The software development field is in a consistent state of innovation and change. AI in Software Testing in 2024 these Modern highlights, complex functionalities, and ever-evolving user requests require a vigorous testing procedure to guarantee quality and unwavering quality. Conventional testing strategies frequently struggle to keep pace and require a lot of maintenance. However, nowadays, AI-powered test automation and AI in software testing is shaping up as a game-changer that’s transforming the way we test software in 2024.
This progressive approach gives a path and the control of artificial intelligence to automate repeatable and time-consuming tasks, produce intelligent test cases, and analyze endless sums of information. The result? Unparalleled test scope progressed productivity and a noteworthy boost in software quality using AI in software testing in 2024.
In this blog post, we’ll dig into the energizing world of AI-powered test automation and how to use AI in Software Testing. We’ll investigate how AI is reshaping the testing process, the key points of interest it offers, and a glimpse into the future of this transformative innovation. So, buckle up and get prepared to find out how AI is revolutionizing software testing in 2024!
The Evolution of Test Automation (AI in Software Testing)
Back in the day, testing utilized to be a manual affair. Think spreadsheets, sticky notes, and parcels of coffee-fueled late nights. Then came the era of scripts and automation tools. In the early days (think the 1970s!), automation was pretty fundamental. We’re talking basic scripts that mirror user activities. Kind of like a clickbot on autopilot.
Then came the 2000s, with geniuses like Selenium rising. These frameworks permitted more complex testing, letting us automate web applications over distinctive browsers. All of a sudden, repetitive tests seem to be run at the tap of a button. It was a game-changer!
Fast forward to nowadays, and we’re in the age of AI and machine learning. Test automation has become more intelligent, more prescient, and fantastically productive. AI-powered testing tools can analyze tremendous sums of information, recognize patterns, and pinpoint potential issues sometime recently they have gotten to be full-blown bugs With help of AI-powered Test Automation and using AI in Software Testing in 2024.
But it doesn’t stop there. The future of test automation looks indeed more energizing. We’re talking approximately autonomous testing, where AI does not only identify issues but also fixes them independently. Envision a world where your testing suite is like a self-driving car, cruising through test scenarios with accuracy and agility.
So, whether you’re a seasoned QA professional or fair plunging your toes into the testing waters, one thing is clear: the journey of test automation is an exciting ride, and we’re as it is beginning!
Understanding AI in Software Testing and Test Automation in 2024
First off, what’s the buzz about AI? Simply put, AI brings a touch of intelligence to automation. It’s like having a smart assistant that learns and adapts to improve tasks over time. In the realm of software testing, AI is a game-changer.
Imagine this: you have a mountain of test cases to run. It’s tedious, time-consuming, and prone to human error. Enter AI-powered test automation! AI algorithms can analyze massive data sets, identify patterns, and make predictions, streamlining your testing process.
One of the coolest AI features is predictive analytics. It can foresee potential issues based on past data, helping you catch bugs before they cause chaos in production. Talk about being proactive!
Natural language processing (NLP) is another star player as it allows testers to interact with systems using human language, making test creation and execution more intuitive. Gone are the days of cryptic commands or complex scripts!
Let’s not forget about Machine Learning (ML). ML algorithms can autonomously improve test coverage by learning from test results and refining test cases. It’s like having a self-improving testing system on autopilot.
But wait, there’s more! Additionally, AI can optimize test execution by prioritizing critical tests, reducing redundant ones, and dynamically adjusting test suites based on code changes. Indeed, it’s like having a super-smart QA team working tirelessly in the background using AI in software testing in 2024.
Benefits of AI-Powered Test Automation
Integrating AI into test automation offers various points of interest to software improvement teams:
Improved Test Scope and Precision: AI algorithms pinpoint critical test scenarios and make test cases covering a wide extent of functionalities, guaranteeing comprehensive scope and exact results.
Faster Test Execution: AI-assisted testing speeds up test execution by automating repetitive tasks, liberating groups to focus on impactful testing and accelerating time-to-market.
Cost Savings and Asset Optimization: Automation diminishes manual effort, leading to noteworthy cost savings and way better asset allocation.
Enhanced Scalability and Adaptability: AI-powered automation scales with project needs, handles complex scenarios and adjusts to application changes consistently.
Challenges and Considerations of AI in Software Testing
Despite the compelling benefits, organizations must explore a few challenges into AI in software testing:
Initial Learning Curve: Implementing AI tools in test automation requires learning and setup, which can be a hurdle for some teams.
Data Quality: AI’s effectiveness pivots on clean, significant training data, emphasizing the significance of data quality.
Maintenance Overhead: Regular upgrades and maintenance of AI models are fundamental to align with advancing software and commerce needs.
Ethical Considerations: AI automation raises moral questions around data security, inclination, and straightforwardness, requiring proactive addressing of these concerns.
Best Practices for Implementing AI in Software Testing and Test Automation
To harness the full potential of AI-powered test automation, organizations should follow best practices such as:
Define Clear Objectives: Before diving into AI-powered test automation, outline your goals. What do you want to achieve? Improved test coverage, faster time to market, or better defect detection rates? Clear objectives will guide your AI implementation strategy.
