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Automating Test Case Generation for Android Applications 
using Model-based Testing

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Automating Test Case Generation for Android Applications 
using Model-based Testing

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Step 1: Understanding the Application Under Test (AUT)

Analyze the application’s functionality by exploring it manually.

Identify the core functionalities and user interactions.

Document possible use cases and user behaviors.

Step 2: Selecting a Suitable Testing Approach

The study recommends Model-Based Testing (MBT).

MBT uses state machine diagrams or statecharts to model the app's behavior.

Step 3: Modeling the Application Using Statecharts

Every possible user interaction is treated as a potential test case.

Statecharts are developed using a modeling tool like Yakindu Suite.

Statecharts represent different states of the application and transitions between them.

Step 4: Generating Abstract Test Cases

Traverse the statecharts to extract potential test paths.

Use a Depth-First Search (DFS) algorithm to ensure full model exploration.

This results in abstract test cases, which are paths representing user interactions.

Step 5: Translating Abstract Test Cases into Scripts

Use the Yakindu model translator to generate code from the statechart.

This transformation results in raw test scripts, which require additional refinement.

The generated code is structured but not yet executable.

Step 6: Editing and Enhancing Test Scripts

Manually refine the scripts to include real user interactions such as:

Clicks

Swipes

Input text

Button presses

Assign identifiers (IDs) to UI elements using UiAutomator.

Integrate scripts with a test execution framework like Appium.

Step 7: Executing Test Scripts

Use Eclipse + Appium + TestNG to execute the refined test cases.

Connect a real Android device or an emulator to run the tests.

Appium will interact with the UI based on the scripts.

Step 8: Evaluating Test Results

Generate test execution reports using TestNG.

Evaluate results manually or with automated reporting.

Identify failed test cases and debug accordingly.

Step 9: Optimizing and Automating Further

If necessary, refine the model and test cases.

Automate repetitive tests for regression testing.

Use Natural Language Processing (NLP) techniques to convert functional requirements into UML models, reducing manual effort.

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1. List all 5 datasets on Padlet.

2. Run app Randomix.

3. Generate test cases for Randomix.

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An application to choose between numbers, answers and options randomly. The sole purpose of this application is to provide users four different ways to make a random choice. Users can shake the device to launch the options of decision maker such as a roulette.

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Sky Map is an open-source Android app that acts as a handheld planetarium. It allows users to explore stars, planets, and constellations by moving their device. The app uses device motion sensors (accelerometer and gyroscope) to adjust the sky view based on device orientation.

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Point & Hit is a fast-paced tilt-based game. Users have to shoot targets before they disappear by tilting the device to aim the targets.

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A utility app that contains the basic features of all clock applications such as an alarm, clock, timer and a stopwatch. Users can flip and shake the device to dismiss or postpone an alarm.

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A fun app that features an interactive teddy bear. The teddy bear can be moved around by moving its head, paws, tummy, and ears. By shaking the device, the teddy bear will be triggered for an animation.

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1. ✅ Semester 1 (6 bulan) : (Okt 2024)Sem 1 2024/2025

RM

Chap 1,2,3

2. Semester 2 (12 bulan): (Mac 2025) Sem 2 2024/2025

Qualifying Assessment

3. Semester 3 (18 bulan): (Okt 2025) Sem 1 2025/2026

Defend Proposal

4. Semester 4 (24 bulan) : (Mac 2026) Sem 2 2025/2026

Progress Assessment

5. Semester 5 (30 bulan): (Okt 2026) Sem 1 2026/2027

Progress Assessment

6. Semester 6 (36 bulan) : (Mac 2027) Sem 2 2026/2027

Submit thesis end of sem 6

7. Semester 7 (42 bulan) : (Okt 2027) Sem 1 2027/2028

Viva

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1. Convert to appium test script ✅

2. Results table (tc no, task, execution time, state coverage, results pass/fail)

3. Screenshot results appium ✅

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1. C4=Find related papers that used state coverage

2. C4=Process generate test cases for touch data set

3. C4=Planning steps for motion data set

4. C4=Justify use of state coverage

5. C4=Planning for phase 3 (process of user behavioral analysis, and user interaction pattern identification, build the multimodal)

6. Conclusion chap 5

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Generate test case - Automating Test Case Generation for Android Applications using Model-Based Testing

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Generate test case - Automating Test Case Generation for Android Applications using Model-Based Testing

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Test Suite Prioritization Based on Optimization Approach

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Test Suite Prioritization Based on Optimization Approach

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Using Reinforcement Learning

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Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

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Testing for Android Apps

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CamDroid Context-Aware Model-Based Automated GUI Testing for Android Apps