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Code Review in a Startup

Here I bring to you the 5th blog in the DevOps series showcasing our learning while #scalingup. Read our previous blog to know about the bunch of tactics that we used at different times during our evolution to achieve a successful clockwork during our DevOps journey.

Proper optimized code review is something that many startups miss. Some take the easy way out and ignore it and others spend years discussing the best practices, conventions and styles without ever committing code. Both these are slightly over exaggerated examples of paths to failure, but I am sure you can relate to these if you have ever tried to answer the all important question about code review “Exactly how much code review should your team do?”

For perfectionists, the answer is that code should always be reviewed and you should always be refactoring code and improving it wherever you see a scope. I have worked in companies, where people used to spend almost as many hours reviewing code as much as they would writing them. There are some practices like pair programming, which has maximizing code review as one of the results. So this is not exactly a wrong direction of thinking. The problem however is that when you are working with time constrained environments like that of a startup, you will not have a luxury where you can keep revisiting and improving code beyond a reasonable amount of time. And I have seen that surprisingly many people altogether avoid code review because timelines are constrained. I don’t even need to mention how risky this is and how dangerous this course of action is. However it seems many people do exactly that.

In this blog post, I will attempt to throw light on our experiences in deciding how much is enough. We too, like other startups have a time constrained environment, and a few (thankfully only a few) customers who want every feature done yesterday. So as a culture, we too try to make sure that we finish everything faster. Development faster, Testing Faster, Deployment to various environments faster. But we do not ignore code review. We ensure that we do code review. We have a few principles that we followed to make sure that code review happens all the time, and is neither too less nor too much. Here are they. What we have seen is that if you follow all these steps, then you have a sane code review process and you can guarantee a stable flow of god code into your repository. These points are in no particular order of importance.

You should do code review: The first principle is not a lame attempt at a joke. The idea is that code review should happen come what may. Without this principle being followed, every other principle in this list breaks down. We use git-flow as one of our developer methodologies with git. One of the advantages of this system is that code review is built in. Unless code is approved by a designated reviewer, the code does not go to the next level be it Testing or Production. The Approver is as responsible for a piece of code as the original developer. This way the reviewer spends more time reviewing and the original coder also reviews and corrects his code in advance to preempt the reviewer. This adds more points in the system where code review can happen and makes sure that code always gets reviewed and the review is not forgotten.

Requirement Matching: Does your code do what the requirements ask it to do? Are we doing less? Are we doing more? A quick inspection reveals mismatches if any. This goes a long way in finding out if there are any problems in the code.

Readability: There is an apocryphal statement that says that code is written once and read hundreds of times. While the statement may not be accurate, you may write more than once, and may not read hundreds of times, you still do get the picture. The basic premise is that code does get read many far more times than it gets written. Also once written, your code will also be read for review, bug fixes, enhancements, etc and not always by you. Also in the IT Sector, jobs switches happen a lot, so a new person should be able to understand and work with the code as soon as possible.. So it makes sense that whoever looks at your code after you have left is able to understand your code well and can alter if needed and maintain the code well till the product lifetime completes.

Reviewability: A further subset of this is that the code should also be reviewable, you and your code should be able to convey to the reviewer what the code is supposed to do and what the reviewer is supposed to review.

Scalability: Will your code be able to stand frequent and/or continuous requirement changes in the future? Will it be able to handle a reasonable amount of requirement change without having to have to rewrite the entire thing? Overall applications are live, especially in the Agile era, your requirements are never frozen, and hence the code also should never be frozen. It should be able to handle changes in requirements. A word of caution here, do not overdo this. While your code should be able to handle requirement changes, you cannot (and should not) make your code so generic that it can handle the proverbial ‘everything under the sun’. Your code should be reasonably scalable. Too much scalability also is as bad as too little. How much to go down this path will depend on your specific business needs. However it is not a bad idea to talk about specifics to your business stakeholders. They can tell you how and more importantly how much a feature will be used. You can then decide how scalable you want the code for that feature to be. The process of arriving at how much is just right, takes time to set, but once done, you will thank yourselves for the foreseeable future.

Improvements: This is one of the basic purposes of code review. This answers the question, “Can we do the same thing in a better way?”. Better way could mean one or more among, faster performance, better readability, more modularity, and others. You need to keep asking this question in a code review. If you can, then your code review is not complete, if you cannot improve any longer, then probably the code reached here after many rounds of reviews. Or was copied from well reviewed code. Again this is one of those things that has the potential to be overdone, so think carefully how far do you want to go down this rabbit hole without losing your wits.

BNBR: This is lifted from one of the policies of Quora, It means Be Nice, Be Right. The point is that while reviewing , you need to be nice and be right. Being Nice First. The point of a code review is to see if things can be done better by putting multiple heads instead of one. It is not to hurt or massage egos. What can be done by just pointing out issues with data should not descend into a shouting match (verbally or through the keyboard). Make sure that your comments are politely worded and are correct.

Code Scanners: Before you give your code for review, your code should be scanned by tools to make sure that basic checks are done for issues like parenthesis matching, formatting, typos, indentation, naming convention etc. Your reviewers will have a tougher time navigating your code if you do not fix these. If your reviewer finds these issues and not a code scanner, then you have not prepared for your review well.