Select the Right Tools: Choose AI tools that align with your testing needs. Look for features like intelligent test case generation, self-healing capabilities, and predictive analytics. Tools like Testim, Applitools, or Eggplant AI offer robust AI-assisted testing solutions.
Start Small, Scale Gradually: Begin with a pilot project or a small set of test cases to evaluate the effectiveness of AI in your automation framework. Once you gain confidence and see tangible benefits, gradually scale up your AI initiatives.
Data Quality Matters: AI thrives on data, so ensure you have high-quality, diverse datasets for training and testing AI models. Clean, relevant data will enhance the accuracy and reliability of your AI-assisted test automation.
Collaborate Across Teams: Foster collaboration between QA, development, and data science teams. Work together to define testing scenarios, validate AI models, and integrate AI-powered testing seamlessly into your CI/CD pipelines.
Continuous Learning and Optimization: AI evolves, so prioritize continuous learning and optimization. Monitor test results, gather feedback, and refine your AI models to adapt to changing requirements and improve overall testing efficiency.
Ethical Considerations: Finally, remember the ethical implications of AI in testing. Ensure transparency, fairness, and accountability in AI-assisted decision-making processes to build trust and maintain integrity in your testing practices.
The Role of AI in Software Testing
As we investigate the impact of AI in software testing, a few key zones come into focus:
AI Utilities for Test Case Generation, Test Execution, and Defect Prediction.
AI-Powered Test Case Generation: AI algorithms utilize strategies such as natural language processing (NLP) and machine learning (ML) to analyze prerequisite archives, user stories, and historical test data. They can recognize critical ways, edge cases, and potential vulnerabilities inside the software, producing test cases that cover these perspectives comprehensively. Moreover, AI in software testing can prioritize test cases based on risk factors, guaranteeing that high-impact zones are altogether tested.
AI-Assisted Test Execution: AI-assisted test execution optimizes testing processes by powerfully designating assets, prioritizing test cases, and adjusting testing techniques based on real-time input. AI algorithms can identify flaky tests, reroute test streams to maintain a strategic distance from bottlenecks and parallelize test execution to speed up input cycles. This approach minimizes testing costs and accelerates time-to-market for software releases.
AI-Based Defect Prediction: Machine learning models trained on historical defect data can anticipate potential defects and vulnerabilities in software code. By analyzing code complexity, altering history, and code quality measurements, AI can flag regions that are likely to cause issues. This proactive approach empowers developers to focus their efforts on code areas with a higher probability of defects, diminishing post-release bug events.
AI-Powered Test Automation Frameworks and Test Data Management
AI-Powered Test Automation Frameworks: AI-powered test automation frameworks consolidate keen features such as self-healing tests, versatile test execution, and prescient support. They utilize AI algorithms to identify and resolve test failures, optimize test execution based on historical information, and anticipate maintenance tasks for test scripts. This moves forward test steadiness, decreases false positives, and improves in general automation efficiency.
AI-Powered Test Data Management: AI computerizes test data management by analyzing data dependencies, making engineered test data sets, and anonymizing delicate data. It can distinguish information varieties required for testing distinctive scenarios and produce data that mimics real-world utilization patterns. This guarantees that testing environments are practical, different, and compliant with data protection regulations.
AI in Test Environment Provisioning and Test Maintenance.
Dynamic Test Environment Provisioning: AI analyzes asset accessibility, test prerequisites, and historical utilization patterns to dynamically provision test environments. It can distribute assets effectively, turn up virtualized environments, and configure network settings based on testing needs. This dynamic provisioning diminishes holding up times for test environments and progresses testing efficiency.
Intelligent Test Maintenance: AI in software testing automates test maintenance tasks by recognizing excess or obsolete test cases, recommending optimizations, and automatically upgrading test scripts. It analyzes code changes, affect analysis reports, and test coverage information to guarantee that tests stay significant and compelling. This decreases maintenance overhead and keeps testing forms agile and responsive to software changes.
AI enhances Test Efficiency, Effectiveness, and Reporting.
Progressed Test Scope and Accuracy: AI algorithms exceed expectations in recognizing complex test scenarios that conventional testing approaches might neglect. By leveraging strategies like genetic algorithms and support learning, AI can create test cases. These test cases cover a wide range of functionalities and edge cases. This comes about in progressed test scope and higher precision in recognizing software defects and performance issues.
AI-Enhanced Test Reporting and Analytics: AI-powered analytics tools analyze test results and identify patterns. They provide significant insights into test scope, performance trends, and defect clustering. AI-powered analytics tools analyze test results and identify patterns, thereby producing visualizations, trend examination reports, and inconsistency detection cautions. These insights help teams prioritize testing endeavors and make data-driven choices, enhancing overall test visibility and effectiveness.
AI-Powered Test Optimization and Performance Monitoring: AI plays a significant part in optimizing test processes and observing performance measurements. AI algorithms analyze testing information, execution times, asset utilization, and system behavior to distinguish optimization opportunities. Furthermore, this incorporates dynamically altering test arrangements, prioritizing critical tests, and optimizing test execution workflows for proficiency. Moreover, AI-assisted performance monitoring tools persistently screen application performance amid testing, identifying bottlenecks, memory leaks, and performance relapses. They produce performance reports, distinguish performance degradation patterns, and give suggestions for improving application performance.
