The onset of microservices and serverless computer solutions has forced an ever-increasing demand for tools and techniques to establish and maintain the quality of infrastructure code, the blueprint that drives the operationalization of large-scale software systems. In the EU H2020 project RADON, we propose a machine-learning approach to elaborate and evolve Infrastructure-as-Code as part of a full-fledged industrial-strength DevOps pipeline. This paper illustrates RADON and shows our research roadmap

Dalla Palma, Stefano

Di Nucci, Dario

Garriga, Martin

Tamburri, Damian A.

van den Heuvel, Willem-Jan

English

The onset of microservices and serverless computer solutions has forced an ever-increasing demand for tools and techniques to establish and maintain the quality of infrastructure code, the blueprint that drives the operationalization of large-scale software systems. In the EU H2020 project RADON, we propose a machine-learning approach to elaborate and evolve Infrastructure-as-Code as part of a full-fledged industrial-strength DevOps pipeline. This paper illustrates RADON andshows our research roadmap

Tilburg University Repository

    Tilburg UniversityDevOps and Quality Management in Serverless Computing: The RADON ApproachDalla Palma, Stefano; Garriga, Martin; Di Nucci, Dario; Tamburri, Damian A.; van den Heuvel,Willem-JanPublication date:2020Document VersionPublisher's PDF, also known as Version of recordLink to publication in Tilburg University Research PortalCitation for published version (APA):Dalla Palma, S., Garriga, M., Di Nucci, D., Tamburri, D. A., & van den Heuvel, W-J. (2020). DevOps and QualityManagement in Serverless Computing: The RADON Approach. Proceedings of the 8th European ConferenceOn Service-Oriented And Cloud Computing (ESOCC), .General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portalTake down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 12. May. 2021DevOps and Quality Management in ServerlessComputing: The RADON ApproachStefano Dalla Palma1, Martin Garriga1, Dario Di Nucci1,Damian Andrew Tamburri2, and Willem-Jan Van Den Heuvel11 Jheronimus Academy of Data Science, Tilburg University, Netherlands{s.dallapalma, m.garriga, d.dinucci, W.J.A.M.vdnHeuvel}@uvt.nl2 Jheronimus Academy of Data Science, Technical University of Eindhoven,Netherlandsd.a.tamburri@tue.nlAbstract. The onset of microservices and serverless computer solutionshas forced an ever-increasing demand for tools and techniques to es-tablish and maintain the quality of infrastructure code, the blueprintthat drives the operationalization of large-scale software systems. In theEU H2020 project RADON, we propose a machine-learning approachto elaborate and evolve Infrastructure-as-Code as part of a full-fledgedindustrial-strength DevOps pipeline. This paper illustrates RADON andshows our research roadmap.Keywords: Infrastructure Code · Serverless Computing · MicroservicesComputing · Software Quality · DevOps · Machine-Learning for SoftwareQuality · DataOps1 IntroductionNot so long ago, companies and individuals used to provision and manage theirsoftware on their in-house server and computing infrastructure. This behaviorimplied several costs, such as the costs involved in buying and maintain machinesor hiring IT personnel. Therefore, those companies started outsourcing some re-sponsibilities (for example, the management and maintenance of the infrastruc-ture). The cloud has come, which, combined with virtualization and container-ization, laid the ground for Infrastructure-as-a-Service, Platform-as-a-Service,and Software-as-a-Service. These technologies allow for more outsourcing and,as a result, a more focus on the business logic while delegating the managementof the infrastructure to those having great expertise in the field. Therefore, thelead time has shortened, and the creation of software and its deployment hasbecome relatively easier, cheaper, and quicker. Serverless Function-as-a-Service(FaaS) is a step forward in this evolution.Function-as-a-service is a serverless way to execute modular pieces of codeon the edge. FaaS lets developers write and update a piece of code on the fly,which can then be executed in response to an event, such as a user clicking on anelement in a web application. It allows for code scalability and is a cost-efficient2 S. Dalla Palma et al.way to implement microservices. Serverless FaaS came with several advantages,among them:– the business logic is deployed to the host in the cloud as code units in formsof functions, which are fully managed by the provider (e.g., Amazon AWS,Azure Lambda, Google Cloud Functions);– the lead time, that is, the latency between the initiation and completion ofthe development process, has shortened;– less server-side work is needed: the provider is responsible for managing thehost machine. Thus, developers can focus on building the business logic.In general, there are reduced costs and risks. However, serverless FaaS is nota silver bullet, and new challenges arise.From a survey conducted with serverless adopters, recurrent problems inadopting serverless have emerged [2]. First, serverless is still relatively in itsinfancy, and hence only a bunch of best practices for its adoption and operationsare available. Second, there are few design principles and patterns for composingand triggering serverless functions. Third, few definitions of bad practices