The Unhappy Path: The code works fine, but some scenarios were not tested. How do you know if your code is able to handle most of the basic errors or exceptions. Your review should make sure that this is in place well before time. Again you need to use this judiciously. You should not overdo it.

Timeliness: Your code review should have a deadline. You cannot indefinitely keep reviewing the code, your review should finish within a deadline. If you ship years late, how will better code help you ?

Dark Spots: Every reviewer may not be able to review all aspects of the code. So it is a good practice to tell in the review comments what aspects were reviewed and what were not, so that everyone knows the extent of the code review. If everyone says it looks good to me, but everyone only happened to review the naming conventions, then it probably was better if only one person reviewed. If each reviewer mentioned this small info, then everyone knows in advance if there were any dark spots in that particular review and they will be able to redress it.

Fatigue: Do not review too many pieces of code at a time. If you happen to be spending a long time reviewing, then probably the code under review is too large or you are reviewing too many PRs at the same time. Reviewing is a thought intensive process and you need to make sure that it is done properly. So please take breaks, if you are tired of reviews and are still somehow powering through that will reflect in the quality of the reviews. A rule of thumb is to not review more than 60 minutes at a time or around 400 lines of code at a time.

Checklists: One good shortcut is to use checklist to review a PR or a piece of code against. These checklists ensure that your mind does not wander, wondering what you have missed and you will also be reviewing against pre decided metrics.

Defects: What do you do with the issues you found ? Not every issue needs to be or can be fixed immediately. You have to decide what to do with each review comment. Whether you will be fixing them, ignoring them or putting back into your backlog. Make sure this is a separate backlog for technical debt.

Overall these are the things that we follow while doing code review. Many of these helped us a lot to make sure that we are reviewing just enough to keep our process wrooming along while at the same time, not ignoring major issues.

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Testing framework plays a crucial role in the success of any automated testing process. Choosing the right test automation framework is important as it will maximize the test coverage and improve test efficiency which means a better return on investment.

There are some key points you need to keep in mind while choosing a suitable python testing framework. The framework should justify your testing needs and it should be easy to use. Check if the framework has integrations with other tools and frameworks that you might use. The features, support, stability, and extensibility are also important. So let’s compare the most popular python testing frameworks to make it easier for you to choose the right one.

Robot framework

It is still the most popular python testing framework that uses a keyword-driven testing approach and is used for acceptance testing. To run Robot you will have to install python 2.7.14 or any later version, python package manager, and a development framework like Pycharm.
Advantages

• Opensource
• Platform independent
• No need to learn a programming language to write Robot Framework test cases
• Automatic report generation after executing each built
• Supports behavior-driven, data-driven and keyword-driven approaches
• Easy installation

Disadvantages

• • Not enough support for parallel testing
  • It’s difficult to create customized HTML reports

Gauge

It is an opensource tool developed by the team that made Selenium. Gauge is immensely useful while integrating continuous testing in the CI/CD pipeline. It is gaining popularity as it supports many plugins like python runner, IDE plugins, build management, java runner, etc.
Advantages

• • • Quick defect detection
    • Easy to write test cases
    • Supports multiple programming languages
    • Command-line support
    • Supports all major plugins
    • Cross-browser tests can be automated

Disadvantages

• • • It is relatively new so it will evolve in the coming years

Pytest

Although Pytest is used for different types of testing, it is more preferred for functional and API testing. There are no prerequisites needed for Pytest, just knowledge of python will be enough to get started. It has a simple syntax which makes test execution easier.
Advantages

• • • Supports Fixtures and Classes that help in creating common test objects available throughout a module
    • It allows the use of multiple fixtures
    • It does not require a debugger
    • Xdist and other plugins support makes parallel execution easier
    • It supports parameterization, which is essential while executing the same test with different
    • configurations using a simple marker
    • Large community support

Disadvantages

• • • Test written in Pytest cannot be shared with other platforms

Pyunit

It is a unit testing framework much like Junit but for python language. Also referred to as unittest, it has five core modules. The test loader class is used to load all the test cases and test suites. The test runner shows the result of the test executed using an interface. The test suite is a collection of test cases that are clubbed logically based on the functionalities. A test case contains the actual implementation of the code and the test report contains the organized data of the test results.

Advantages

• • • No need for high-level python knowledge for test execution
    • Extensive report generation
    • Pyunit comes with Python package, no need to install any additional module
    • Simple and flexible test case execution

Disadvantages

• • • Requires boilerplate code
    • Pyunit is derived from Junit and so it still uses camelCase naming instead of snake_case naming method
    • It supports abstraction so the code intent sometimes becomes unclear

Behave

In Behave, test cases can be written in simple language and lets teams execute behavior-driven development (BDD) testing with ease. Behavior-driven development encourages quality analysts, developers, and business managers to work in collaboration to achieve higher efficiency.
Advantages

• • • Easy execution of all kind of test cases and easy coordination
    • Better clarity on the developers and testers output as the format of the spec is similar
    • Domain vocabulary that keeps the behavior consistent in the organization and the system behavior is expressed in a semi-formal language
    • Detailed reasoning and thinking promotes better product specs

Disadvantages

• • • Only for black-box testing

To sum it up

All the above-mentioned frameworks have their specialties like Pyunit is used for unit testing and Behave is good for behavior-driven testing. Although Robot framework is the best tool for a beginner to learn the nuances of automation framework. It’s always better to jot down your requirements based on their priority and then choose the right python testing framework.