Enhanced Collaboration between Development and Testing Teams
AI-powered test automation cultivates upgraded collaboration between development and testing teams:
Streamlined Communication: AI-assisted testing tools encourage consistent communication and collaboration between development and testing teams, empowering real-time input and issue resolution.
Shared Bits of Knowledge: AI-powered analytics give important bits of knowledge into testing measurements, performance patterns, and defect patterns, cultivating data-driven decision-making and persistent improvement.
Cross-Functional Collaboration: AI empowers cross-functional collaboration between developers, testers, data researchers, and AI masters, advancing collaboration and collective problem-solving.
Predictions for the Future of AI in Software Testing
Looking ahead, the future of AI in software testing holds promising predictions:
Advancements in AI Algorithms: Proceeded advancements in AI algorithms will lead to more advanced testing techniques, including progressed inconsistency discovery, self-learning testing frameworks, and predictive analytics.
Integration with DevOps and CI/CD: AI-powered testing will consistently coordinate with DevOps and Continuous Integration/Continuous Deployment (CI/CD) pipelines, thereby enabling quicker feedback loops. This includes automated testing in production environments and upgraded release cycles.
AI-Assisted Test Orchestration: AI will play a central part in test orchestration, dynamically managing test environments, assets, and test execution methodologies based on real-time data and project priorities.
Challenges and Opportunities in AI-Powered Testing
While AI-powered testing offers immense opportunities, it also presents challenges:
Complexity of AI Integration: Integrating AI into existing testing frameworks requires expertise in AI technologies, data management, and test automation, posing initial implementation challenges.
Data Quality and Bias: Ensuring data quality, addressing biases in AI models, and maintaining data privacy and security are ongoing challenges that organizations must address.
Skills Gap and Training: Building AI capabilities within testing teams, upskilling testers in AI concepts, and fostering a culture of AI-assisted testing require continuous learning and investment in training programs.
Strategies for Maximizing the Potential of AI-Powered Test Automation
To maximize the potential of AI-powered test automation, organizations can adopt the following strategies:
Strategic Alignment: Align AI initiatives with business objectives, prioritize use cases with high ROI potential, and develop a roadmap for AI integration into testing processes.
Continuous Learning and Collaboration: Invest in training programs, workshops, and knowledge-sharing sessions to build AI expertise within testing teams and foster collaboration with AI specialists and data scientists.
Data Governance and Ethics: Implement robust data governance practices, ensure data quality and integrity, address algorithm biases, and adhere to ethical guidelines for AI-assisted testing.
Pilot Projects and Iterative Approach: Start with pilot projects to validate AI capabilities, gather feedback, iterate on improvements, and gradually scale AI initiatives across testing environments.
Conclusion
In conclusion, AI-powered test automation stands as a significant force in revolutionizing AI in software testing in 2024 and beyond. With its capacity to upgrade effectiveness, precision, and speed in testing processes, AI-Assisted solutions are, therefore, reshaping how software is created and validated. By leveraging machine learning, natural language processing, and other AI technologies, organizations can streamline their testing workflows and identify defects earlier. Consequently, this enables them to bring high-quality software products to market faster than ever before.
As we move forward, the integration of AI into test automation will proceed to advance, offering indeed more progressed capabilities such as predictive analytics, autonomous testing, and versatile test procedures. This development will help optimize testing efforts, reduce costs, and improve overall software quality. Ultimately, it will benefit both businesses and end-users alike. Grasping AI-powered test automation is not just a trend; rather, it is a key imperative for modern software development organizations. Furthermore, it is essential for staying competitive in today’s fast-paced digital environment using AI in software testing in 2024.
Working with PDF documents programmatically can be a challenging task, especially when you need to extract and manipulate text content. However, with the right tools and libraries, you can efficiently convert PDF text to a structured JSON format.
Converting PDF to JSON programmatically offers flexibility and customization, especially in dynamic runtime environments where reliance on external tools may not be feasible. While free tools exist, they may not always cater to specific runtime requirements or integrate seamlessly into existing systems.
Consider scenarios like real-time data extraction from PDF reports generated by various sources. During runtime, integrating with a specific tool might not be viable due to constraints such as security policies, network connectivity, or the need for real-time processing. In such cases, a custom-coded solution allows for on-the-fly conversion tailored to the application’s needs.
For Example:
E-commerce Invoice Processing: Extracting invoice details and converting them to JSON for real-time database updates.
Healthcare Records Management: Converting patient records to JSON for integration with EHR systems, ensuring HIPAA compliance.
Legal Document Analysis: Extracting specific clauses and dates from legal documents for analysis.
Free tools are inadequate for real-time, automated, and secure PDF to JSON conversion. Coding your own solution ensures efficient, scalable, and compliant data handling.
In this blog, we’ll walk through a Java program that accomplishes using the powerful iTextPDF and Jackson libraries. Screenshots will be included to illustrate the process in Testing.