existin the digital-native technical domain, which should be amended.Furthermore, the rising of serverless solutions, combined with microservices,has forced an ever-increase demand for tools and techniques to establish andmaintain them across the entire DevOps lifecycle [1]. With those challenges inmind, the RADON approach for serverless computing comes to play.2 ObjectivesRADON stands for “rational decomposition and orchestration for serverlesscomputing”, and is a project funded by the Horizon-2020 European program.It aims to unlock the benefits of serverless computing and Function-as-a-Service(FaaS), and broaden their adoption within the European software industry bydeveloping a model-driven DevOps framework and methodology to create andmanage applications based on fine-grained and independently deployable mi-croservices exploiting the serverless paradigm through FaaS and container tech-nologies.In particular, this work introduces the key tools on the RADON methodol-ogy, the user workflow, and their integration and cooperation in the context ofDevOps. The methodology strives to tackle complexity, harmonize the abstrac-tion, enforce action-trigger rules, avoid Faas lock-in, and optimize decompositionand reuse through model-based FaaS-enabled development and orchestration.3 Early ResultsWe have currently defined the RADON Architecture, including (i) several work-flows to organize and display the possible interactions between the tools of theRADON framework and the identified actors and (ii) a tool-chain to defineThe RADON Framework for Quality Infrastructure Code 3microservices-based serverless applications with focusing on design-, development-, and run-time.In particular, RADON envisions a model-based approach to manage andorchestrate modern, distributed, cloud-native application systems that will typ-ically apply a microservice architecture and exploit the FaaS model. The overallRADON framework features rotate around the RADON modeling environment.The other tools are responsible for the correctness and quality of the generatedartifacts, such as the Verification, Continuous Testing, and Defect Predictiontools. The framework uses the Topology and Orchestration Specification forCloud Applications (i.e., OASIS TOSCA) [3] as a baseline to define the RADONmodels. TOSCA describes the topology generated via the Graphical Modelingtool and the orchestration of cloud applications in a declarative manner. Theorchestration process takes place through the Orchestration tool, making it pos-sible to integrate changes and deploy and monitor the application. An overviewof the architecture is depicted in Figure 1.RADONIDEGraphical Modelling ToolDecomposition ToolDefect Prediction ToolVerification ToolContinuous Testing ToolTOSCAblueprint OrchestratorCI/CDFunction-HubMonitoringData pipelineFig. 1. A broad overview of the RADON framework architecture3.1 Graphical Modeling ToolRather than modeling the environment by manually define TOSCA blueprints,the graphical modeling tool (GMT) enables the creation, development, and mod-eling of TOSCA applications through a web-based software solution. Its maingoal is combing TOSCA service templates that represent the applications de-ployed using the RADON Orchestrator. RADON users can then package theirapplications as a CSAR (i.e., Cloud Service Archive) before it is deployed intoproduction by using the RADON Orchestrator.4 S. Dalla Palma et al.In the first year of the project, the main achievements provided the foun-dation to graphically maintain RADON applications using the TOSCA SimpleProfile standard in version 1.3. Relying on Eclipse Winery as a baseline and byextending it with the respective YAML-based modeling features, allowed a com-prehensive modeling tool to graphically (i) create and adapt reusable modelingentities, such as TOSCA Node Types and Policy Types; (ii) compose RADONapplication structures in the form of TOSCA Service Templates; (iii) enrich ex-isting RADON applications with test-related and performance-specific attributesusing TOSCA Policies; and(iv) export a portable archive containing all informa-tion to execute the deployment by using the RADON Orchestrator.3.2 Decomposition ToolThe topology generated by the GMT, or anyone imported into the tool, canbe passed as input to the decomposition tool to find the optimal decompositionsolution for an application based on the microservices architectural style and theserverless FaaS paradigm, taking into account those constraints.The tool provides suggestions to map abstract components to concrete tech-nologies and adjust the topology itself. For example, to split a monolithic appli-cation into microservices or microservices into serverless functions. The feedbackfrom the tool is sent back to the GMT, containing decomposition suggestionsand/or the revised TOSCA model.In the first year of the project, we introduced several modularized approachesto model the performance of applications based on microservices or server-less functions. This tool enables to extend standard languages for IaC, such asTOSCA, with a modeling formalism that describes the behavior of a RADONapplication and predicts its performance. Such an aspect is essential to obtainoptimal deployment schemes.3.3 Defect Prediction ToolThe same topology can be used by the Defect Prediction tool to analyze thecorrectness of the delivered infrastructure code.Indeed, like