A Beginner’s Guide to Types of Testing in Swift

3 minutes, 33 seconds read

Published on Jul 15, 2020

Updated on Jul 16, 2020

It’s very human to skip tests. But, while developing enterprise apps, testing is something that should never be compromised. If you don’t test, there will be no way to find out the application performance and determine user experiences.

Testing is a must! You might already know that you should write tests for your code and UI, but you might not know — how? I’ll walk you through types of tests that developers usually perform on Swift programming language in order to help you deliver a supreme-quality app to your user.

Whether you’re building a new application or expanding the existing app, you might want to test it on the go. Testing in swift is as simple as building the app itself. (For your information, the Xcode also tests the application). All you need is test cases and an idea about where code usually goes wrong.

But first, it’s necessary to find out what to test.

Developing an App? What to Test?

Start with the basics. You must write mandatory tests if you plan to expand the application.

Tests usually cover the following issues.

Core functionality: Model classes and methods and their interactions with the controller
The most common UI workflows
Boundary conditions
Bug fixes

Let’s take a quick look at the types of testing while developing an app in Swift.

#1 Unit testing using Xcode

It is a process of creating small functionality-based tests for a particular unit of code, which will eventually ensure that all other units will pass the test.

The Test navigator provides the easiest way to work with tests; you’ll use it to create test targets and run tests against your app.

#2 UI Testing

UI testing is useful for testing interactions with the User interface. In UI testing, the developer needs to find the app’s UI objects through queries, synthesizing events. Tester has to then send the events to those objects. The API lets you examine the UI object’s properties and state which you can compare against the expected state.

#3 Performance Testing

A performance test uses a block of code that you want to evaluate. It is then run 10 times to collect the average execution time and the standard deviation for the runs. The average of these individual measurements (of the test run) are compared against the from a benchmark value to evaluate the success/failure of the project.

It’s very simple to write a performance test: You just place the code you want to measure into the closure of the measure().

Bonus – Code Coverage

The code coverage tool tells you about the parts of code that were actually executed during your tests. This way, you’ll know the parts of the app code that aren’t yet tested.

You can enable code coverage by editing the scheme’s Test action. Post this, check the Gather coverage for check box under the Options tab:

Now:

Run all tests (Command-U)
Open the Report navigator (Command-9)
Select Coverage under the top item in that list (image below):

You can see the list of functions and closures in SearchViewController.swift by clicking the disclosure triangle:

Scroll down to updateSearchResults(_:) to see that coverage is 87.9%.

Now:
Click the arrow button for this function to open the source file to the function. As you hover over the coverage annotations in the right sidebar, sections of code highlight green or red:

Code Coverage Annotations - Testing in Swift

The coverage annotations show how many times a test hits each code section. Sections that weren’t called are highlighted in red. This implies — the for-loop ran 3 times, but nothing in the error paths were executed.

You can also increase the coverage of this function by duplicating abbaData.json, then edit it so it causes the different errors. For example, change “results” to “result” for a test that hits print(“Results key not found in dictionary”).

We help enterprises mitigate technical & business risk by securing vulnerable blind spots. Check out our testing services.

For your specific requirements, please feel free to drop us a word at hello@mantralabsglobal.com

About the author:

Anand Nanavaty is a Software Engineer with Mantra Labs. He has been deeply involved in mobile app development for the company’s B2B clients. Apart from coding, testing and experimenting with different application development frameworks, Anand loves travelling, trekking, mountaineering, sports (especially cricket), watching movies and sometimes making short films.

Further reading:

For in-depth understanding of testing in Swift, you can refer to — Writing Test Classes and Methods

Governments worldwide are implementing strict energy and emission policies to drive sustainability and efficiency in industries:

China’s Dual Control Policy (since 2016) enforces strict limits on energy intensity and usage to regulate industrial consumption.
The EU’s Fit for 55 Package mandates industries to adopt circular economy practices and cut emissions by at least 55% by 2030.
Japan’s Green Growth Strategy incentivizes manufacturers to implement energy-efficient technologies through targeted tax benefits.
India’s Perform, Achieve, and Trade (PAT) Scheme encourages energy-intensive industries to improve efficiency, rewarding those who exceed targets with tradable energy-saving certificates.

These policies reflect a global push toward sustainability, urging industries to innovate, reduce carbon footprints, and embrace energy efficiency.

What’s driving the world to impose these mandates in manufacturing?