Introduction for Converting PDF to JSON in Java
PDF documents are ubiquitous in the modern world, used for everything from reports and ebooks to invoices and forms. They provide a versatile way to share formatted text, images, and even interactive content. Despite their convenience, PDFs can be difficult to work with programmatically, especially when you need to extract specific information from them.
Often, there arises a need to extract text content from PDFs for various purposes such as:
Data Analysis: Extracting textual data for analysis, reporting, or further processing.
Indexing: Creating searchable indexes for large collections of PDF documents.
Transformation: Converting PDF content into different formats like JSON, XML, or CSV for interoperability with other systems.
JSON (JavaScript Object Notation) is a lightweight data interchange format that’s easy for humans to read and write, and easy for machines to parse and generate. It is widely used in web applications, APIs, and configuration files due to its simplicity and versatility.
In this guide, we will explore how to convert the text content of a PDF file into a JSON format using Java. We’ll leverage the iTextPDF library for PDF text extraction and the Jackson library for JSON processing. This approach will allow us to take advantage of the structured nature of JSON to organize the extracted text in a meaningful way.
Prerequisites for Converting PDF to JSON in Java
Before we dive into the code, ensure you have the following prerequisites installed and configured:
Java Development Kit (JDK)
Maven for managing dependencies
iTextPDF library for handling PDF documents
Jackson library for JSON processing
Step-by-Step Installation and Setup for Converting PDF to JSON in Java
Install Java Development Kit (JDK)
The JDK is a software development environment used for developing Java applications. To install the JDK:
Start IntelliJ IDEA: Open from the start menu (Windows).
Complete Initial Setup: Import settings or start fresh.
Start a New Project: Begin a new project or open an existing one.
Open IntelliJ IDEA:
Launch IntelliJ IDEA on your computer
Create or Open a Project
If you already have a project, open it. Otherwise, create a new project by selecting File > New > Project….
Name your project and select the project location
Choose Java from Language.
Choose Maven from the Build systems.
Select the project SDK (JDK) and click Next.
Choose the project template (if any) and click Next.
Then click Create.
Create a New Java Class
In the Project tool window (usually on the left side), right-click on the (src → test → java) directory or any of its subdirectories where you want to create the new class.
Select New > Java Class from the context menu.
Name Your Class
In the dialog that appears, enter the name of your new class. For example, you can name it PdfToJsonConversion.
Click OK/Enter.
Add the following dependencies to your pom.xml file for Converting PDF to JSON in Java:
<dependencies>
<!-- https://mvnrepository.com/artifact/com.itextpdf/itext-core -->
<dependency>
<groupId>com.itextpdf</groupId>
<artifactId>itext-core</artifactId>
<version>8.0.3</version>
<type>pom</type>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>2.13.0</version> <!-- Use the same version for consistency -->
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.13.4.1</version> <!-- Use the latest version available -->
</dependency>
<!-- Jackson Annotations -->
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>2.13.3</version> <!-- Use the latest version available -->
</dependency>
<dependency>
<groupId>org.testng</groupId>
<artifactId>testng</artifactId>
<version>7.8.0</version>
<scope>compile</scope>
</dependency>
</dependencies>
Write Your Code to Convert PDF to JSON in Java
IntelliJ IDEA will create a new .java file with the name you provided.
You can start writing your Java code inside this file.
The Java Program to Covert PFT to JSON
Here is the complete Java program that converts a PDF file to JSON:
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.SerializationFeature;
import com.fasterxml.jackson.databind.node.ArrayNode;
import com.fasterxml.jackson.databind.node.ObjectNode;
import com.itextpdf.kernel.pdf.PdfDocument;
import com.itextpdf.kernel.pdf.PdfPage;
import com.itextpdf.kernel.pdf.PdfReader;
import com.itextpdf.kernel.pdf.canvas.parser.PdfTextExtractor;
import org.testng.annotations.Test;
import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
public class PdfToJsonConversion {
@Test
public static void convertPdfFileToJson() {
String inputPdfPath = "C:\\Users\\Mangesh\\Downloads\\What is Software Testing.pdf";
String outputJsonPath = "src/test/java/What is Software Testing.json";
List<String> contentList = new ArrayList<>();
try (PdfDocument pdfDoc = new PdfDocument(new PdfReader(inputPdfPath))) {
int numPages = pdfDoc.getNumberOfPages();
for (int i = 1; i <= numPages; i++) {
PdfPage page = pdfDoc.getPage(i);
String pageContent = PdfTextExtractor.getTextFromPage(page);
contentList.add(pageContent);
}
} catch (IOException e) {
e.printStackTrace();
}
// Create JSON object
ObjectMapper mapper = new ObjectMapper();
mapper.enable(SerializationFeature.INDENT_OUTPUT);
ArrayNode pagesArray = mapper.createArrayNode();
// Add page contents to JSON array
for (int i = 0; i < contentList.size(); i++) {
ObjectNode pageNode = mapper.createObjectNode();
pageNode.put("Page", i + 1);
// Split content by lines and add to JSON object with line number as key
String[] lines = contentList.get(i).split("\\r?\\n");
ObjectNode linesObject = mapper.createObjectNode();
for (int j = 0; j < lines.length; j++) {
linesObject.put(Integer.toString(j + 1), lines[j]);
}
pageNode.set("Content", linesObject);
pagesArray.add(pageNode);
}
File outputJsonFile = new File(outputJsonPath);
// Write JSON to file
try {
mapper.writeValue(outputJsonFile, pagesArray);
} catch (IOException e) {
e.printStackTrace();
}
System.out.println("Content stored in " + outputJsonFile.getName());
}
}
Explanation
Let’s break down the code step by step:
1. Dependencies
Jackson Library:
ObjectMapper, SerializationFeature, ArrayNode, ObjectNode: These are from the Jackson library, used for creating and manipulating JSON objects.
