Software Quality is a key priority in any company involves with software development. Quality which can be describe as a distinguish feature of a software, has a high competitive advantage for most business organisations, especially during this turbulent time with the world battling pandemic. Software has grown and diversifies to ease our day to day life, therefore, the role of quality assurance activities has increase and become extremely important and complex. However, successful software which meets customer’s requirement and expectation depends on the quality of the software. In order to maintain the quality of their applications, several development industries have revised their quality procedure. DevOps and agile development have greatly improved the success rate of software projects with the introduction of test coverage measures. The purpose of this thesis is to implement existing test coverage metrics which are used for improving and measuring the quality of software in order to help reduce excess time consumption, overshooting of budget and maintain scope within requirement. The software quality metrics selected for this study is the ISO 9000 and the core focus area to implement this quality metrics is on unit test, integration test and acceptance test. For this requirement, a design workflow using a flowchart to get a clear description of the work process will be created, configuration of DevOps environment for the pipelines which combines continuous integration and continuous development (CI/CD) to test and build our code constantly and consistently with SonarCloud. Finally, configuration of TestProject for creating automated test script and automated acceptance test execution with automatic generation of test report as well as evaluating the quality of the software product based on the test execution and coverage result. The documentation for this implementation will contain all the steps necessary to configure the test coverage metrics. The metrics will be used to create unit tests, integration tests, and acceptance tests for web applications that run on a variety of browsers and versions, including Chrome Version 103.0.1264.37, Edge Version 103.0.5060.114, and Firefox Version 103.0.

Background : Embedded system consists of both hardware and software. Boththese hardware and software development teams must be worked together to complete the project successfully, which requires a lot of communication among theteams. DevOps is an evolution of agile development that bridges communicationgap between teams. Since Embedded systems are not service-based, it isn’t easy toimplement DevOps. So there is a need to study what challenges are currently facedin implementing embedded application development. Objectives: The focus of this thesis was to gather the challenges and mitigationstrategies to overcome these challenges faced during the implementation of DevOpsin Embedded application development, along with mitigation strategies followed toovercome these challenges. Methods: In this thesis, surveys, and interviews were selected as research methods to identify challenges faced while implementing DevOps in embedded systemapplication development and mitigation strategies, and the survey was considered tocollect demographic questions. Results: A survey was conducted to identify embedded developers with experienceimplementing DevOps for Embedded Application Development. Out of 19 surveyresponses, 12 respondents were further interviewed for detailed information. Datacollected from the interviews are analyzed using thematic analysis and narrativeanalysis. Conclusions: From the analysis, the research concludes that continuous testingis the most challenging phase to implement. Continuous testing and continuous integration are the phases that need to be focused on more for the future implementationof DevOps

Background: Maintaining system reliability and customer satisfaction in a DevOps environment requires effective incident management. In the modern day, due to increasing system complexity, several incidents occur daily. Incident prioritization and resolution are essential to manage these incidents and lessen their impact on business continuity. Prioritization of incidents, estimation of recovery time objective (RTO), and resolution times are traditionally subjective processes that rely more on the DevOps team’s competence. However, as the volume of incidents rises, it becomes increasingly challenging to handle them effectively.  Objectives: This thesis aims to develop an approach that prioritizes incidents and estimates the corresponding resolution times and RTO values leveraging machine learning. The objective is to provide an effective solution to streamline DevOps activities. To verify the performance of our solution, an evaluation is later carried out by the users in a large organization (Ericsson).  Methods: The methodology used for this thesis is design science methodology. It starts with the problem identification phase, where a rapid literature review is done to lay the groundwork for the development of the solution. Cross-Industry Standard Process for Data Mining (CRISP-DM) is carried out later in the development phase. In the evaluation phase, a static validation is carried out in a DevOps environment to collect user feedback on the tool’s usability and feasibility.  Results:  According to the results, the tool helps the DevOps team prioritize incidents and determine the resolution time and RTO. Based on the team’s feedback, 84% of participants agree that the tool is helpful, and 76% agree that the tool is easy to use and understand. The tool’s performance evaluation of the three metrics chosen for estimating the priority was accuracy 93%, Recall 78%, F1 score 87% on average for all four priority levels, and the BERT accuracy for estimating the resolution time range was 88%. Hence, we can expect the tool to help speed up the incident response’s efficiency and decrease the resolution time.  Conclusions: The tool’s validation and implementation indicate that it has the potential to increase the reliability of the system and the effectiveness of incident management in a DevOps setting. Prioritizing incidents and predicting resolution time ranges based on impact and urgency can enable the DevOps team to make well-informed decisions. Some of the future progression for the tool can be to investigate how to integrate it with other third-party DevOps tools and explore areas to provide guidelines to handle sensitive incident data. Another work could be to analyze the tool in a live project and obtain feedback.

