From Code To Unshakable QA: Doing Test Automation Right

When it comes to software testing, it’s clear that we've moved from labor-intensive, manual processes to sophisticated, automated methods. This article goes beyond the basics of test automation. You’ll understand common pitfalls, strategies, the differences between automation tools and frameworks, how to set up test automation from scratch, and the best practices that go with it.

Also Read: STLC: Getting Started with Software Testing

Test automation uses tools and scripts to run set test cases with little human involvement. It helps you handle repetitive or long tests more consistently, thereby, reducing any mistakes that can happen during manual testing. 

While manual testing remains crucial for a majority of software testing tasks, especially for those that rely solely on human expertise, test automation is widely adopted for running a range of tests where consistency, speed, and scalability are critical factors.

Automation testing has gained widespread traction in the software industry since its emergence in the late 1970s with IBM’s Automated Test Engineer System (ATS) being the first to bring test automation to the mainstream. Since then, it has become increasingly popular.

Fast forward to today, ~77% of all global companies use automated testing tools in some form. No wonder the automation testing market reached a staggering worth of around USD 28.1 billion in 2023 and is estimated to grow to a whopping USD 55.2 billion mark by 2028 at a steady CAGR of 14.5% from 2023-2028, as per the report published by MarketsandMarkets.

Key Stages for the History of Test Automation

Let’s take a brief look at the evolution of test automation with the key stages of its growth:

• Record and playback tools — was the stepping stone for test automation between the 1980s and 1990s with capabilities for scripting and replaying interactions enabling testers to automate repetitive tests. Prime examples include the IBM Rational Robot and the Mercury QuickTest.
• Script-based tools — entered the scene in the 1990s and 2000s script-based tools emerged, giving testers more control over automation with tools like Mercury LoadRunner and Mercury QuickTest Professional (QTP) as prominent players till this day.
• Test automation frameworks and tools (2000s - 2020s) — were the next in line with prebuilt methods, libraries, and functions to provide a systematic approach to test automation. Prominent examples include TestNG (Java-based framework released in 2004), Selenium (released in 2004), Robot framework (released in 2008), and Cucumber BDD framework (came out in 2008).
• AI-powered automation tools (2020 - onwards) — have been a hot cake since the early 2020s. With capabilities for AI-driven test case creation and execution, it has also allowed users to analyze historical test data to predict potential failures and optimize test execution. Prime examples include Katalon Studio, LambdaTest, Functionize, and Applitools.

Other Advancements

Among many other advancements in test automation, here are the prominent implementations that have been incredibly useful and widely adopted:

• Continuous testing tools — with CI/CD pipelines allowed teams to detect and address issues early in the process and push updates to the live apps regularly by integrating test automation with DevOps and DevSecOps technologies. Cypress and Playwright are popular examples of it.
• No-code/low-code tools — allowed non-technical users to create and run automated tests without extensive coding knowledge. With features like drag-and-drop functions and intuitive GUIs, users can design and run test cases with prebuilt methods leading to significantly faster testing sessions. Katalon Studio and Tricentis Tosca are prime examples of it.
• Cloud-based tools — are also incredibly popular allowing cloud infrastructure to be used for executing automated tests. These tools offer scalable solutions to run tests on virtual machines on a wide range of browsers, devices, and operating systems at a fraction of the cost. Prominent examples include BrowserStack, BlazeMeter, and Jenkins.

Also Read: Top 10 Automation Testing Tools (2024)

Here are the most common pitfalls to avoid while running automated tests for your system:

Lack of Proper Planning

A poorly planned strategy for test automation can falter automation efforts. Companies sometimes underestimate the time, effort, and resources required to use automation tools strategically, leading to incomplete projects or deprioritization. 

Over-Reliance on End-to-end Testing

While end-to-end testing is a crucial method to be considered for automation, excessive reliance on it can be detrimental, creating a false sense of security that could easily backfire. Often conducted at the later stages of testing, automated end-to-end tests are typically slower, more complex, harder to maintain, and may not cover all possible scenarios or pinpoint specific issues.

Learn More: What is End-to-End Testing? Explained

Poor Tool Selection

Teams often fall into the trap of selecting automation tools based on popularity rather than suitability for their specific needs. Rather than going for the fanciest tool in the market, it’s best to employ tools that suit company use cases, can easily be integrated with the existing software ecosystem, and have lower maintenance costs.

Also Read: Types of Software Testing Tools: What Works Best For You?

Poorly Designed Automation Test Cases

Poorly designed cases can lead to unreliable results and increased maintenance burdens. Common issues include overly complex tests, lack of clarity in test objectives, and insufficient coverage of critical scenarios.