This is because the manufacturing industry is at a crossroads. With environmental concerns mounting, the sector faces some stark realities. Annually, it generates 9.2 billion tonnes of industrial waste—enough to fill 3.7 million Olympic-sized swimming pools or cover the entire city of Manhattan in a 340-foot layer of waste. Manufacturing also consumes 54% of the world’s energy resources, roughly equal to the total energy usage of India, Japan, and Germany combined. And with the sector contributing around 25% of global greenhouse gas emissions, it outpaces emissions from all passenger vehicles worldwide.

These regulations are ambitious and necessary. But here’s the question: Can industries meet these demands without sacrificing profitability?

Yes, sustainability initiatives are not a recent phenomenon. They have traditionally been driven by the emergence of smart technologies like the Internet of Things (IoT), which laid the groundwork for more efficient and responsible manufacturing practices.

Today, most enterprises are turning to AI in manufacturing to further drive efficiencies, lower costs while staying compliant with regulations. Here’s how AI-driven manufacturing is enhancing energy efficiency, waste reduction, and sustainable supply chain practices across the manufacturing landscape.

How Does AI Help in Building a Sustainable Future for Manufacturing?

1. Energy Efficiency

Energy consumption is a major contributor to manufacturing emissions. AI-powered systems help optimize energy usage by analyzing production data, monitoring equipment performance, and identifying inefficiencies.

Siemens has implemented AI in its manufacturing facilities to optimize energy usage in real-time. By analyzing historical data and predicting energy demand, Siemens reduced energy consumption by 10% across its plants.
In China, manufacturers are leveraging AI-driven energy management platforms to comply with the Dual Control Policy. These systems forecast energy consumption patterns and recommend adjustments to stay within mandated limits.

Impact: AI-driven energy management systems not only reduce costs but also ensure compliance with stringent energy caps, proving that sustainability and profitability can go hand in hand.

2. Waste Reduction

Manufacturing waste is a double-edged sword—it pollutes the environment and represents inefficiencies in production. AI helps manufacturers minimize waste by enhancing production accuracy and enabling circular practices like recycling and reuse.

Procter & Gamble (P&G) uses AI-powered vision systems to detect defects in manufacturing lines, reducing waste caused by faulty products. This not only ensures higher quality but also significantly reduces raw material usage.
The European Union‘s circular economy mandates have inspired manufacturers in the steel and cement industries to adopt AI-driven waste recovery systems. For example, AI algorithms are used to identify recyclable materials from production waste streams, enabling closed-loop systems.

Impact: AI helps companies cut down on waste while complying with mandates like the EU’s Fit for 55 package, making sustainability an operational advantage.

3. Sustainable Supply Chains

Supply chains in manufacturing are vast and complex, often contributing significantly to carbon footprints. AI-powered analytics enable manufacturers to monitor and optimize supply chain operations, from sourcing raw materials to final delivery.

Unilever uses AI to track and reduce the carbon emissions of its suppliers. By analyzing data across the supply chain, the company ensures that partners comply with sustainability standards, reducing overall emissions.
In Japan, automotive manufacturers are leveraging AI for supply chain optimization. AI algorithms optimize delivery routes and load capacities, cutting fuel usage and emissions while benefiting from tax incentives under Japan’s Green Growth Strategy.

Impact: By making supply chains more efficient, AI not only reduces emissions but also builds resilience, helping manufacturers adapt to global disruptions while staying sustainable.

4. Predictive Maintenance

Industrial machinery is a significant source of emissions and waste when it operates inefficiently or breaks down. AI-driven predictive maintenance ensures that equipment is operating at peak performance, reducing energy consumption and downtime.

General Electric (GE) uses AI-powered sensors to monitor the health of manufacturing equipment. These systems predict failures before they happen, allowing timely maintenance and reducing energy waste.
AI-enabled predictive tools are also being adopted under India’s PAT scheme, where energy-intensive industries leverage real-time equipment monitoring to enhance efficiency. (Source)

Impact: Predictive maintenance not only extends the lifespan of machinery but also ensures that energy-intensive equipment operates within sustainable parameters.

The Road Ahead

AI is no longer just a tool—it’s a critical partner in achieving sustainability. By addressing challenges in energy usage, waste management, and supply chain optimization, AI helps manufacturers not just comply with global mandates but thrive in a world increasingly focused on sustainability.

As countries continue to tighten regulations and push for decarbonization, manufacturers that embrace AI stand to gain a competitive edge while contributing to a cleaner, greener future.

Mantra Labs helps manufacturers achieve sustainable outcomes—driving efficiencies across the shop floor to operational excellence, lowering costs, and enabling them to hit ESG targets. By integrating AI-driven solutions, manufacturers can turn sustainability challenges into opportunities for innovation and growth, building a more resilient and responsible industry for the future.

A Beginner’s Guide to Types of Testing in Swift