iText Library:
PdfDocument, PdfPage, PdfReader, PdfTextExtractor: These classes are from the iText library, used for reading and extracting text from PDF documents.
TestNG Library:
@Test: An annotation from the TestNG library, used for marking the convertPdfFileToJson method as a test method.
Java Standard Library:
File, IOException, ArrayList, List: Standard Java classes for file operations, handling exceptions, and working with lists.
2. Test Annotation
The class PdfToJsonConversion contains a static method convertPdfFileToJson which is annotated with @Test, making it a test method in a TestNG test class.
3. Method convertPdfFileToJson:
This method handles the core functionality of reading a PDF and converting its content to JSON.
4. Input and Output Paths:
inputPdfPath specifies the PDF file location, and outputJsonPath defines where the resulting JSON file will be saved.
5. PDF to Text Conversion:
Create a PdfDocument object using a PdfReader for the input PDF file.
Get the number of pages in the PDF.
Loop through each page, extract text using PdfTextExtractor, and add the text to contentList.
Handle any IOException that may occur.
6. Creating JSON Objects:
Create an ObjectMapper for JSON manipulation.
Enable pretty printing with SerializationFeature.INDENT_OUTPUT.
Create an ArrayNode to hold the content of each page.
7. Adding Page Content to JSON:
Iterate over contentList to process each page’s content.
For each page, create an ObjectNode and set the page number.
Split the page content into lines, then create another ObjectNode to hold each line with its number as the key.
Add the linesObject to the pageNode and then add the pageNode to pagesArray.
8. Writing JSON to File
Create a File object for the output JSON file.
Use the ObjectMapper to write pagesArray to the JSON file, handling any IOException.
Print a confirmation message indicating the completion of the process.
9. Output
The program outputs the name of the JSON file once the conversion is complete.
Running the Program
To run this program, ensure you have the required libraries in your project’s classpath. You can run it through your IDE or using a build tool like Maven.
Open your IDE and load the project.
Ensure dependencies are correctly set in your pom.xml.
Run the test method convertPdfFileToJson.
You should see output similar to this in your console: Content stored in What is Software Testing.json. The JSON file will be created in the specified output path.
JSON Output Example
Here’s a snippet of what the JSON output might look like.
[ {
"Page" : 1,
"Content" : {
"1" : "What is Software Testing? ",
"2" : "Last Updated : 24 May, 2024 ",
"3" : " ",
"4" : " ",
"5" : "",
"6" : "Software testing can be stated as the process of verifying and validating whether a ",
"7" : "software or application is bug-free, meets the technical requirements as guided by "
}
}, {
"Page" : 2,
"Content" : {
"1" : " Increased customer satisfaction: Software testing ensures reliability, security, ",
"2" : "and high performance which results in saving time, costs, and customer "
}
} ]
Conclusion
Converting PDF text content to JSON can greatly simplify data processing and integration tasks. With Java, the iTextPDF, and Jackson libraries, this task becomes straightforward and efficient. This guide provides a comprehensive example to help you get started with your own PDF to JSON conversion projects. https://github.com/mangesh-31/PdfToJsonConversion
Hello! I’m Mangesh, a Software Tester SDET. In my professional life, I focus on ensuring the quality of software products through thorough testing and analysis. I have been learning and working with Selenium, Java, and Playwright to develop automated testing solutions. Currently, I am working Jr. SDET at SpurQLabs Technologies Pvt. Ltd.
Mobile App Automation using Appium involves various ways to locate elements for effective testing. In this blog, we’ll se the Mobile app automation Using Appium Inspector, we can inspect elements on both iOS and Android devices.
Now we’ll go for locating the Android Element
Mobile App Testing tools are available in the market right now are as follows:
Katalon
Appium
Espresso
XCTest
Robotium
Selndroid
Flutter
Robot Framework
iOS-driver
Xamarin
So currently we are going to Inspecting the Locator Strategy for Mobile App Automation using Appium, For the initial setup of Appium for Android device you can refer this blog How To Configure Our System For Real Mobile App Automation. This blog will guide you for the mobile app automation using Appium setup on Android device.
We have various ways to locate the elements for mobile app automation using Appium Inspector, mainly we have the following ways to locate the elements:
Id
Xpath
Customized Xpath
Accessible Id
First, we’ll see how to locate the specific element for Mobile App Automation using Appium
After starting the session on an Android phone you will see the below Appium inspector window
In this image, you can see the mobile screen, App source, and Selected Element tabs.
