Les news de la curation sur des nouveautés et l’humour

We’ve put together this article by talking to the people involved in software modernization projects (in leading positions) here at ObjectStyle. The goal is to help decision-makers choose an optimal software modernization strategy. The post covers some common use cases and approaches to legacy software redesign. It’s also centered primarily around enterprise software development because these are the kind of projects we do most of the time.

What Is Legacy Software

The term "legacy software" is usually applied to a software system that was written decades ago in an outdated programming language, using some no-longer-supported framework(s), according to dated design principles, and/or that is currently running in an increasingly unsupportable environment.

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Our team at DigiMantra Labs consists of many developers, but it has been dominated by frontend developers after the rising popularity of various JavaScript libraries like AngularJS, React.js, and others. We have moved to Microsoft Visual Studio Code and have long forgotten Sublime Text, but it’s a little secret that we still have developers that like "Dreamweaver." Yes, they do exist!

During one of our workshops, “Learn & Rise,” we discusses the various extensions that we use every day to make our lives easier. I collated them all and I’m going to share the top five VSC extensions that our developers use.

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Lizzie is a dynamically compiled scripting language for .NET, allowing you to incorporate dynamically loaded pieces of code into your C# and F# projects. One of its defining traits, is that, first of all, out of the box, it is literally theoretically impossible to execute malicious code, simply since out of the box, it doesn’t contain a single piece of functionality that changes the state of your computer in any way. If you need such functions, which I assume most would, then creating such functions is as easy as marking your method with an attribute, and making sure it has the correct signature. Below is an example.

using System;
using lizzie; class MainClass
{ [Bind(Name = "write")] object Write(Binder<MainClass> binder, Arguments arguments) { Console.WriteLine(arguments.Get(0)); return null; } public static void Main(string[] args) { // Some inline Lizzie code var code = @" var(@foo, function({ +(bar, ""world"")
}, @bar)) write(foo(""Hello "")) "; // Creating a Lizzie lambda object from the above code, and evaluating it var lambda = LambdaCompiler.Compile<MainClass>(new MainClass(), code); lambda(); // Waiting for user input Console.Read(); }
}

Notice how we "bind" a C# method in the above code to our Lizzie lambda object. This makes the C# method available as a "function" internally within our Lizzie code. This trait allows you to easily extend the language, with whatever domain specific extensions you need to solve your particular problem. This makes the language particularly well suited for "Domain Specific Languages."

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Analytics forms a major part of the conceptual design of an app. Data tracking and collection for the purpose of analytics allows us to better update our app for consumer use. Data tracking is akin to the idea of feedback from a user. By collecting the data in a way that makes sense to us, we can add features or upgrade the existing elements of our app to meet the demand of the consumer. While this allows for a certain level of automatic feedback it does not mean that we can outright ignore our app users’ comments either. A firm balance of both is the middle ground that we should be chasing.

Relevant Data Tracking

As a developer, data tracking is simply one more SDK that needs to be built into our existing application framework. We obviously don’t need every bit of information the app can collect – most of this is useless to our determination of whether the app functions as expected or not. We can, however, produce separate use cases to test whether users find a certain button layout more conducive to their app use. We can also collect information such as uninstall/reinstall information, the orientation of the device, loading time of the application and, by extension, its performance on a number of different handsets (very useful in benchmarking the processing friendliness of the application), account information, and, of course, crashes and exception data that can help improve our user experience. David Cearley from Gartner Inc. is noted as saying that every app now needs to be an analytics app and we can only do this through tracking the data relevant to our app.

Source de l’article sur DZONE

After my article, “Role of Project Manager in Data Science”, a couple of program managers suggested me to elaborate the use case on meeting release commitments. We are going to explore simulation, one of the amazing concepts in Artificial Intelligence. Quantitative analytic techniques, such as the Monte Carlo simulation, helps program managers in decision making through probabilistic distributions of potential outcomes.

Monte Carlo relies heavily on the randomness of key variables in solving the problem. Along with key parameters, we also need to understand the relationship between them and sufficient data to analyze further. The five steps listed in “Forecasting the future: Let’s rewind to the basics” are essential to building an accurate model.


Source de l’article sur DZONE (AI)

Factories have witnessed a sea change in the past three decades. The 80s and 90s witnessed Industrial Automation and robots came to the forefront. During the past decade, multiple game-changing technologies are reshaping the factories. Machine Learning, Internet of Things (IoT), Big Data, Virtual Reality (VR), and Artificial Intelligence (AI) are fundamentally alerting the way factories work. Their impact is not limited to manufacturing, they are influencing almost every industry. This article tries to explain Machine Learning and its significance in the world of manufacturing.

First, let’s try and explain Machine Learning. Simply put, it refers to algorithms improving on their own. Normally, when a program is written, it is expected to deliver a prespecified output for a given set of inputs. Over time, they identify patterns and “learn” to generate an output corresponding to a different set of inputs. In cases, algorithms learn to respond to new situations, e.g. algorithms on the trading floor “learn” to respond to different market situations. Machine Learning can be implemented through Decision Tree Learning, Association Rule Learning, Artificial Neural Networks etc.


Source de l’article sur DZONE (AI)

In continuation of my previous articles on design patterns, here I am with another very useful pattern — the bridge design pattern.

Bridge Design Pattern

  • The bridge design pattern is a structural pattern used to decouple an abstraction from its implementation so that the two can vary independently.

    Source de l’article sur DZONE

The deployment of IoT in the oil and gas industry ensures better field communication, real-time monitoring, digital oil field infrastructure, reduced cost of maintenance, mine automation, reduced power consumption, higher productivity, and, thus, enhanced safety and security of assets and workforce.

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One of my early blog posts that I wrote nearly 10 years ago that I called "Sony Baloney" discussed how the electronics giant cultivated some unusual, yet highly successful practices. One of the practices that Sony is known for is taking young and inexperienced engineers and putting them on new product development, while their senior engineers focus on reworking and redesigning their successful products to be more cost-effective. I consider this to be a very wise practice and so do they.

We want innovation in new products but we always want to balance it so that our work is maintainable and cost-effective. The electronics industry pays a lot of attention to this because manufacturing is expensive. Of course, there are no manufacturing costs, per se, in the software industry but there are maintenance and ongoing costs for software and that’s actually where the bulk of our funds are spent. So, paying attention to the maintainability of our code is quite important yet often overlooked.

Source de l’article sur DZONE

Every year, come fall, Americans can enroll in health insurance programs for the upcoming year. As this is no decision to take lightly, people take time in advance to research plans, gather information and study the topic. This is also an opportunity for health insurance companies to draw crowds to their websites, to read about their offerings. Therefore, health insurance companies and government websites should expect traffic spikes way before Nov. 1st, the day open enrollment begins, and up until the sign-up period ends.

To ensure people seeking insurance can access the different plans and services, health insurance companies and agencies should test their websites, apps, and APIs. These tests should check the performance of the systems under different loads, to make sure they don’t crash, there are no memory leaks, response time stays low, etc. Otherwise, customers will choose different plans, and express their anger at the companies and the government across social media (you can learn more and view a demo from our free webinar about preparing your website for peak traffic on Open Enrollment).

Source de l’article sur DZONE