Companies follow different approaches in the life cycle of software, but usually, activities are divided into different teams, the most important teams are the development team and the operations team. Often the goals of the operations team and the development team are different, which results in an escalating relationship between the two teams, and a conflict occurs between teams due to the blame of responsibility on each other, which in turn leads to long delivery periods and ineffective delivery methods. To solve these problems, the DevOps approach emerged, which is an acronym for the two words development and operations. This approach aims to align both the development team and the operations team and break the barriers between them by working as one team toward a single goal that shares everyone the responsibility. Thus, the speed of the program delivery process increases and the company provides better services to its customers. Since DevOps is a new approach and many companies tend to adopt this approach, this study aims to investigate the benefits of the DevOps approach and the challenges involved in adopting this approach. This study summarized that the adoption of DevOps approach has an impact on the companies in several aspects. The research presents the most important challenges that the DevOps attempts to overcome, DevOps tools that helps to achieve the value of DevOps, and the benefits of the DevOps approach.

The rapid development, updating, and maintenance of industrial software systems have increased the necessity for software artifact testing. Some medium and large industries are forced to automate the test analysis process due to the proliferation of test data. The examination of test results can be automated by grouping them into subsets comprised of comparable test outcomes and their batch analysis. In this instance, the first step is to identify a precise and reliable categorization mechanism based on structural similarities and error categories. In addition, since errors and the number of subgroups are not specified, a method that does not require prior knowledge of the target subsets should be implemented. Clustering is one of the appropriate methods for separating test results, given this description. This work presents an appropriate approach for grouping test results and accelerating the test analysis process by implementing multiple clustering algorithms (K-means, Agglomerative, DBSCAN, Fuzzy-c-means, and Spectral) on test results from industrial contexts and comparing their time and efficiency in outputs. The lack of organization and textual character of the test findings is one of the primary obstacles in this study, necessitating the implementation of feature selection methods. Consequently, this study employs three distinct approaches to feature selection (TF-IDF, FastText, and Bert). This research was conducted by implementing a series of trials in a controlled and isolated environment, with the assistance of Westermo Technologies AB's test process results, as part of the AIDOaRT Project, in order to establish an acceptable way for clustering industrial test results. The conclusion of this thesis shows that K-means and Agglomerative yield the highest performance and evaluation scores; however, the K-means is superior in terms of execution time and speed. In addition, by organizing a Focus Group meeting to qualitatively examine the results from the perspective of engineers and experts, it can be determined that, from their perspective, clustering results increases the speed of test analysis and decreases the review workload.

The purpose of this research is to investigate the effect of DevOps adoption on organizational agility. The first chapter establishes through literature review and a pilot study, a theoretical definition of DevOps and identifies through survey data, the key DevOps adoption capabilities. The definition and key capabilities identified during the pilot study establishes the foundation through which a subsequent study is performed. The second chapter details an examination of the effects of DevOps adoption capability on organizational agility. Through capability theory, we propose that DevOps adoption capability positively affects organizational agility. We also propose that the capabilities of collaboration/communication, continuous monitoring, measurement and automation positively affect DevOps adoption capability and likewise; the capabilities of responsiveness, competency, flexibility, and quickness affect organizational agility capability. We test our model with survey data collected from 333 respondents and find that our hypotheses are supported and that DevOps adoption capability has a positive effect on organizational agility capability. / Business Administration/Management Information Systems

[ES] DevOps (Development & Operations) es un nuevo movimiento que fomenta la colaboración entre los desarrolladores y el personal de operaciones a través de un conjunto de principios, prácticas y herramientas para optimizar el tiempo de entrega del software. En particular, la práctica del despliegue continuo de software es una gran fuente de problemas y genera mucha atención cuando los artefactos de software se entregan tarde o cuando un defecto crítico llega a producción. Al mismo tiempo, la práctica del despliegue continuo es la frontera entre los desarrolladores y el personal de operaciones en el ciclo de entrega del software. En consecuencia, se recomienda iniciar la implantación de DevOps con la práctica del despliegue de software. Para enfrentar este desafío, los profesionales e investigadores están utilizando la Infraestructura como Código (Infrastructure as Code, IaC), que es un enfoque para la automatización de la infraestructura basado en prácticas de desarrollo de software. El objetivo de IaC es definir en un script todas las instrucciones para crear, actualizar y ejecutar recursos de infraestructura. En este escenario, la automatización del aprovisionamiento de la infraestructura acelera la práctica del despliegue continuo en el ciclo de entrega del software. La computación en la nube se ha convertido en el principal modelo de pago por uso utilizado por profesionales e investigadores para conseguir servicios en la nube en un corto período de tiempo. En este escenario, las compañías están pasando de generar potencia informática in-house hacia la obtención de recursos informáticos provistos en la nube a través de internet como servicios web. Al mismo tiempo, IaC y la computación en la nube están promoviendo algunos cambios en la industria. Por ejemplo, los equipos de operaciones pasan todo su tiempo trabajando en software, en lugar de configurar servidores y conectar cables de red. Existe una variedad de herramientas IaC que utilizan scripts para definir y ejecutar los recursos de infraestructura en diferentes proveedores de servicios IaaS (Infrastructure as a Service). Sin embargo, la diversidad de lenguajes de scripting de las herramientas IaC junto con la heterogeneidad del tipo de infraestructura que ofrece cada proveedor de servicios IaaS, han ocasionado que utilizar scripts IaC para el aprovisionamiento de infraestructura sea una actividad lenta y propensa a errores. El objetivo del proyecto de doctorado es proponer una solución a la diversidad de los lenguajes de scripting ¿de las herramientas IaC¿ y a la heterogeneidad del tipo de infraestructura que ofrece cada proveedor de servicios IaaS respecto al aprovisionamiento de infraestructura en la nube. Para afrontar estos desafíos se propone MoCIP (A Model-Driven Approach to Cloud Infrastructure Provisioning). MoCIP es un enfoque dirigido por modelos para el aprovisionamiento de la infraestructura en la nube que soporta IaC mediante la Ingeniería de Software Dirigida por Modelos (Model-Driven Engineering, MDE). MoCIP utiliza los dos principios fundamentales de MDE: abstracción y automatización. En primer lugar, se desarrolló el lenguaje específico de dominio ArgonML para abstraer las capacidades de la computación en la nube, tales como cómputo, elasticidad, almacenamiento y redes. El lenguaje ArgonML permite modelar los recursos de infraestructura de la nube. En segundo lugar, se desarrolló la herramienta ARGON para automatizar el aprovisionamiento de infraestructura en la nube. ARGON realiza transformaciones de modelo a modelo para generar modelos que representan la infraestructura de diferentes proveedores de servicios IaaS. Además, ARGON realiza transformaciones de modelo a texto para generar scripts IaC con la información subyacente de cada proveedor de servicios IaaS y de cada herramienta de aprovisionamiento de infraestructura. / [CA] DevOps (Development & Operations) és un nou moviment que fomenta la col·laboració entre els desenvolupadors i el personal d'operacions a través d'un conjunt de principis, pràctiques i eines per a millorar el temps de lliurament del programari. En particular, la pràctica del desplegament continu de programari és una gran font de problemes i genera molta atenció quan els artefactes de programari s'entreguen tard o quan un