any other source code artifacts, infrastructure files (such asTOSCA topology definitions or Ansible configuration files) may contain defectsthat can preclude their correct functioning. The quality of these files shouldevolve and be maintained through the entire system’s life-cycle. The defect pre-diction tool supports the correctness of the infrastructure code developed usingthe RADON framework and allows DevOps engineers to focus on critical filesthat may be failure-prone while skipping the others. Thus, allocating resourcesmore efficiently, for example, for testing or code audit.In the first year of the project, we designed and implemented its architec-ture to automatically gather meaningful data to improve model performance.More specifically, we developed a set of tools and framework to (i) automaticallycrawl and mine projects from Github and Gitlab; (ii) extract code and processmetrics from Ansible playbooks and TOSCA blueprints; (iii) support DevOpsThe RADON Framework for Quality Infrastructure Code 5engineers in training defect-prediction models and identifying snapshots of filescontaining defects. The tool exposes RESTful APIs that can be used locally ordeployed online to interact with the defect prediction tool. The APIs and theMongoDB database will be publicly accessible to retrieve the models trainedduring our in-vitro experimentations and those added by the community in thefuture. However, organizations that do not want to expose their data/modelscan deploy the APIs starting from an empty DB on-premises and grant accessto specific users. A command-line client was also developed to use the defectprediction tool in a CI/CD pipeline. It provides functionalities to (i) train amodel from scratch using different configurations in terms of data balancing,normalization, feature selection, and classifiers; (ii) download a model from theonline APIs; (iii) predict unseen instances based on the model trained with (i)or collected with (ii).3.4 Verification ToolThe verification tool enables a user to verify that a RADON model conforms toa set of constraints (e.g., privacy, security, design pattern violations) before de-ployment. While modeling the application via the GMT, the Software Designercan set desired properties and constraints (e.g., security/privacy requirements)using a Constraint Definition Language (CDL). The Software Designer can pro-vide examples of the desired behavior through the RADON IDE or manually.Upon the generated models, the software designer or the DevOps engineer canuse this tool to perform static checking upon their validity. When a violationis encountered (e.g., circular calls, or privacy violations), the engineer can openthe corresponding artifact(s) in the RADON IDE for debugging.During the first year of the project, the CDL and the command-line versionof the verification tool have been developed. It currently supports the verificationand the correction of a RADON model and can detect inconsistencies with theconstraints expressed in the CDL.3.5 Continuous Testing ToolThe testing tool comprises several modules for microservices/FaaS testing anda data pipeline that will support the continuous testing workflow of RADON.It will provide a set of functionalities to support three main usage scenarios: (i)test case definition, (ii) test execution, and (iii) test maintenance. Such scenarioswill help RADON users correctly test their application by creating, executing,inspecting, and removing test cases. In the first year of the project, we specifiedthe requirements of the tool, designed its architecture, envisioned its integrationwith the other tools of the framework, implemented a prototype, and showcaseda proof-of-concept based on two sample applications.6 S. Dalla Palma et al.4 Conclusions and Research RoadmapThe RADON project aims to define a decision-making toolkit to optimize micro-services in terms of size, dependencies, and costs by leveraging a reference setof architectural patterns and service templates [1]. To support this goal, theRADON methodology will integrate a workflow that enables decision makingon architecture optimization through the decomposition tool. To avoid defectiveinfrastructures, it will detect defects through the defect prediction tool. To testthe application, the framework will check the application invariants against thechanges through the testing tool. Furthermore, the methodology will define theoperations and workflows to use the tools and how they should be integratedand should cooperate. Finally, RADON aims at organizing and accelerating themicro-services evolution in a team-based fashion. Security and privacy policieswill be automatically enforced in the run-time environment of the framework toensure protection for sensitive data and services.References1. Guerriero, M., Garriga, M., Tamburri, D.A., Palomba, F.: Adoption, support, andchallenges of infrastructure-as-code: Insights from industry. In: 2019 IEEE Interna-tional Conference on Software Maintenance and Evolution (ICSME). pp. 580–589.IEEE (2019)2. Lenarduzzi, V., Daly, J., Martini, A., Panichella, S., Tamburri, D.: Towards a tech-nical debt conceptualization for serverless computing. IEEE Software (10 2020)3. Matt Rutkowski, Chris Lauwers, C.N., Curescu, C.: Tosca simple pro-file in yaml version 1.3 (2019), http://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.3/TOSCA-Simple-Profile-YAML-v1.3.html

