ОБЗОР ПРОБЛЕМ ЭВОЛЮЦИИ СИСТЕМ НАСЫЩЕННЫХ ДАННЫМИ

Сообщества инженерии баз данных и программного обеспечения все еще испытывают большое количество трудностей, которые препятствуют качественному решению проблем бизнес-систем, насыщенных информацией. В данной публикации представлен обзор текущих проблем эволюции систем насыщенных данн

30 Years of Software Refactoring Research: A Systematic Literature Review

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155872/4/30YRefactoring.pd

30 Years of Software Refactoring Research:A Systematic Literature Review

Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools and techniques on software refactoring in the last ten years, defined traditionally as a set of program transformations intended to improve the system design while preserving the behavior. Refactoring studies are expanded beyond code-level restructuring to be applied at different levels (architecture, model, requirements, etc.), adopted in many domains beyond the object-oriented paradigm (cloud computing, mobile, web, etc.), used in industrial settings and considered objectives beyond improving the design to include other non-functional requirements (e.g., improve performance, security, etc.). Thus, challenges to be addressed by refactoring work are, nowadays, beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommendations of specific refactoring activities, detection of refactoring opportunities, and testing the correctness of applied refactorings. Therefore, the refactoring research efforts are fragmented over several research communities, various domains, and objectives. To structure the field and existing research results, this paper provides a systematic literature review and analyzes the results of 3183 research papers on refactoring covering the last three decades to offer the most scalable and comprehensive literature review of existing refactoring research studies. Based on this survey, we created a taxonomy to classify the existing research, identified research trends, and highlighted gaps in the literature and avenues for further research.Comment: 23 page

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Reverse Engineering Heterogeneous Applications

Nowadays a large majority of software systems are built using various technologies that in turn rely on different languages (e.g. Java, XML, SQL etc.). We call such systems heterogeneous applications (HAs). By contrast, we call software systems that are written in one language homogeneous applications. In HAs the information regarding the structure and the behaviour of the system is spread across various components and languages and the interactions between different application elements could be hidden. In this context applying existing reverse engineering and quality assurance techniques developed for homogeneous applications is not enough. These techniques have been created to measure quality or provide information about one aspect of the system and they cannot grasp the complexity of HAs. In this dissertation we present our approach to support the analysis and evolution of HAs based on: (1) a unified first-class description of HAs and, (2) a meta-model that reifies the concept of horizontal and vertical dependencies between application elements at different levels of abstraction. We implemented our approach in two tools, MooseEE and Carrack. The first is an extension of the Moose platform for software and data analysis and contains our unified meta-model for HAs. The latter is an engine to infer derived dependencies that can support the analysis of associations among the heterogeneous elements composing HA. We validate our approach and tools by case studies on industrial and open-source JEAs which demonstrate how we can handle the complexity of such applications and how we can solve problems deriving from their heterogeneous nature