Articles

Technical Debt Affects Everyone

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If you’ve read our piece about the habits engineers need to beat tech debt, you might recall Conway’s law, which states that organizations which design systems […] are constrained to produce designs that are copies of the communication structures of these organizations.

It’s one of the forces that can push us towards technical bankruptcy because the systems designed by software engineers are constrained by their company’s organizational structure, over which they have little control. The right way to fight these forces is to talk about tech debt across the whole company so that everyone can understand why it’s vital to manage it carefully.

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Java Code Quality Tools Recommended by Developers

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The best way to protect your Java code from avoidable bugs is to use static code analysis tools that can help you find and fix problematic code before it reaches production. Let’s look at some popular static code analysis tools that can be used to test code from a number of different angles.

DeepSource

DeepSource delivers what is probably the best static code analysis you can find for Java. The DeepSource Java analyzer detects 190+ code quality issues, including performance bugs, security risks, bug risks, and anti-patterns. Currently, It supports Gradle Java projects, and in the future, DeepSource will add support for Maven and Android too. DeepSource is also working on bringing Autofix support to the Java analyzer, which will let developers fix issues without writing a single code line.

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Big / Bug Data: Analyzing the Apache Flink Source Code

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Applications used in the field of Big Data process huge amounts of information, and this often happens in real time. Naturally, such applications must be highly reliable so that no error in the code can interfere with data processing. To achieve high reliability, one needs to keep a wary eye on the code quality of projects developed for this area. The PVS-Studio static analyzer is one of the solutions to this problem. Today, the Apache Flink project developed by the Apache Software Foundation, one of the leaders in the Big Data software market, was chosen as a test subject for the analyzer.

So, what is Apache Flink? It is an open-source framework for distributed processing of large amounts of data. It was developed as an alternative to Hadoop MapReduce in 2010 at the Technical University of Berlin. The framework is based on the distributed execution engine for batch and streaming data processing applications. This engine is written in Java and Scala. Today, Apache Flink can be used in projects written using Java, Scala, Python, and even SQL.

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Code Quality: Honing Your Craft

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Code quality may be a moving target, but you can get closer to the target with some help. In this post, we go through the lessons learned by software developers with decades of combined experience. Read on and enhance your code quality!  

Code Quality Lessons and Tutorials

Code Quality
Code Quality Takes Practice. So Get to Practicing!

  1. What Does Code Quality Actually Mean? by RJ Williams and Erik Dietrich. Want to make sure your code is of high-quality? This dev suggests that you should be able to read it as if it were written in prose. Does your code pass the test?

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Broken Windows: How to Create Clean Code

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Introduction

This time it won’t be my favorite rant about certain operating system. Instead, just a few thoughts about the psychology of bad code.

The broken windows theory, an academic theory proposed by James Q. Wilson and George Kelling in 1982, is a metaphor for disorder within neighborhoods. Their theory links disorder and incivility within a community to subsequent occurrences of serious crime. – Encyclopedia Britannica 

The Tale of Shrubbery Code

Once, in the past, me and my team had a discussion about event handlers in front-end code. We looked in bewilderment at some ancient code. Something similar to this Vanilla JS (I’m a big fan of Vanilla JS, to be clear):

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What Errors Lurk in Infer.NET Code?

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Posting of projects sources by Microsoft is a good reason to perform their analysis. This time is no exception, and today, we will look at suspicious places found in Infer.NET code.

Briefly About the Project and the Analyzer

Infer.NET is a Machine Learning system developed by Microsoft specialists. Project source code has become recently available on GitHub, which gave rise to its check. More details about the project can be found here.


Source de l’article sur DZONE (AI)

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