Also Read: How to Write Test Cases? A Stepwise Guide

Scalability Issues

Tests that work well in a small context may struggle to scale with increased testing complexity and test case volume, often leading to longer execution times and difficulties in maintaining test coverage.

The Myth of 100% Test Automation Coverage

While automation testing tools promise high coverage, achieving 100% is unrealistic. It might sound both tempting and desirable to automate everything. Unfortunately, that’s practically impossible. In fact, only ~30-35% of all tests can be automated with end-to-end testing, API testing, and regression testing among the top ones considered for automation. Other methods like exploratory testing and usability testing are best left for human expertise and just cannot be fully automated.

Hidden Costs for Maintenance

Automated tests are not set-and-forget solutions. As the app grows, automated tests must be updated to reflect changes in functionality. Failing to account for the time and resources needed for test maintenance can lead to increased costs and significant overheads.

Also Read: The Hidden Cost of Poor Bug Reporting

Separate Development and Testing Activities

Siloed development and testing activities can lead to communication breakdowns and a lack of collaboration resulting in delays. In such scenarios, QA testers may not have access to the latest code changes or insights from developers. It’s best to take a more integrated approach, where developers and testers work closely together to address such gaps.

Also Read: Professional Courses and Certifications for QA Testers and Developers

Underutilizing DevOps and CI/CD Tools

Missing out or underutilizing DevOps and CI/CD pipelines with automation tools can result in slower release cycles and increased costs of reworks in later stages of development.

Also Read: GitHub vs GitLab: What Works Best For You?

Inaccurate Test Data Strategies 

Inaccurate test data strategies can undermine automated testing resulting in misleading results and gaps in test coverage. Common solutions to address the issue include utilizing suitable data generation tools, regularly refreshing test data, and establishing clear data management policies to match project goals.

Here are some key considerations for creating an effective test automation strategy:

Aligning Test Automation with Project Needs

Before starting on test automation, take time to understand the unique requirements of your project. Consider factors such as the system’s complexity, the team’s skill set, and the project timeline. 

Having Dedicated Oversight Roles

Having a dedicated individual or team to oversee automation strategies is extremely important for maintaining focus and coherence across testing efforts. This typically involves managing the technical aspects of test automation, such as developing and maintaining the automation framework, selecting suitable tools, ensuring test scripts are well-designed and reusable, and monitoring test execution to analyze results. 

Designing a Scalable Test Automation Framework

As a project grows, the automation framework should accommodate new features and changes without requiring a complete overhaul or without significant trouble. It’s best to adopt a modular design using a test automation framework that allows for easy updates and expansions. 

Here are some cutting-edge approaches to consider:

AI for Predicting Flaky Tests

One of the challenges in test automation is dealing with flaky tests — tests that produce inconsistent results even if the inputs are the same. AI-driven tools can analyze historical test data to identify patterns indicating flaky tests.

Generating Test Cases with AI

AI-driven tools can automatically generate test cases based on user behavior and app usage patterns which helps enhance test coverage and reduce the manual effort required to create test cases.

Integrating BDD with Test Automation

Behavior-driven development (BDD) encourages collaboration between technical and non-technical stakeholders. By defining requirements in an English-like Gherkin language that everyone understands, BDD can facilitate clearer communication and more effective test scenarios without the constraints of technical knowledge gaps.

A Mix of Shift-left and Shift-right Testing

Adopting shift-left testing involves integrating testing very early in the development process. It’s a proactive approach that allows teams to identify and address issues before they escalate and find defects as soon as they get introduced to a system. 

Conversely, shift-right testing focuses on testing in production, enabling teams to gather real-world feedback and make necessary adjustments once the development is complete. A mix of both these approaches contributes to a more robust test automation strategy. 

Read More: What is Shift-left Testing? 

Test automation framework and tool indeed sound very similar. Sure, there are more than a few overlaps between them, but they are not the same. 

Here’s a table summarizing “test automation framework vs. test automation tool”:

Here’s a stepwise guide for running automation tests from scratch:

Step 1 - Select Test Automation Approach 

Select an approach that best suits your goals and objectives. You could go with using a:

• Test automation framework
• Test automation tool

The choice between the two can also depend on factors such as the complexity of the app, the team's technical expertise, and the project's timeline. Don’t forget to consider the maintenance and scalability of the chosen approach since some frameworks and tools may be easier to maintain and scale than others.