When you select the particular element on the mobile screen displayed on Appium Inspector, You will see the below image, I have selected the C button from a calculator for mobile app automation using appium.
Now we can see the DOM content is loaded in the App Source tab, and the Attributes and values will be displayed in the Selected Element tab.
Now we’ll see how to locate the element from the Selected Element tab.
In the above image you can see the attribute and values to locate the element
Now we can see the Locator strategies to locate this element for mobile app automation using appium. First, we’ll see locating the element using the Id
First, we’ll have to see the available Attributes for that particular element and then try to locate the element. So copy the ID from given Selected Element tab as shown below
So now We’ll see how to check whether the Id is a valid locator or not.
For that first click on the Search bar
Then make sure you have selected the correct locator Strategy as shown in the below image.
Now after clicking on the search element, you will get to see the identified element as shown in the below image
As the element is getting highlighted it indicated that we can use that ID to locate that particular element
Now we’ll see locating elements using XPath for Mobile App Automation using Appium
In a similar way to Id we can locate the element using Xpath, So for first we need to click on the Xpath shown in the below image.
Now click on the search button explained above
Make sure that you have selected the XPath as Locator Strategy as shown. Then Paste the copied XPath in the Selector Box and click on the Search Button, so then you can see the below image the element is located by the XPath
The element is getting highlighted and that means we can use this XPath to locate this element
Now we’ll see how to use customized XPath for Mobile App Automation
This allows us to handle parameterization and overcome limitations when ID or XPath is not available by default. So for that, we need to know how we can create XPath
The first step is you need to find the class for that particular element
As you can see the above image, class is present for that particular element. So first step is we need to copy this class value
The next step is to choose the attribute you want to use the value of.
These are the various attributes you can use to customize XPath
So after that, you can create the Customized XPath, So here is a sample XPath I have used to locate the equal button from the Calculator app
In this XPath, I have chosen text attribute. So in the below image, you can see the combination of class and attribute and value. This is how we can create customized XPath
As shown in the below image you can see the Located element
So when the requirement is there to create a parameterized locator or ID is not available, at that time you can use Customized XPath
For accessibility Id you can follow similar steps like ID to locate the element. The only condition is Accessibility ID should be available for that particular element
Now we’ll go for locating the iOS element for Mobile App Automation using Appium
For iOS automation We’ll be going to see how we can locate the element. To locate elements on iOS devices following strategies are available
Accessibility Id
XPath
Customized XPath
Now we’ll see how to locate the element using Accessibility ID on iOS device.
For that, we’ll have to start the Appium Session on iOS. After starting the Appium session on iOS device you will get to see the below window of Appium inspector
This will be the home page of the calculator on the iOS App. On this screen, you can see three windows Mobile screen, App Source, and Selected Element. When you select any of the elements displayed on the Mobile screen the screen will be shown below.
In the above Image, I have selected the AC button which is for All Clear. After selecting that element the DOM content is loaded in the App Source window and in the Selected Element window we can see the attributes and values that can be used for inspecting the elements.
We have so many options to locate the element as you can see in the Selected Element window. We have accessibility ID, XPath, and customized XPath for Mobile App Automation using Appium.
Now we’ll see how to locate the element using accessibility id for Mobile App Automation using Appium
So first we’ll go to search for element as shown in the below image
As shown in the above image you can see that I have selected Locator Strategy as the Accessibility ID and the value I have passed the accessibility ID got from the Selected Element window. Now, I’ll click on the Search button.
The system will display the result window below.
As shown in the screenshot, the AC button is highlighted after successfully finding the window element. The count for the found element is 1, and you can use this accessibility ID to locate this specific element.
Note: So for locating the elements using XPath and customized XPath you can refer the steps mentioned for Android.
Preffered Locator Strategy: As you can see the Selected element window, We have multiple options to locate the element for Mobile App Automation. So there might be a confusion to select the correct locator strategy. So here are some key points which you can consider while choosing the locator strategy
Most preferred locator strategy will be id (Android) or accessibility id (iOS). Because id’s are designed to be unique for direct access.
name locator strategy can be used if the particular element have the unique name which can be used to locate element.
The XPath are more likely to use if id not available or we have requirement to create locator which needs to be parameterized.
Conclusion:
As we see, we have multiple ways to locate the elements on the Mobile Application. Here in this blog, we got to know the the locator strategies to locate the elements on Android and iOS Application for Mobile App Automation using Appium. So you have multiple options to locate the elements, From which you have to decide which strategy suits best for your requirements. So as mentioned above id is fastest way to locate elements, But you have choice to use XPath and customized XPath for parameterization. https://github.com/appium/appium-inspector/releases
Overall, this blog provides an overview of how to locate elements Mobile App Automation using Appium Inspector. Additionally, it explains the various locator strategies you can choose based on the requirements of your test script.
Swapnil is an SDET with 1+ years of hands-on experience in Manual, Automation, and API testing. The technologies I have worked on include Selenium, Playwright, Cucumber, Appium, Postman, SQL, GitHub, Java, and Python. Also, I love Blog writing and learning new technologies.