DevOps and Quality Management in Serverless Computing: The RADON Approach

    Tilburg UniversityDevOps and Quality Management in Serverless Computing: The RADON ApproachDalla Palma, Stefano; Garriga, Martin; Di Nucci, Dario; Tamburri, Damian A.; van den Heuvel,Willem-JanPublication date:2020Document VersionPublisher's PDF, also known as Version of recordLink to publication in Tilburg University Research PortalCitation for published version (APA):Dalla Palma, S., Garriga, M., Di Nucci, D., Tamburri, D. A., & van den Heuvel, W-J. (2020). DevOps and QualityManagement in Serverless Computing: The RADON Approach. Proceedings of the 8th European ConferenceOn Service-Oriented And Cloud Computing (ESOCC).General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portalTake down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 26. Sep. 2023DevOps and Quality Management in ServerlessComputing: The RADON ApproachStefano Dalla Palma1, Martin Garriga1, Dario Di Nucci1,Damian Andrew Tamburri2, and Willem-Jan Van Den Heuvel11 Jheronimus Academy of Data Science, Tilburg University, Netherlands{s.dallapalma, m.garriga, d.dinucci, W.J.A.M.vdnHeuvel}@uvt.nl2 Jheronimus Academy of Data Science, Technical University of Eindhoven,Netherlandsd.a.tamburri@tue.nlAbstract. The onset of microservices and serverless computer solutionshas forced an ever-increasing demand for tools and techniques to es-tablish and maintain the quality of infrastructure code, the blueprintthat drives the operationalization of large-scale software systems. In theEU H2020 project RADON, we propose a machine-learning approachto elaborate and evolve Infrastructure-as-Code as part of a full-fledgedindustrial-strength DevOps pipeline. This paper illustrates RADON andshows our research roadmap.Keywords: Infrastructure Code · Serverless Computing · MicroservicesComputing · Software Quality · DevOps · Machine-Learning for SoftwareQuality · DataOps1 IntroductionNot so long ago, companies and individuals used to provision and manage theirsoftware on their in-house server and computing infrastructure. This behaviorimplied several costs, such as the costs involved in buying and maintain machinesor hiring IT personnel. Therefore, those companies started outsourcing some re-sponsibilities (for example, the management and maintenance of the infrastruc-ture). The cloud has come, which, combined with virtualization and container-ization, laid the ground for Infrastructure-as-a-Service, Platform-as-a-Service,and Software-as-a-Service. These technologies allow for more outsourcing and,as a result, a more focus on the business logic while delegating the managementof the infrastructure to those having great expertise in the field. Therefore, thelead time has shortened, and the creation of software and its deployment hasbecome relatively easier, cheaper, and quicker. Serverless Function-as-a-Service(FaaS) is a step forward in this evolution.Function-as-a-service is a serverless way to execute modular pieces of codeon the edge. FaaS lets developers write and update a piece of code on the fly,which can then be executed in response to an event, such as a user clicking on anelement in a web application. It allows for code scalability and is a cost-efficient2 S. Dalla Palma et al.way to implement microservices. Serverless FaaS came with several advantages,among them:– the business logic is deployed to the host in the cloud as code units in formsof functions, which are fully managed by the provider (e.g., Amazon AWS,Azure Lambda, Google Cloud Functions);– the lead time, that is, the latency between the initiation and completion ofthe development process, has shortened;– less server-side work is needed: the provider is responsible for managing thehost machine. Thus, developers can focus on building the business logic.In general, there are reduced costs and risks. However, serverless FaaS is nota silver bullet, and new challenges arise.From a survey conducted with serverless adopters, recurrent problems inadopting serverless have emerged [2]. First, serverless is still relatively in itsinfancy, and hence only a bunch of best practices for its adoption and operationsare available. Second, there are few design principles and patterns for composingand triggering serverless functions. Third, few definitions of bad practices existin the digital-native technical domain, which should be amended.Furthermore, the rising of serverless solutions, combined with microservices,has forced an ever-increase demand for tools and techniques to establish andmaintain them across the entire DevOps lifecycle [1]. With those challenges inmind, the RADON approach for serverless computing comes to play.2 ObjectivesRADON stands for “rational decomposition and orchestration for serverlesscomputing”, and is a project funded by the Horizon-2020 European program.It aims to unlock the benefits of serverless computing and Function-as-a-Service(FaaS), and broaden their adoption within the European software industry bydeveloping a model-driven DevOps framework and methodology to create andmanage applications based on