Step 2 - Perform Exploratory Testing

Once you’re set with an automation approach, perform exploratory testing to check out what to test and why. There’s no fixed rule of thumb for it which allows you to explore the entire app. Based on your knowledge, expertise, and domain, it’s best to target high-risk areas and the critical paths that directly align with business requirements. Other areas include the integration points, code fixes, or recent code additions.

Also Read: What is Integration Testing?

Step 3 - Define the Scope

Determine the specific areas of the system that need to be tested and require prioritization. Consider factors such as functionality, critical paths, risk levels, and business priorities by creating a scope document outlining the boundaries of the testing effort.

Step 4 - Choose A Suitable Tool or Framework

Select the appropriate test automation tool or framework based on factors such as the app’s technology, team expertise, and project specs. 

Step 5 - Prepare and Configure Your Test Environment

Set up the necessary hardware, software, and test data management strategies to create a testing environment that accurately reflects the production environment such as operating systems, browsers, devices, and network configurations.

Step 6 - Design Test Cases and Automation Scripts

Create detailed test cases that outline the steps required to test specific functionalities and write automation scripts using the chosen tool or framework to automate the execution of these test cases. Use standard test design techniques like equivalence partitioning, boundary value analysis, and decision table testing. Utilize an RTM document with regular project reporting activities to keep track of the current progress using project management tools.

Also Read: What is an RTM (Requirements Traceability Matrix)?

Step 7 - Manage Your Test Data

Prepare and organize the test data required for the automation scripts based on data sensitivity, data policies, data volume, and data generation methods.

Step 8 - Execute and Run Tests

Run the automation scripts to execute the test cases and generate test results. Monitor the test execution for any failures or errors and use CI/CD pipelines to automate the process.

Step 9 - Analyze, Report, and Document Test Results

Analyze the test results to identify defects, assess test coverage, and evaluate the quality of the system by generating comprehensive test reports summarizing the test outcomes. Use bug-tracking tools to manage and track identified defects for running bug lifecycles, whenever required. Lastly, document the testing process and results for future works and reference.

Also Read: Top 10 Bug Tracking Tools (2024)

Here are some best practices you can employ while using test automation for your system:

Decide on What Tests to Automate and What Not to

Not all tests are suitable for automation. Exploratory tests, which typically rely on human intuition and expertise are better suited for manual methods. It’s best to automate repetitive, high-volume tests, and long-running tests that require precision and consistency, such as unit tests, API tests, regression tests, and end-to-end tests.

Prioritize Critical Tests First

Ensure that the most critical aspects of your app are validated early and often. Identify and automate tests that cover the most critical functionalities of your app. Moreover, prioritize the tests that directly relate to user experience, security, and performance. 

Use the Right Automation Testing Framework or Tool

Before getting fixated on a tool, consider aspects such as the tech stack of your app, team expertise, and integration capabilities with existing tools that you already have in place.

Create Quality Tests with Good Test Data Quality

Using good quality and real-world data not only improves the reliability of your tests but also helps identify edge cases and potential issues that could arise in the future. Ensure that your test data isn’t skewed and closely matches real-world scenarios.

Create and Execute Tests that Are Resilient to UI Changes

Resilient tests can minimize maintenance efforts and ensure that your automation suite remains effective despite UI updates. Avoid using hard-coded or stringent input values in your tests. Moreover, use object identification strategies to provide more flexibility and ensure that your test suite remains effective despite UI updates.

Ensure Proper Test Environments

Ensure that your test environments mirror production settings as closely as possible to avoid discrepancies. While testing, use the same configurations, databases, and third-party services for the app just like it would in a live environment.

Write Modular and Reusable Tests 

Break down tests into smaller, independent chunks to easily reuse them across different test cases. It’s a huge time saver. Moreover, it also simplifies maintenance, as updates to a single module automatically propagate to all tests that utilize it.

Use CI/CD Pipelines

Using CI/CD pipelines can be extremely crucial for rapid feedback loops, faster release cycles, and early detection of issues. Automated tests can be triggered with every code change, allowing for immediate validation of new features and bug fixes.

Document Your Tests

Documenting test cases, scripts, and the rationale behind test design decisions helps in onboarding new team members and facilitates easier troubleshooting. Moreover, docs also serve as a valuable reference for future works and modifications.

Use Monitoring, Analysis, and Reporting Tools

Monitoring tools provide insights into test execution times, pass/fail rates, and trends over time. Analyzing this data can help identify bottlenecks, improve test strategies, and ensure that your automation efforts align with project goals. Bug reporting and bug tracking tools weigh in equally to track project performance in a streamlined fashion.