Autumn  Blick

Autumn Blick

1613615119

Testing Asynchronous Code - RxJS Live London

Jay Phelps, former member of the RxJS core team, recently explained how to test code that leverages code using RxJS, the reactive programming library used by the Angular front-end framework for asynchronous programming. RxJS provides a testing API with a DSL to express timed sequences and lifecycle events.

RxJS provides an Observable type that encloses a mechanism to produce a potentially unbounded sequence of values. That sequence of values can originate from a source that is external to the program (e.g., button clicks), or result from transformations applied to existing sequences. RxJS provides additional APIs, types, and methods that let developers transform sequences (e.g., operators); and express the relationship between sources and sequences (e.g., Subject, schedulers).

Asynchronous code that leverages RxJS thus typically expresses a sequence of events as an Observable and applies transformations to it to express some business logic or processing. The resulting observable can then be further transformed. It can alternatively be subscribed to, with the program performing effects based on the values enclosed in the observable (e.g., API calls). In that context, testing asynchronous code is often testing that a transformation function takes an input observable into an output observable that contains an expected sequence of values (e.g., a sequence of purchases is taken into a sequence of updated basket content).

#rxjs #javascript #testing #web-development

What is GEEK

Buddha Community

Testing Asynchronous Code - RxJS Live London

Top Security Penetration Testing Companies

Cybercrime is one of the world’s fastest-growing threats, with malicious actors constantly elaborating their methods of undetectable intrusion. According to Verizon’s Business 2020 Data Breach Investigations report, there has been a 100% increase in web app breaches, and stolen credentials were used in more than 80% of these cases. These statistics are worrying for many businesses that actively move their processes to the cloud and deal heavily with customers’ personal data.

Under these circumstances, companies need to run regular automated and manual tests to determine weak spots in their infrastructure, software, network and physical perimeter security. One of the most efficient testing methods is security penetration testing, or pentesting.

Pentesting is a benign hacking attempt, manual or automated, to break into the system and uncover its vulnerabilities before actual cyber criminals do it. This method is directed at testing the system security controls for their real-world effectiveness. It involves such stages as data collection, threat modeling, vulnerability scans, penetration tests, and so on.

To get proactive with their cyber security protection, many businesses cooperate with professional security testing companies that are able to comprehensively check the system, identify risks, fix vulnerabilities, and stay one step ahead of potential hackers.

The ranking criteria for security testing companies

When asking a professional software testing company to check your system’s security, in most cases you need to grant them access to sensitive information. For this reason, it’s important to choose a reliable company with an exceptional reputation, which will become your trusted partner.

Unsurprisingly, the market of security penetration testing companies is overwhelmingly crowded. To narrow down your search, we have analyzed hundreds of testing companies and compiled the list of top testing professionals. We have applied the following criteria:

  • Pentesting expertise
  • Portfolio
  • Software QA experience
  • Market penetration
  • Online reviews

As a result, we’ve picked 30 skilled security testing companies and rated them accordingly.

1. a1qa

a1qa is a software testing company from Lakewood, CO, that has delivered over 1,500 successful projects and established 10 Centers of Excellence during their 17 years of operation. It has partnered with more than 500 companies, from smaller businesses to Fortune 500 giants. The company’s prominent customers include adidas, Kaspersky Lab, SAP, Yandex, Forex Club, and more.

a1qa specializes in delivering full-cycle QA and testing services, including comprehensive security penetration testing. Its expertise covers testing of web apps such as portals, ecommerce, media and e-learning platforms, games and online casinos, and line-of-business testing, such as CRM, collaboration, document management, and financial systems. The company also runs a specialized security testing lab.

2. QA Mentor

Founded in New York in 2010, QA Mentor has managed to establish a strong global presence with 12 testing centers around the world. Its team consists of 300 certified QA professionals that have successfully completed over 870 projects, including the ones for Amazon, eBay, Bosch, HTC, and more. The company offers more than 30 testing services, with cyber security penetration testing among them.

QA Mentor is recognized as a top software testing company by Clutch, GoodFirms, and Gartner.

3. UnderDefense

UnderDefense is a certified computer and network security company that was established in New York in 2016. It provides a wide range of testing services, with a special focus on security penetration testing. The company’s certified security testing team has performed hundreds of penetration tests, including compliance-specific tests, app and wireless network penetration testing, and social engineering security testing. UnderDefense has been repeatedly awarded by Clutch.

#testing #software-testing #security-testing #penetration-testing #top-software-testing-companies #software-testing-companies #good-company #code-quality

Live Webinar, Testing Superpowers: Using CLion to Add Tests Easily

CLion is great for refactoring C++ code to make it more maintainable.
But as someone asked in Arne Mertz’s “Refactoring C++ Code” webinar, “What can we do if we don’t have tests on the project and can’t easily check the changes introduced by refactorings?
In this webinar you will learn how to:

  • Add tests for untested code, quickly and safely.
  • Use CLion’s code coverage tools to guide your testing.
  • Use Approval Tests to get good coverage really quickly, and explore the behavior of the code.

#testing #clion #tests #live webinar #testing superpowers #add tests

Autumn  Blick

Autumn Blick

1613615119

Testing Asynchronous Code - RxJS Live London

Jay Phelps, former member of the RxJS core team, recently explained how to test code that leverages code using RxJS, the reactive programming library used by the Angular front-end framework for asynchronous programming. RxJS provides a testing API with a DSL to express timed sequences and lifecycle events.

RxJS provides an Observable type that encloses a mechanism to produce a potentially unbounded sequence of values. That sequence of values can originate from a source that is external to the program (e.g., button clicks), or result from transformations applied to existing sequences. RxJS provides additional APIs, types, and methods that let developers transform sequences (e.g., operators); and express the relationship between sources and sequences (e.g., Subject, schedulers).

Asynchronous code that leverages RxJS thus typically expresses a sequence of events as an Observable and applies transformations to it to express some business logic or processing. The resulting observable can then be further transformed. It can alternatively be subscribed to, with the program performing effects based on the values enclosed in the observable (e.g., API calls). In that context, testing asynchronous code is often testing that a transformation function takes an input observable into an output observable that contains an expected sequence of values (e.g., a sequence of purchases is taken into a sequence of updated basket content).

#rxjs #javascript #testing #web-development

Tamia  Walter

Tamia Walter

1596754901

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

  • Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
  • Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
  • While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

Ways to Test Microservices

It is easy to fall into the trap of making testing microservices complicated, but there are ways to avoid this problem. Testing microservices doesn’t have to be complicated at all when you have the right strategy in place.

There are several ways to test microservices too, including:

  • Unit testing: Which allows developers to test microservices in a granular way. It doesn’t limit testing to individual microservices, but rather allows developers to take a more granular approach such as testing individual features or runtimes.
  • Integration testing: Which handles the testing of microservices in an interactive way. Microservices still need to work with each other when they are deployed, and integration testing is a key process in making sure that they do.
  • End-to-end testing: Which⁠—as the name suggests⁠—tests microservices as a complete app. This type of testing enables the testing of features, UI, communications, and other components that construct the app.

What’s important to note is the fact that these testing approaches allow for asynchronous testing. After all, asynchronous development is what makes developing microservices very appealing in the first place. By allowing for asynchronous testing, you can also make sure that components or microservices can be updated independently to one another.

#blog #microservices #testing #caylent #contract testing #end-to-end testing #hoverfly #integration testing #microservices #microservices architecture #pact #testing #unit testing #vagrant #vcr

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer