QA trends to lookout for in 2022

In the era of Digital transformation, Software Quality Assurance (QA) plays an important role in identifying flaws in design and ensures the product is functional and secured.

Photo by Uriel Soberanes on Unsplash

As every new year is the beginning of a new chapter, in this blog post I will elaborate a few key Software QA trends to lookout for in 2022.

Localization Testing:

Go global ! Increasing number of apps around the world are planning to enter into new markets and offer customized local features based on the target audience.

In this context, Localization testing ensures products are fully functional in a particular local and verifies if the product is accurate and usable in the locale.

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Key validations:

• Locale UI has same elements as the source
• Text layout and alignment
• Input fields
• Messages
• Special characters and Hyperlinks
• Date/time and phone-number formats

Trending localization testing tools:

• Pseudolocales (Android studio)
• BrowserStack
• Testlio

What is a Pseudolocale?

A pseudolocale is a locale that is designed to simulate characteristics of languages that cause UI, layout, and other translation-related problems when an app is translated.

Pseudolocales are created by instant and automatic translations that are readable in English for all localizable messages. Un-pseudolocalized text points to untranslatable messages in your source code.

Pseudolocales save time and money because you can make adjustments to the UI text and its layout before you commit your messages to the source repository to be sent for translation later.

The (Android) pseudolocale names follow standard locale naming conventions and their locale IDs can be parsed by any BCP 47 compliant programming language. In this sense, pseudolocales are just like any other locales such as French, Italian, Chinese etc.

The Android platform provides the following two pseudolocales to represent Left-to-right (LTR) and right-to-left (RTL) languages:

English (XA): Adds Latin accents to the base English UI text, expands the original text by adding non-accented text, and brackets each message unit to expose potential issues from expanded text. Potential issues can be layout breakage and badly formed message syntax, as shown by one sentence split into multiple parts displaying as multiple bracketed messages.

AR (XB): Sets the text direction of the original left-to-right messages to the right-to-left direction, which reverses the order of the characters in the original message.

Left Image: English (XA) pseudolocale (Source: https://developer.android.com/) | Right Image: AR (XB) pseudolocale (Source: https://developer.android.com/)

Enabling Pseudolocales:

Pseudolocales are usually added to developer-oriented builds. In Android Studio, enable pseudolocales for a specific app by adding the following configuration to your build.gradle file.

//Groovy
android {
   ...
   buildTypes {
       debug {
           pseudoLocalesEnabled true
       }
   }
}
//Kotlin
android {
   ...
   buildTypes.getByName("debug") {
       isPseudoLocalesEnabled = true
   }
}

How to spot localization issues?

Pseudolocales provide a time-saving and effective way to spot potential localization issues in the UI by helping you to identify problems in the following areas:

• Hard-coded strings, which cannot be sent to translation, display as unaccented text in the pseudolocale to make them easy to notice.
• UI layout issues caused by text expansion showing where the UI can break due to text length.
• String concatenation, which displays as one message split across 2 or more brackets. This can make correct translation difficult because translators have to translate each part independently without knowing that the parts are related.
• Bi-directional (BIDI) text problems, such as when content in one text direction includes an inline phrase in the opposite text direction, making the string difficult to read.
• Right-to-left (RTL) problems such as elements not being mirrored.

Visual Testing:

Visual testing evaluates the visual output of an application and compares it with expected UX design. It catches visual bugs with web elements and colour coding.

Visual testing addresses a user experience layer of testing and scales the validations and look and feel of a UI (user interface) across digital platforms, as opposed to differential testing. Automated visual testing tools address the pain of constant changes made to the UI layer together with an ever-growing number of platforms, screen sizes, and configurations.

Key validations:

• Height & Width
• Visibility (true/false)
• Background colour
• Coordinates

Automated visual testing is possible using Snapshot testing concept, where a bitmap of a screen is captured at various points of a test run and its pixels are compared to a baseline bitmap.

Snapshot testing illustration (Source: applitools.com)

Trending automated visual testing tools:

• Applitools
• Kobiton
• LambdaTest
• Percy

Game Testing:

Game Testing evaluates quality of video games. The main goal of game testing is to identify and discover defects and bugs in a video game and improve the stability and performance.

Photo by Florian Olivo on Unsplash

Key validations:

• Game flow logic
  - Rules for attaining next levels
• Visual attributes
  - Player actions and music controls
• Camera
  - Zoom in and out, Cinematic view and Replay
• Gamepad and button functions
• Privacy & Legal texts,
• Movie/Teaser clips
• Player movement and positioning
• Player statistics & Scores
• Special effects (shock/vibration effects), title screens, multi-button actions

Performance tuning is very important to optimize the game speed. Below are the key performance parameters to verify in Game testing,

• Battery consumption: Battery Consumption must be optimum over long hours. Game response time should not be impacted under varying heavy loads across different devices.
• Processor & Memory limits
• Response time on client & server

AI & ML Testing:

Artificial Intelligence (AI) and Machine Learning (ML) technologies are well trained to process data/patterns and act as drivers of the digital-era.

As every organization is moving towards adapting AI-based applications/products, there is a huge growth potential for these technologies in the near future.

Photo by Daniel Frank on Unsplash

Test cases for AI/ML based applications are expected to meet acceptable accuracy as fixed set of data inputs are no longer valid with AI systems analyzing millions of data patterns.

Below are the key elements for planning test strategy for an AI/ML based application/product,

• Algorithms
  - NLP (Natural Language Processing)
  - Deep Learning
  - Image Processing
• Data
  - Creation & Labelling
  - Identifying test data sets
  - Clustering
• Non Functional Testing (NFR)
• Decision analysis
• Model back testing
  - Model back testing is useful in evaluating assumptions based on testing the model/design based on historical data
  - Model back testing is very effective in determining a model’s accuracy

To conclude, in this blog post we glanced over the QA trends to lookout for in 2022 with elaborated steps on validations and tools. I hope you enjoyed reading this article, and you know the drill — clap, comment and share.

I wish 2022 to be an incredible part of your story.

Cheers, Karthik