defecte crític arriba a producció. Al mateix temps, la pràctica del desplegament continu és la frontera entre els desenvolupadors i el personal d'operacions en el cicle de lliurament del programari. En conseqüència, es recomana iniciar la implantació de DevOps amb la pràctica del desplegament de programari. Per a enfrontar aquest desafiament, els professionals i investigadors estan utilitzant la Infraestructura com a Codi (Infrastructure as Code, IaC), que és un enfocament per a l'automatització de la infraestructura basat en pràctiques de desenvolupament de programari. L'objectiu de IaC és definir en un script totes les instruccions per a crear, actualitzar i executar recursos d'infraestructura. En aquest escenari, l'automatització de l'aprovisionament de la infraestructura accelera la pràctica del desplegament continu en el cicle de lliurament del programari. La computació en el núvol s'ha convertit en el principal model de pagament per ús utilitzat per professionals i investigadors per a aconseguir serveis en el núvol en un curt període de temps. En aquest escenari, les companyies estan passant de generar potència informàtica in-house cap a l'obtenció de recursos informàtics proveïts en el núvol a través d'internet com a serveis web. Al mateix temps, IaC i la computació en el núvol estan promovent alguns canvis en la indústria, per exemple, els equips d'operacions passen tot el seu temps treballant en programari, en lloc de configurar servidors i connectar cables de xarxa. Existeix una varietat d'eines IaC que utilitzen scripts per a definir i executar els recursos d'infraestructura en diferents proveïdors de serveis IaaS (Infrastructure as a Service). No obstant això, la diversitat de llenguatges de scripting de les eines IaC juntament amb l'heterogeneïtat del tipus d'infraestructura que ofereix cada proveïdor de serveis IaaS, han ocasionat que utilitzar scripts IaC per a l'aprovisionament d'infraestructura siga una activitat lenta i propensa a errors. L'objectiu del projecte de doctorat és proposar una solució a la diversitat dels llenguatges de scripting ¿de les eines IaC¿ i a l'heterogeneïtat del tipus d'infraestructura que ofereix cada proveïdor de serveis IaaS respecte a l'aprovisionament d'infraestructura en el núvol. Per a afrontar aquests desafiaments es proposa MoCIP (A Model-Driven Approach to Cloud Infrastructure Provisioning). MoCIP és un enfocament dirigit per models per a l'aprovisionament de la infraestructura en el núvol que suporta IaC mitjançant l'Enginyeria de Programari Dirigida per Models (Model-Driven Engineering, MDE). MoCIP utilitza els dos principis fonamentals de MDE: abstracció i automatització. En primer lloc, es va desenvolupar el llenguatge específic de domini ArgonML per a abstraure les capacitats de la computació en el núvol tals com còmput, elasticitat, emmagatzematge i xarxes. El llenguatge ArgonML permet modelar els recursos d'infraestructura del núvol. En segon lloc, es va desenvolupar l'eina ARGON per a automatitzar l'aprovisionament d'infraestructura en el núvol. ARGON realitza transformacions de model a model per a generar models que representen la infraestructura de diferents proveïdors de serveis IaaS. A més, ARGON realitza transformacions de model a text per a generar scripts IaC amb la informació subjacent de cada proveïdor de serveis IaaS i de cada eina d'aprovisionament d'infraestructura. / [EN] DevOps (Development & Operations) is a new movement that encourages collaboration between developers and operation staff through a set of principles, practices, and tools in order to optimize the software delivery time. In particular, continuous deployment is a software practice, which is a great source of problems and generates a lot of attention when software artifacts are delivered late, or a critical defect comes to production. At the same time, continuous deployment is the border between developers and operation staff in the software delivery cycle. Consequently, it is recommended to start the DevOps implantation with continuous deployment practice. To address this challenge, practitioners and researchers are using Infrastructure as Code (IaC), which is an approach to infrastructure automation based on software development practices. The aim of IaC is to define in a script all the instructions to create, update and execute infrastructure resources. In this scenario, infrastructure provisioning automation accelerates the continuous deployment in the software delivery cycle. Cloud computing has become the principal pay-as-you-go model used by practitioners and researchers to obtain cloud services in a short period of time. In this scenario, companies are moving from generating in-house computing power into obtaining computing resources provided by cloud computing through the Internet as web services. At the same time, IaC and cloud computing are promoting some changes in the industry. For instance, operation teams spend all their time working on software, instead of racking servers and plugging in network cables. There is a diversity of IaC tools that use scripts to define and execute infrastructure resources in different IaaS (Infrastructure as a Service) service providers. However, the diversity of scripting languages of IaC tools along with the heterogeneity of the type of infrastructure offered by each IaaS service provider have caused that the use of IaC scripts for infrastructure provisioning to be a time-consuming and error-prone activity. The aim of the Ph.D. project