Setting up Appium for testing on real devices for android app automation can be tricky. Many testers struggle with installing the right software, setting environment variables, and connecting their devices properly. These issues can cause a lot of frustration and slow down the testing process.
In this blog, we’ll make it easy for you. We’ll walk you through each step, from installing necessary software like the Java Development Kit (JDK) and Android Studio, to setting up your Android device for android app automation. We’ll also show you how to install Appium, configure it correctly, and use tools like Appium Inspector to interact with your app.
By following this simple guide, you’ll be ready to test your mobile apps on real devices quickly and efficiently.
What is Appium testing in Android App Automation
Appium is an open-source automation tool used for testing mobile applications. It allows testers to automate native, hybrid, and mobile web applications on iOS and Android platforms using the WebDriver protocol. Appium provides a unified API (Application Programming Interface) that allows you to write tests using your preferred programming language (such as Java, Python, JavaScript, etc.) and test frameworks. It supports a wide range of automation capabilities, including gestures, device rotation, multi-touch actions, and handling various types of mobile elements. Appium enables cross-platform testing, where the same tests can be executed on multiple devices, operating systems, and versions, providing flexibility and scalability in mobile app testing or android testing.
Advantages of Using Appium in Android App Automation:
Appium is an open source and free tool available for testers and developers.
Appium supports both real device and emulators/simulators testing.
Appium is compatible with popular testing frameworks and tools, making it easy to integrate into existing testing workflows and environments.
Advantages of using real device for Android App Automation:
Real device allows you to check your application under different network like 2G,3G,4G and 5G.
Using real device we can test hardware specific features like GPS, fingerprint and camera.
Using a real device provides more accuracy by taking some factors into consideration like device battery, processor, memory and device size.
Step→1
Install Java Development Kit (JDK):
Download and install the latest JDK from the official Oracle website.
Set JAVA_HOME as environment variable.
Also add jdk’s bin folder path in Path environment variable.
Step→2
Install Android Studio:
Download and install Android Studio from the official Android website.
After successful installation now we will set the ANDROID_HOME environment variable.
Also put platform tools path in path variable.
Now open cmd and run adb in command line and it should get executed successfully.
Step→3
Install Node.js:
If you haven’t already installed Node.js, you can download and install it from the official Node.js website.
Once the installation is complete check node version using command node -v also npm -v.
Step→4
Install Appium for real device testing using command npm install -g appium in command line:
Verify appium version using appium -v in command line.
Now run the command appiumin command line using this command your server should start and we are ready to do testing.
Step→5
Install Appium for real device testing using command npm install -g appium in command line:
Now run appium-doctor in command lineto check weather every dependency required for appium has been installed successfully.
Step→6
Now we need to install UIAutomator driver which allows to interact with the UI elements of Android apps during automated testing. It provides improved stability and performance compared to the original UIAutomator driver. To install it use this command appium driver install uiautomator2 in command line.
Step→7
Now for real device testing we also need to make some changes on device side too so we need to enable developer option for this:
Open setting and click on about phone.
Click on software information.
Click on Build number 5 times to enable developer mode.
Now once this option is enabled we need to enable usb debugging option as well.
Note: Above information to enable the developer mode its for SAMSUNG device it will be different for other device type.
What is Appium Inspector in Android App Testing?
Appium inspector is a tool which provides testers with a graphical user interface for inspecting and interacting with elements within mobile applications.
Step→8
Install appium inspector for windows using below link appium inspector.
Step→9
Start the appium session using command appium -a 127.0.0.1 -p 4723
Alternatively we can use appium GUI Appium GUI to start the server
i. Enter the host as 127.0.0.1
ii. Enter port number as 4723
iii. If you are using Appium GUI for start server.we need to also add remote path for Appium inspector
Step→10
Open the appium inspector enter remote host as 127.0.0.1 and port as 4723.
Configuring Desired Capabilities using Appium for Android App Automation:
When setting up automation with Appium for Android devices, it’s crucial to define the desired capabilities appropriately. These capabilities act as parameters that instruct Appium on how to interact with the device and the application under test.
deviceName: This parameter specifies the name of the device being used for testing. It’s essential to provide an accurate device name to ensure that Appium connects to the correct device.
udid: The Unique Device Identifier (UDID) uniquely identifies the device among all others. Appium uses this identifier to target the specific device for automation. Make sure to input the correct UDID of the device you intend to automate.
platformName: Here, the platform name is set to “Android,” indicating that the automation is targeted towards the Android platform.
platformVersion: This parameter denotes the version of the Android platform installed on the device.
automationName: Appium supports multiple automation frameworks, and here, “UiAutomator2” is specified as the automation name. UiAutomator2 is a widely used automation framework for testing Android apps.
appPackage: The app package is the unique identifier for the application under test. It’s essential for Appium to know which app to launch and interact with during automation.
appActivity: This parameter specifies the main activity of the application that needs to be launched.
For device udid run adb device command in command line
For device name and version we can check software information from android settings
For application package and appActivity we can download Apk Info application from play store
For application bundle Id and App activity
Step→11
Once you enter the remote host and port number enter below capabilities to open calculator application from your android devic for android testing.