fine-grained and independently deployable mi-croservices exploiting the serverless paradigm through FaaS and container tech-nologies.In particular, this work introduces the key tools on the RADON methodol-ogy, the user workflow, and their integration and cooperation in the context ofDevOps. The methodology strives to tackle complexity, harmonize the abstrac-tion, enforce action-trigger rules, avoid Faas lock-in, and optimize decompositionand reuse through model-based FaaS-enabled development and orchestration.3 Early ResultsWe have currently defined the RADON Architecture, including (i) several work-flows to organize and display the possible interactions between the tools of theRADON framework and the identified actors and (ii) a tool-chain to defineThe RADON Framework for Quality Infrastructure Code 3microservices-based serverless applications with focusing on design-, development-, and run-time.In particular, RADON envisions a model-based approach to manage andorchestrate modern, distributed, cloud-native application systems that will typ-ically apply a microservice architecture and exploit the FaaS model. The overallRADON framework features rotate around the RADON modeling environment.The other tools are responsible for the correctness and quality of the generatedartifacts, such as the Verification, Continuous Testing, and Defect Predictiontools. The framework uses the Topology and Orchestration Specification forCloud Applications (i.e., OASIS TOSCA) [3] as a baseline to define the RADONmodels. TOSCA describes the topology generated via the Graphical Modelingtool and the orchestration of cloud applications in a declarative manner. Theorchestration process takes place through the Orchestration tool, making it pos-sible to integrate changes and deploy and monitor the application. An overviewof the architecture is depicted in Figure 1.RADONIDEGraphical Modelling ToolDecomposition ToolDefect Prediction ToolVerification ToolContinuous Testing ToolTOSCAblueprint OrchestratorCI/CDFunction-HubMonitoringData pipelineFig. 1. A broad overview of the RADON framework architecture3.1 Graphical Modeling ToolRather than modeling the environment by manually define TOSCA blueprints,the graphical modeling tool (GMT) enables the creation, development, and mod-eling of TOSCA applications through a web-based software solution. Its maingoal is combing TOSCA service templates that represent the applications de-ployed using the RADON Orchestrator. RADON users can then package theirapplications as a CSAR (i.e., Cloud Service Archive) before it is deployed intoproduction by using the RADON Orchestrator.4 S. Dalla Palma et al.In the first year of the project, the main achievements provided the foun-dation to graphically maintain RADON applications using the TOSCA SimpleProfile standard in version 1.3. Relying on Eclipse Winery as a baseline and byextending it with the respective YAML-based modeling features, allowed a com-prehensive modeling tool to graphically (i) create and adapt reusable modelingentities, such as TOSCA Node Types and Policy Types; (ii) compose RADONapplication structures in the form of TOSCA Service Templates; (iii) enrich ex-isting RADON applications with test-related and performance-specific attributesusing TOSCA Policies; and(iv) export a portable archive containing all informa-tion to execute the deployment by using the RADON Orchestrator.3.2 Decomposition ToolThe topology generated by the GMT, or anyone imported into the tool, canbe passed as input to the decomposition tool to find the optimal decompositionsolution for an application based on the microservices architectural style and theserverless FaaS paradigm, taking into account those constraints.The tool provides suggestions to map abstract components to concrete tech-nologies and adjust the topology itself. For example, to split a monolithic appli-cation into microservices or microservices into serverless functions. The feedbackfrom the tool is sent back to the GMT, containing decomposition suggestionsand/or the revised TOSCA model.In the first year of the project, we introduced several modularized approachesto model the performance of applications based on microservices or server-less functions. This tool enables to extend standard languages for IaC, such asTOSCA, with a modeling formalism that describes the behavior of a RADONapplication and predicts its performance. Such an aspect is essential to obtainoptimal deployment schemes.3.3 Defect Prediction ToolThe same topology can be used by the Defect Prediction tool to analyze thecorrectness of the delivered infrastructure code.Indeed, like any other source code artifacts, infrastructure files (such asTOSCA topology definitions or Ansible configuration files) may contain defectsthat can preclude their correct functioning. The quality of these files shouldevolve and be maintained through the entire system’s life-cycle. The defect pre-diction tool supports the correctness of the infrastructure code developed usingthe RADON framework and allows DevOps engineers to focus on critical filesthat may be failure-prone while