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

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

A Beginner’s Guide to Types of Testing in Swift