Verifying big data topologies by-design: a semi-automated approach

Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Apache Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors. Such complex architectures make possible refactoring of the applications a difficult task for software architects, as applications might be very different with respect to the initial designs. As an aid to designers and developers, we developed OSTIA (Ordinary Static Topology Inference Analysis) that allows detecting the occurrence of common anti-patterns across big data architectures and exploiting software verification techniques on the elicited architectural models. This paper illustrates OSTIA and evaluates its uses and benefits on three industrial-scale case-studies

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

DevOps and information technology service management: A problem management case study

The use of DevOps is a predominant attribute of businesses engaged in the development and maintenance of Information Technology systems. Although literature exploring DevOps practices has expanded, there is still much unexplored territory on its operational ramifications. This is particularly observed when considering their potential impact on ITSM frameworks such as ITIL, which governs Operations. This research aims to establish how DevOps principles and practices can be applied to Problem Management, a core Service Management process. Specifically, it explores which DevOps practices may be used throughout the Problem lifecycle, as well as benefits which may result from them. An exploratory case study was carried out with the participation of Problem Managers operating in a DevOps environment. Three data collection methods were applied: Semi structured interviews, in which participants described their experience and insight in relation to DevOps and Problem Management; documental analysis and observation, where processes and workflows were examined; and a focus group exercise in which study outcomes were discussed and systematized. This research indicates that DevOps practices have varying degrees of significance for a Problem Management process. Practices associated with continuous planning and collaboration are prone to having greater significance in a Problem lifecycle, with the potential of enabling benefits such as quicker Problem identification, higher quality Root Cause Analysis, and improved resolution times. The novelty of insight gathered in this study benefits both academics, through its contribution to an expanding body of knowledge, and professionals, considering the practical and applicable nature of findings. Future work is also presented.A utilização de metodologias DevOps é hoje uma característica predominante de organizações envolvidas no desenvolvimento e manutenção de sistemas de Tecnologia e Informação. Apesar da crescente produção de literatura a examinar práticas DevOps, existe muito território por explorar referente às suas ramificações a nível operacional. Isto é particularmente notável quando se consideram potenciais interações com frameworks de ITSM como o ITIL, que governam Operações. Esta pesquisa tem como objetivo estabelecer quais princípios e práticas DevOps podem ser aplicadas na Gestão de Problemas, um processo central para a Gestão de Serviços. Especificamente, exploramos quais práticas DevOps podem ser utilizadas ao longo do ciclo de vida de um Problema, tal como que benefícios poderão resultar da sua aplicação. Um caso de estudo exploratório foi realizado com a participação de Gestores de Problemas a operar num ambiente DevOps. Três métodos de recolha de dados foram aplicados: Entrevistas semiestruturadas, onde participantes descreveram a sua experiência e conhecimento em relação a DevOps e Gestão de Problemas; análise documental e observação, onde processos operacionais foram examinados; e uma discussão em grupo onde resultados do estudo foram discutidos e sistematizados. Esta investigação indica que práticas DevOps tem variados níveis de significância para um processo de Gestão de Problemas. Práticas associadas ao planeamento contínuo e colaboração tendem a ter maior significância no ciclo de vida de um Problema, com potencial para gerar benefícios como a mais rápida identificação de Problemas, maior qualidade na análise de causa, e melhorias nos tempos de resolução. As conclusões apresentadas neste estudo trazem benefícios tanto para académicos, expandindo o corpo de conhecimento disponível sobre o tema, como para profissionais, considerando a sua natureza prática e aplicável. Direções para trabalho futuro são também apresentadas

Detectando y evitando defectos de diseño de software : un catálogo de antipatterns y un análisis de los code smells en los que incurren estudiantes de grado

El diseño de software es un proceso creativo y fundamental en la construcción de software de calidad. La creación de un diseño simple y eficiente puede ser una tarea muy compleja y su construcción requiere de ciertas habilidades que al parecer no están desarrolladas en los estudiantes de pregrado. Las malas practicas de diseño de software originan defectos de diseño, que no necesariamente producen errores de compilación o de ejecución pero que afectan negativamente a los factores de calidad del software. Los defectos pueden surgir en diferentes niveles de granularidad, como son los antipatterns y code smells. Desafortunadamente, identificar y corregir estos defectos de diseño puede ser una tarea muy compleja. Este trabajo pretende contribuir al conocimiento existente sobre técnicas y herramientas de detección de antipatterns y code smells, a partir de una revisión secundaria de la literatura. Además, busca conocer los defectos de diseño en los que incurren los estudiantes de pregrado de nuestra Facultad e investigar, mediante estudios experimentales, si estos pueden ser evitados utilizando un conjunto de plantillas de diseño. El resultado de la revisión secundaria es un catalogo de defectos de diseño, donde para cada uno se incluyen técnicas y herramientas utilizadas para detectarlo. Este catalogo puede ser utilizado en otras investigaciones o por profesionales de la industria como gua en el diseño de software. Del análisis de defectos de diseño, se observa que los estudiantes de pregrado incurren en una gran variedad de estos defectos y no mejoran la calidad interna del software cuando utilizan plantillas para representar el diseño. El uso de estas plantillas no evitan ni disminuyen la aparición de ciertos defectos de diseño. Comprender los tipos de defectos de diseño que están presentes en proyectos que desarrollan los estudiantes, sirve como puntapié inicial para generar nuevas hipótesis y diseñar nuevos estudios experimentales. Además, estos resultados pueden servir como insumo en la investigación sobre las practicas de diseño y cómo se enseña a diseñar software

Continuous architecting of stream-based systems

Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors making it a difficult task for software architects to refactor the initial designs. As an aid to designers and developers, we developed OSTIA (On-the-fly Static Topology Inference Analysis) that allows: (a) visualising big data architectures for the purpose of design-time refactoring while maintaining constraints that would only be evaluated at later stages such as deployment and run-time, (b) detecting the occurrence of common anti-patterns across big data architectures, (c) exploiting software verification techniques on the elicited architectural models. This paper illustrates OSTIA and evaluates its uses and benefits on three industrial-scale case studies