Entries Tagged as 'Architecture '

Evolutionary Improvements of Quality Attributes: Performance in Practice

Agile , Architecture , Architecture Tradeoff Analysis Method (ATAM) , Quality Attribute Workshop No Comments »

By Neil Ernst 
Member of the Technical Staff 
Software Solutions Division

This post is co-authored by Stephany Bellomo

Neil ErnstContinuous delivery practices, popularized in Jez Humble’s 2010 book Continuous Delivery, enable rapid and reliable software system deployment by emphasizing the need for automated testing and building, as well as closer cooperation between developers and delivery teams. As part of the Carnegie Mellon University Software Engineering Institute's (SEI) focus on Agile software development, we have been researching ways to incorporate quality attributes into the short iterations common to Agile development. We know from existing SEI work on Attribute-Driven DesignQuality Attribute Workshops, and the Architecture Tradeoff Analysis Method that a focus on quality attributes prevents costly rework. Such a long-term perspective, however, can be hard to maintain in a high-tempo, Agile delivery model, which is why the SEI continues to recommend an architecture-centric engineering approach, regardless of the software methodology chosen. As part of our work in value-driven incremental delivery, we conducted exploratory interviews with teams in these high-tempo environments to characterize how they managed architectural quality attribute requirements (QARs). These requirements—such as performance, security, and availability—have a profound impact on system architecture and design, yet are often hard to divide, or slice, into the iteration-sized user stories common to iterative and incremental development. This difficulty typically exists because some attributes, such as performance, touch multiple parts of the system. This blog post summarizes the results of our research on slicing (refining) performance in two production software systems. We also examined the ratcheting (periodic increase of a specific response measure) of scenario components to allocate QAR work.

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Principles of Big Data Systems: You Can’t Manage What You Don’t Monitor

Architecture , Big Data No Comments »

By Ian Gorton 
Senior Member of the Technical Staff 
Software Solutions Division

Ian Gorton The term big data is a subject of much hype in both government and business today. Big data is variously the cause of all existing system problems and, simultaneously, the savior that will lead us to the innovative solutions and business insights of tomorrow. All this hype fuels predictions such as the one from IDC that the market for big data will reach $16.1 billion in 2014, growing six times faster than the overall information technology  market, despite the fact that the “benefits of big data are not always clear today,” according to IDC. From a software-engineering perspective, however, the challenges of big data are very clear, since they are driven by ever-increasing system scale and complexity. This blog post, a continuation of my last post on the four principles of building big data systems, describes how we must address one of these challenges, namely, you can’t manage what you don’t monitor. 

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Four Principles of Engineering Scalable, Big Data Software Systems

Architecture , Big Data 2 Comments »

By Ian Gorton
Senior Member of the Technical Staff
Software Solutions Division

Ian Gorton In earlier posts on big data, I have written about how long-held design approaches for software systems simply don’t work as we build larger, scalable big data systems. Examples of design factors that must be addressed for success at scale include the need to handle the ever-present failures that occur at scale, assure the necessary levels of availability and responsiveness, and devise optimizations that drive down costs. Of course, the required application functionality and engineering constraints, such as schedule and budgets, directly impact the manner in which these factors manifest themselves in any specific big data system. In this post, the latest in my ongoing series on big data, I step back from specifics and describe four general principles that hold for any scalable, big data system. These principles can help architects continually validate major design decisions across development iterations, and hence provide a guide through the complex collection of design trade-offs all big data systems require.

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Android, Heartbleed, Testing, and DevOps: An SEI Blog Mid-Year Review

Android , Architecture , Big Data , DevOps , Secure Coding , Testing 1 Comment »

By Douglas C. Schmidt 
Principal Researcher

Douglas C. Schmidt In the first half of this year, the SEI blog has experienced unprecedented growth, with visitors in record numbers learning more about our work in big datasecure coding for Androidmalware analysisHeartbleed, and V Models for Testing. In the first six months of 2014 (through June 20), the SEI blog has logged 60,240 visits, which is nearly comparable with the entire 2013 yearly total of 66,757 visits. As we reach the mid-year point, this blog posting takes a look back at our most popular areas of work (at least according to you, our readers) and highlights our most popular blog posts for the first half of 2014, as well as links to additional related resources that readers might find of interest. 

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Architecture Analysis Using AADL: A Beginner’s Perspective

Architecture , Architecture Analysis & Design Language (AADL) No Comments »

By Julien Delange
Member of the Technical Staff
Software Solutions Division

Julien Delange Introducing new software languages, tools, and methods in industrial and production environments incurs a number of challenges. Among other necessary changes, practices must be updated, and engineers must learn new methods and tools. These updates incur additional costs, so transitioning to a new technology must be carefully evaluated and discussed. Also, the impact and associated costs for introducing a new technology vary significantly by type of project, team size, engineers’ backgrounds, and other factors, so that it is hard to estimate the real acquisition costs. A previous post in our ongoing series on the Architecture Analysis and Design Language (AADL) described the use of AADL in research projects (such as System Architectural Virtual Integration (SAVI)) in which experienced researchers explored the language capabilities to capture and analyze safety-critical systems from different perspectives. These successful projects have demonstrated the accuracy of AADL as a modeling notation. This blog post presents research conducted independently of the SEI that aims to evaluate the safety concerns of several unmanned aerial vehicle (UAV) systems using AADL and the SEI safety analysis tools implemented in OSATE.

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