The images below illustrate how I started the Appium server using the Appium GUI and successfully opened the Calculator app in Appium Inspector with the specified capabilities and now it’s ready to inspect your app to prepare for automated testing efficiently.
Conclusion:
Setting up Appium for testing on real Android devices can initially seem daunting due to the numerous steps involved and the technical nuances of configuring software and environment variables. However, by following this step-by-step guide, the process becomes manageable and straightforward.
Investing the time and effort to configure Appium correctly pays off by significantly enhancing the efficiency and effectiveness of your mobile testing strategy. This setup not only improves the
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Junior Software Development Engineer in Test (JR. SDET) with 1 year of hands-on experience in automating and testing mobile applications using Python and Appium. Proficient in Selenium and Java, with a solid understanding of real device testing on both iOS and Android platforms. Adept at ensuring the quality and performance of applications through thorough manual and automated testing. Skilled in SQL and API testing using Postman.
At times, certain project specifications demand comprehensive testing of a web application across a diverse range of web browsers and their varying versions. Maintaining consistent functionality, visual appearance, and user engagement is essential.
To execute this type of cross-browser testing efficiently, there are two primary approaches:
One involves manual testing, where the website is examined across multiple browsers and devices.
The other approach entails leveraging specialized tools and services that replicate distinct browser environments.
Particularly, BrowserStack stands out as a tool that has garnered considerable attention in this field, thanks to its intuitive user interface.
Executing test cases on BrowserStack is crucial for QA teams to ensure the compatibility, functionality, and performance of websites and applications across a wide range of real browsers and devices.
It helps identify bugs, responsive design issues, and user experience problems, while also supporting automated testing and real environment testing.
BrowserStack eliminates the need for local testing infrastructure, reducing costs and enhancing overall testing efficiency.
So first let’s talk about the Basic Framework where we can automate test cases using the BDD approach to execute tests on a browser on the local machine.
So for this instead of explaining it from scratch here again, You can explore my blog where I have explained How to create a BDD automation framework using Cucumber in JavaScript and Playwright.
This blog gives you step-by-step guidance on how to Create Feature files, Steps files, Page Files, and various other utilities to make the framework more reusable and robust. So here is the link to this blog: https://shorturl.at/bjuvU
You must have a BrowserStack account to execute your tests, to create a new BrowserStack account you can visit the below link. https://www.browserstack.com/users/sign_up
To get started with BrowserStack implementation first store your BrowserStack username and access key in the .env file. To get the username and access key to navigate to your BrowserStack account. Click on Access key dropdown from dashboard and you will be able to see your username and access key.
Here you will be able to see your account details. Copy your Username and Access Key and store it in a .env file as shown below. To execute tests on BrowserStack set the Browserstack variable to ‘true’.
Now to set BrowserStack configurations Create a file as “Browserstack.js”, as you can see I have created a “Browserstack.js file” in the Utility folder.
Here are the essential BrowserStack configurations to integrate into this file.
The playwrightClientVersion variable is declared and assigned the output of the shell command executed using execSync from the child_process module.
.execSync('npx playwright --version’):
The command executed is npx playwright –version, which retrieves the version of the Playwright framework installed on the system. The output is converted to a string, trimmed to remove leading or trailing whitespace, and then split into an array based on spaces. The second element of the array (split(‘ ‘)[1]) represents the Playwright’s version.
caps():
This function is responsible for generating a configuration object used for browser testing with the BrowserStack platform and Playwright framework. It returns an object that contains configuration options for browser testing like browser, os, os_version, etc.
These are the variables from the .env file. If you are not using a .env file you can directly pass the BrowserStack username and access key.
As we have all the configurations ready, we will set up a test environment to execute tests on BrowserStack. Use the file where you have your Before and After methods. In the playwright-Js Framework we have these methods stored in the ‘TestHooks.js file’ so will update that file as below.
The above code Generates capabilities for BrowserStack using the caps() function from the ‘BrowserStack’ module. Construct the WebSocket endpoint URL for BrowserStack with the generated capabilities.
Now we have completed the BrowserStack implementation in our framework, now you can execute your tests on BrowserStack.
To execute tests on BrowserStack make sure you have “Browserstack = true” in the .env file.
Then you can execute your tests using the command ’npm run test’ on your terminal.
After running the test cases on BrowserStack, you’ll find the report below. Access comprehensive execution videos, and test logs for both successful and unsuccessful cases, along with execution duration.
Conclusion:
Integrating BrowserStack with Playwright JavaScript tests, gives access to a wide range of real browsers and devices, enabling comprehensive cross-browser and cross-platform testing. The scalability and cost-effectiveness of BrowserStack eliminate the need for maintaining an extensive device lab. Ultimately, this integration enhances test coverage, improves software quality, and ensures a seamless user experience across various browsers and platforms.
Priyanka is an SDET with 2.5+ years of hands-on experience in Manual, Automation, and API testing. The technologies she has worked on include Selenium, Playwright, Cucumber, Appium, Postman, SQL, GitHub, and Java. Also, she is interested in Blog writing and learning new technologies.
