify intra-zone properties, or all together, to verify inter-zone properties over the entire system. In addition, the framework allows the verification of (timed) robustness properties to guarantee self-healing capabilities when higher levels of reliability and availability are required to the system, especially when dealing with time-critical systems. This article presents also the ZAFETY tool, a Java software implementation of the proposed framework, and the validation and experimental results obtained in modeling and verifying two time-critical self-adaptive systems: the Gas Burner system and the Unmanned Aerial Vehicle system.

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A Formal Framework for Specifying and Verifying Microservices Based Process Flows

Cited by 17 publications

References 24 publications

Model‐based hypothesis testing of uncertain software systems

Model‐based hypothesis testing of uncertain software systems

Co-design of Business and IT Services - A Tool-Supported Approach

Zone-based formal specification and timing analysis of real-time self-adaptive systems

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