is to propose a solution to the diversity of scripting languages of IaC tools along with the heterogeneity of the type of infrastructure offered by each IaaS service provider regarding the cloud infrastructure provisioning. To face these challenges, a Model-Driven Approach to Cloud Infrastructure Provisioning (MoCIP) is proposed. MoCIP is a model-driven approach to cloud infrastructure provisioning that supports IaC through Model-Driven Engineering (MDE). MoCIP uses the fundamental principles of MDE: abstraction and automation. First, the domain-specific language called ArgonML was developed to abstract the capabilities of cloud computing such as computing, elasticity, storage, and networking. The ArgonML language allows modeling the cloud infrastructure. Second, the ARGON tool automates the cloud infrastructure provisioning. ARGON performs model-to-model transformations to generate models that represent the infrastructure of different IaaS service providers. In addition, ARGON performs model-to-text transformations to generate IaC scripts with the underlying information of the IaaS service provider along with the infrastructure provisioning tool. / Sandobalín Guamán, JC. (2020). MoCIP: Un enfoque dirigido por modelos para el aprovisionamiento de infraestructura en la nube [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/139077

This research investigates the integration and effectiveness of two monitoring frameworks, the Four Golden Signals and the RED Method, with Argo Rollouts for automated deployments. The study aims to identify which framework integrates better with Argo Rollouts, compare their effectiveness in automating deployment procedures, and assess the impact of automated deployments on application performance. Experiments involve fault injections, such as HTTP 500 errors and delays, to evaluate the frameworks ability to detect unhealthy deployments and trigger rollbacks. Both frameworks were successfully integrated using Prometheus for metric collection and custom analysis templates for health assessment. The Four Golden Signals provided more comprehensive insights due to its additional metrics (saturation and latency), whereas the RED Method was simpler to configure and interpret. The findings highlight the importance of carefully calibrating metric thresholds to accurately identify unhealthy deployments. Future work suggests exploring Blue-Green deployments, investigating the robustness of systems under security breaches, and assessing cost savings from using Argo Rollouts over time.

Denna studie undersöker hur säkerhetsåtgärder designade för att förhindra Poisoned Pipeline Execution (PPE) attacker påverkar utvecklare som arbetar inom DevOps-team. Studien använder Technology Acceptance Model (TAM) för att analysera hur dessa åtgärder påverkar utvecklares uppfattning om användbarhet, användarvänlighet och deras intentioner att implementera och använda dem. Studiens syfte är att förstå de utmaningar och möjligheter som är förknippade med att integrera säkerhetsåtgärder i DevOps-arbetsflöden, för att säkerställa att de stärker säkerheten utan att hindra produktivitet eller samarbete. Genom en kvalitativ ansats genomfördes semistrukturerade intervjuer med tre erfarna utvecklare från olika organisationer. Tematisk analys visade att bristande medvetenhet om DevOpsSec och dess risker utgör en betydande utmaning. Medan utvecklare erkänner vikten av säkerhet, finns det oro för hur nya åtgärder kan påverka effektivitet och flexibilitet. Resultaten belyser behovet av att organisationer integrerar säkerhet i sin DevOps-kultur, främjar delat ansvar och förståelse. Studien betonar också vikten av att automatisera säkerhetsåtgärder, då detta kan öka deras upplevda användbarhet och användarvänlighet. Genom att analysera utvecklares perspektiv ger studien insikter i komplexiteten av att implementera säkerhetsåtgärder inom DevOps och belyser de nödvändiga stegen för att stärka säkerheten utan att hindra effektiviteten. / This study investigates the impact of security measures designed to prevent Poisoned Pipeline Execution (PPE) attacks on developers working within DevOps teams. It leverages the Technology Acceptance Model (TAM) to analyze how these measures influence developers' perceived usefulness, ease of use, and subsequent behavioral intentions regarding implementation and utilization. The research aims to understand the challenges and opportunities associated with integrating security measures into DevOps workflows, ensuring they enhance security without hindering productivity or collaboration. Employing a qualitative approach, the study conducted semi-structured interviews with three experienced developers from different organizations. Thematic analysis revealed that a lack of awareness about DevOpsSec and its associated risks poses a significant challenge. While developers acknowledge the importance of security, concerns about the impact of new measures on efficiency and flexibility arise. The findings highlight the need for organizations to integrate security into their DevOps culture, fostering a shared responsibility and understanding. Additionally, the study emphasizes the importance of automating security measures, as this can increase their perceived usefulness and ease of use. By analyzing developers' perspectives, the study offers insights into the complexities of implementing security measures within DevOps and sheds light on the necessary steps to enhance security without hindering efficiency.

