InfoQ Homepage Development Content on InfoQ
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Turbocharge React with GraphQL
GraphQL and React are two Facebook technologies that have grown up together. In this article, Shane Stillwell shows how GraphQL, a strongly-typed JavaScript-based language, helps developers build relationships with their data and improves marshaling across service boundaries. GraphQL is extensible, works alongside REST, and can be implemented in any back-end software solution.
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Building a Blockchain PoC in Ten Minutes Using Hyperledger Composer
This article examines what businesses look for when considering blockchain’s role in their organization and how the Linux Foundation's Hyperledger Composer can help application developers easily create compelling blockchain solutions for the enterprise.
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GitLab's CEO Sid Sijbrandij on Current Development Practices
In this all-round interview, GitLab CEO Sid Sijbrandij speaks about how GitLab was born, what differentiates it from its competitors, the importance of being an "open" company, how GitLab engineers use continuous integration, what being a remote-only company means, and much more.
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Robotic Testing of Mobile Apps for Truly Black-Box Automation
Axiz is a robotic-test generator for mobile apps. Here, we compare our approach with simulation-based test automation, describe scenarios in which robotic testing is beneficial (or even essential), and tell how we applied Axiz to the popular Google Calculator app.
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InfoQ Virtual Panel: A Practical Approach to Serverless Computing
Add serverless computing to the growing list of options developers have when building software. Serverless products—more accurately referred to as Functions-as-a-Service—offer incredible simplicity, but at a cost. To learn more about this exciting space and the practical implications, InfoQ reached out to three experienced technologists.
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Introducing Reladomo - Enterprise Open Source Java ORM, Batteries Included! (Part 2)
Goldman Sachs is widely known as a leader in investment banking, but they are very much a leading technology firm as well. Continuing our exploration of Reladomo, the primary Java ORM used at GS and now open source, GS Technology Fellow, Mohammad Rezaei looks at advanced features, such as sharding, caching, bitemporal access, performance, and testing.
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Security Implications of Permission Models in Smart-Home Application Frameworks
This article presents an analysis of a popular smart-home programming framework, SmartThings, which reveals that many smart-home apps are automatically overprivileged, leaving users at risk for remote attacks that can cause physical, financial, and psychological harm.
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Key Abstractions for IoT-Oriented Software Engineering
This article provides an overview of the key general characteristics of complex IoT systems and applications. Based on them, the author identifies the software abstractions that could provide the basis for IoT-oriented software engineering, including stakeholders and users, requirements, avatars, and coalitions.
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Benchmarks Don't Have to Die
Are tracing and profiling the future of performance engineering outside of the fast-moving JavaScript community? Do all benchmarks have a shelf-life? In this article, Matt Fleming talks about benchmarks and what keeps the good ones alive and why others die. By adapting benchmarks, they can live forever.
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A Roadmap to the Programmable World
The emergence of millions of remotely programmable devices in our surroundings will pose significant challenges for software developers. This article proposes a roadmap from today’s cloud-centric, data-centric Internet of Things systems to the Programmable World highlights those challenges that haven’t received enough attention yet.
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Are Unit Tests Part of Your Team’s Performance Reviews?
No matter how often you conduct performance reviews, there is no doubt unit testing should be one of the metrics measured. Eli Lopian explains what makes a good unit test and how to measure them to ensure your development team is truly agile.
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Machine Learning Techniques for Predictive Maintenance
In this article, the authors explore how we can build a machine learning model to do predictive maintenance of systems. They discuss a sample application using NASA engine failure dataset to predict the Remaining Useful Time (RUL) with regression models.