SEI Insights


SEI Architecture Technology User Network (SATURN) News and Updates

Minimum Viable Architecture
In his Introduction to Minimum Viable Architecture, Savita Pahuja at InfoQ recalls an older blog by Kavis Technology that described the role of agile methods as serving a balancing function between the minimum viable product and the minimum viable architecture. Below are several recent opinions on this topic and a project that is putting the theory into practice. Less is More with Minimalist Architecture: Ruth Malan and Dana Bredemeyer wrote in the October 2002 issue of IT Professional that you should "sort out your highest-priority architectural requirements, and then do the least you possibly can to achieve them!" Good Enough Is Good Enough: Minimum Viable Architecture in a Startup: In a presentation given at the San Francisco Startup CTO Summit, Randy Shoup encourages startups to ignore the advice he's been giving for a decade on building large-scale systems.

The Watson Explorer The Watson Developer Cloud brings Watson to developers and the cognitive cloud to Internet applications. Watson offers a variety of services for building cognitive apps, including language identification and translation, interpreting meaning based on context, and communicating with people in their own styles. Here are some reviews and links to APIs and sample code. IBM's Watson Supercomputer Gives Developers Access to Cognitive Cloud: George Lawton at TechTarget provides an early review of the Watson Explorer’s unified view of enterprise information. The cloud allows the technology to be accessible for a greater variety of applications and improves the scale and time to market of those applications. IBM Debuts First Watson Machine-Learning APIs: Serdar Yegulalp at Java World previews the eight services that developers can access for building cognitive apps based on Watson’s machine intelligence service. He focuses on visualization rendering as the service least limited by data training.

Deep Neural Networks  “At some fundamental level, no one understands machine learning.” —Christopher Olah “Neural networks are one of the most beautiful programming paradigms ever invented.” —Michael Nielsen This week, we round up a few examples on deep neural networks (DNNs), a subfield of machine learning that deals with developing training algorithms and uses raw video and speech data as input. Replicating Deep Mind: Kristjan Korjus is working on a project to reproduce the results of Playing Atari with Deep Reinforcement Learning, by Volodymyr Mnih and colleagues of DeepMind Technologies. Mnih et al. presented a deep learning model that used reinforcement learning to learn control policies from sensory input and outperformed human experts on three of seven Atari games. Deep Learning, NLP, and Representations: Christopher Olah at Colah's Blog looks at deep learning from a perspective on natural-language processing and discusses how different DNNs designed for different language-processing tasks have learned the same things.

We at the SEI are excited about the Team Software Process (TSP) Symposium, which we are holding in Pittsburgh, Pa. November 3-6, 2014. The theme of the symposium is "Going Beyond Methodology to Maximize Performance." By this, we mean that the technical program goes beyond the core methodology of TSP to encompass a broader range of complementary practices that contribute to peak performance on system and software projects. As part of our strategy to expand the scope of the symposium and bring in more architectural thinking to those who have adopted TSP and are using it, we've added several architecture-related sessions to the technical program. We at the SEI have seen how successful combining TSP and architecture-centric engineering approaches can be in the project we undertook with Bursatec, the technology subsidiary of the Mexican stock exchange.

Mobile Wallets This blog post began as a mission to compare and contrast mobile wallet systems. Instead, it became a survey of why mobile wallets are not more popular. The reason is not that we’re lacking choice in the mobile wallet economy. We can choose from Amazon Wallet, Apple Pay, Coin, Google Wallet, LoopPay, and Verizon’s Softcard, among others. Why Aren’t Mobile Wallets More Popular in the US? and The Future Of Mobile Digital Wallet Technology in the UK:  Lindsay Konsko at NerdWallet speculates on why the United States lags other countries in adopting virtual wallet technology; then Kristopher Arcand at Forrester explains why the UK lags the US. Why Mobile Wallets Are Failing and Will Keep Failing: Kyle Chayka at Pacific Standard Magazine maintains that mobile wallets won’t be universally accepted by smartphone users until they are universally accepted by merchants.

How has something you learned or saw at SATURN changed how you develop software? Since the first conference in 2004, SATURN has been a place for software developers to share stories about our adventures in building software. Architects, managers, and programmers from across industries and the world came together once a year to share stories about our experiences applying software architecture-centric practices.

Call for Papers, Tutorials and Technical Briefings, and Student Research Competition

MobileSoft 2015 -- 2nd ACM International Conference on Mobile Software Engineering and Systems May 16-17, 2015 Firenze, Italy Co-located with ICSE 2015 May 16-24, 2015 RESEARCH PAPERS AND SHORT PAPERS ================================ Important Dates ===============

Consensus Algorithms and Distributed Systems Consensus algorithms for distributed systems represent a growing field focused on increasing the efficiency of these systems while decreasing their vulnerability to attack and component failure. These recent blog posts offer some theory and practice on consensus algorithms. The Space Between Theory and Practice in Distributed Systems: Marc Brooker at Marc’s Blog discusses the gap between theory and practice in materials on distributed systems, using consensus algorithms as an example. Much material exists on the theory end of the continuum; much exists on the practice end of the continuum. What’s in the middle?