All developers understand the benefits of testing their code to ensure its functionality. Today’s market is moving further towards design principles where testing is a central or driving force during development. This puts a certain pressure on academia to supply these skills to their students.Recently the course II1302 Projects and project methods at the Royal Institute of Technology in Kista made a concerted effort to introduce the students of the course to these modern concepts. This thesis investigates how areas of testing can effectively be introduced to the students in the course, utilizing a tailored example that takes the area of testing into particular consideration and how to automate it via DevOps-tools provided by a cloud-based service. Further, it also makes an attempt to provide additional material to be used for teaching testing in conjunction with the example provided.The case study covers the development of an example application, meant to mirror a typical student project. It also covers how this was used for teaching the students about the testing areas considered. The covered testing areas include unit testing, integration testing and UI testing. With these given testing areas, the application and an associated learning module was developed for each area in question. Relevant standards, strategies and approaches was also identified for each of these areas.The thesis also presents important properties to take into consideration when developing similar examples in the future, based on the experiences obtained during the study. These include needs such as understandable by inexperienced students, applicability outside the course, adherence to established standards, tools that are simple to use and an architectural structure that allows for testing.Some improvements are also recommended: the students would benefit from learning software testing from an early stage of their studies. The content of the learning modules should also be brought to the students earlier in the course, so it can be applied in their projects at an early stage as well.Further research is also recommended to evaluate the suitability of using other cloud-based environments instead, and to evaluate the applicability of the learning modules for students of varying disabilities. / Alla utvecklare förstår fördelarna med att testa kod för att garantera dess funktionalitet. Dagens industri går i en riktning där testning spelar en central del av design under utveckling av mjukvara. Denna tendens lägger en viss press på högskolan att lära ut dessa erfarenheter till dess studenter.På senare tid har kursen II1302 Projekt och projektmetoder på Kungliga Tekniska Högskolan i Kista tagit en stor ansats för att introducera sina studenter inför dessa moderna koncept. Denna uppsats undersöker hur testningsområdet effektivt kan introduceras till studenterna inom denna kurs, genom att utnyttja ett egengjort exempel som har området i fokus, samt att automatisera detta via DevOps-verktyg tillhandahållna av molnbaserade tjänster. Dessutom görs även en ansats för att tilldela ytterligare material som kan användas för att lära ut testning av mjukvara i samband med det givna exemplet.Fallstudien omfattar utvecklingen av en exempelapplikation, som var avsedd att likna ett typiskt studentprojekt. Den täcker även hur denna användes för att lära ut de betraktade testningsområdena till studenterna. De täckta områdena av testning inkluderar enhetstestning, integrationstestning och testning av användargränssnitt. Med dessa givna testningsområden utvecklades både applikationen och dess associerade lärmoduler för vardera testningområde i fråga. Relevanta standarder, strategier och metoder var också identifierade för vardera av dessa områden.Denna uppsats presenterar även ett antal viktiga egenskaper att hålla i åtanke vid utveckling av liknande exempel i framtiden, baserat på erfarenheterna från studien. Detta inkluderar behov som tillgänglighet för mindre erfarna studenter, applicerbarhet utanför själva kursen, tillämpning av etablerade standarder, utnyttjande av lättanvända verktyg och en arkitektur som tillåter testning.Några förbättringar föreslås även: studenterna skulle gynnas av att lära sig om mjukvarutestning i ett tidigt skede av sina studier. Innehållet i lärmodulerna bör även presenteras för studenterna tidigare i kursen för att kunna appliceras i deras projekt.Ytterligare forskning rekommenderas även för att utvärdera andra lämpliga molnbaserade miljöer, samt för att utvärdera tillämpbarheten av lärmodulerna hos studenter med inlärningssvårigheter.