skipping the others. Thus, allocating resourcesmore efficiently, for example, for testing or code audit.In the first year of the project, we designed and implemented its architec-ture to automatically gather meaningful data to improve model performance.More specifically, we developed a set of tools and framework to (i) automaticallycrawl and mine projects from Github and Gitlab; (ii) extract code and processmetrics from Ansible playbooks and TOSCA blueprints; (iii) support DevOpsThe RADON Framework for Quality Infrastructure Code 5engineers in training defect-prediction models and identifying snapshots of filescontaining defects. The tool exposes RESTful APIs that can be used locally ordeployed online to interact with the defect prediction tool. The APIs and theMongoDB database will be publicly accessible to retrieve the models trainedduring our in-vitro experimentations and those added by the community in thefuture. However, organizations that do not want to expose their data/modelscan deploy the APIs starting from an empty DB on-premises and grant accessto specific users. A command-line client was also developed to use the defectprediction tool in a CI/CD pipeline. It provides functionalities to (i) train amodel from scratch using different configurations in terms of data balancing,normalization, feature selection, and classifiers; (ii) download a model from theonline APIs; (iii) predict unseen instances based on the model trained with (i)or collected with (ii).3.4 Verification ToolThe verification tool enables a user to verify that a RADON model conforms toa set of constraints (e.g., privacy, security, design pattern violations) before de-ployment. While modeling the application via the GMT, the Software Designercan set desired properties and constraints (e.g., security/privacy requirements)using a Constraint Definition Language (CDL). The Software Designer can pro-vide examples of the desired behavior through the RADON IDE or manually.Upon the generated models, the software designer or the DevOps engineer canuse this tool to perform static checking upon their validity. When a violationis encountered (e.g., circular calls, or privacy violations), the engineer can openthe corresponding artifact(s) in the RADON IDE for debugging.During the first year of the project, the CDL and the command-line versionof the verification tool have been developed. It currently supports the verificationand the correction of a RADON model and can detect inconsistencies with theconstraints expressed in the CDL.3.5 Continuous Testing ToolThe testing tool comprises several modules for microservices/FaaS testing anda data pipeline that will support the continuous testing workflow of RADON.It will provide a set of functionalities to support three main usage scenarios: (i)test case definition, (ii) test execution, and (iii) test maintenance. Such scenarioswill help RADON users correctly test their application by creating, executing,inspecting, and removing test cases. In the first year of the project, we specifiedthe requirements of the tool, designed its architecture, envisioned its integrationwith the other tools of the framework, implemented a prototype, and showcaseda proof-of-concept based on two sample applications.6 S. Dalla Palma et al.4 Conclusions and Research RoadmapThe RADON project aims to define a decision-making toolkit to optimize micro-services in terms of size, dependencies, and costs by leveraging a reference setof architectural patterns and service templates [1]. To support this goal, theRADON methodology will integrate a workflow that enables decision makingon architecture optimization through the decomposition tool. To avoid defectiveinfrastructures, it will detect defects through the defect prediction tool. To testthe application, the framework will check the application invariants against thechanges through the testing tool. Furthermore, the methodology will define theoperations and workflows to use the tools and how they should be integratedand should cooperate. Finally, RADON aims at organizing and accelerating themicro-services evolution in a team-based fashion. Security and privacy policieswill be automatically enforced in the run-time environment of the framework toensure protection for sensitive data and services.References1. Guerriero, M., Garriga, M., Tamburri, D.A., Palomba, F.: Adoption, support, andchallenges of infrastructure-as-code: Insights from industry. In: 2019 IEEE Interna-tional Conference on Software Maintenance and Evolution (ICSME). pp. 580–589.IEEE (2019)2. Lenarduzzi, V., Daly, J., Martini, A., Panichella, S., Tamburri, D.: Towards a tech-nical debt conceptualization for serverless computing. IEEE Software (10 2020)3. Matt Rutkowski, Chris Lauwers, C.N., Curescu, C.: Tosca simple pro-file in yaml version 1.3 (2019), http://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.3/TOSCA-Simple-Profile-YAML-v1.3.html

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DevOps and Quality Management in Serverless Computing: The RADON Approach

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