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Test cases are the first step in any testing cycle and are very important for any project. If anything goes wrong at this step, the impact gets proliferated in the entire software testing process. This can be avoided if the testers use proper procedure and guidelines while creating the test case template.

In this blog, I am going to share some simple yet effective tips which you could use for writing effective test cases. These tips will save you time and effort while optimizing the use of resources.

How to write test cases in a better way

Let’s have a look a the tips to write better test case template.

1. Detailed Domain Knowledge

Domain knowledge in information technology means deep knowledge of business and operational dynamics, the risks involved and the opportunities in that particular project. It is required to follow the best practices in the domain.

2. Break long test cases into many smaller ones

It is better to break the test case into a group of smaller ones if it has too many steps. It would be easier for the developer to backtrack and repeat the test steps if an error occurs somewhere in the test script. If not done than there are high chances that the developer will miss the bug.

3. Preconditions

Before starting on the test case it is suggested confirm all the assumptions that apply to the test and the preconditions that must be met before execution. There can be data dependency or the dependencies on the test environment or any other test cases.

4. Attach Artifacts

Relevant artifacts should be attached to the test cases. This can be done using a test management tool. At the time of product delivery, It will help to track the changes in the application. I will be easy to understand the flow of the function when there is a change at any step which will not be easy to relate otherwise.

5. Test data input

While writing a new test case a tester can share test data wherever applicable to be used for the Test Case within the test case description or add with the specific test case step. This will save time as there is no need to look for the test data anywhere else.

If the values are to be verified then testers can specify the value range or describe what values are to be tested for a particular field. Choose a few values from each class which will give good coverage for your test.
It’s better not to mention the real test data value but the type of data which is required to run the test. In projects where multiple teams use the test data and it keeps changing, mentioning only the type of data will be a wise choice.

6. Organize your work

Use a test management tool to manages your test cases instead of using a spreadsheet. There are many test management tools that can be used to organize the test cases in one place which will increase the productivity of the team.

7. Stop Assuming

It is better to refer to the specification document. Assumptions about the features or functionalities can lead to disagreements between the client and the developers. This gap between the client’s requirement and the application under development will impact the business.

8. Test Case Naming Conventions

To write tests which are easy to understand, we have to stop coding on autopilot and pay attention to the naming conventions. It is required to name our test classes, fields of our test classes, test methods and the local variables while writing automated tests for our application.

It does not matter which team member wrote the test, others will know what feature is tested under what scenario without even looking at the test code.

9. Meet Customer’s Requirements

If the testers miss a bug or write test cases that do not relate to the real world scenarios then it’s just a waste of resources and time. The goal is to meet the customer’s expectations and that can be attained only if the testers think from the users perspective.

10. Cover All Verification Points

It is important to write well-defined test case verification steps covering all the verification points for the feature under test. To make sure that the test Case covers all the verification points match your test case steps with the artifacts given for your project.

11. Avoid Repetitions

Do test automation when needed as it will reduce the manual effort and save a lot of time. The test scripts should be written in such a way that they can be used afterward for some other project.

12. Make it Reusable

Create test case template which could be re-used in the future by other teams. Also, before writing a new test case for your module, find out if there are similar test cases written already for some other project. Doing this you will avoid any redundancies in your test management tools. Call the existing test case in pre-conditions or at a specific design step if there is a need for a particular test case to execute some other test case.

13. All-Inclusive Test Coverage

Test cases should include all the features and functionalities mentioned in the software requirement. Requirement traceability matrix will help in finding the untested functions of the application.

14. Group Similar Test Cases

A test run is a collection of test cases that testers should execute in a particular order. Test cases are often grouped in test runs. It’s preferred to put preconditions at the beginning of a test run rather than inserting them into each test case.

Actually, only a few of the test cases need preconditions, so the field is often left empty. A test management tool will help to customize your forms and create a test case template which will save your time and effort when writing test cases. Another thing to keep in mind is to avoid writing the same instructions several times by moving repeated preconditions to a test run.

15. Easy to Understand

The test cases should be well defined with comments where ever needed so that any other software tester can work on it in the future. Whatever project you work on, when designing test cases, you should always consider that the test cases will not always be executed by the one who designs them. Therefore, the tests should be easily understandable and to-the-point.

In a scenario where the person who wrote all those test cases leaves for some reason and you have a completely new testing team to work with, the entire effort spent during the design phase could go down the drain.

16. Test Case Description

In the description, the testers need to mention all the details about what is going to be tested, what needs to be verified, the test environment and test data.

The information mentioned below should be there in a well-written test case description:

• Test to be carried out
• Testing tools
• Test Environment Details
• Behavior being verified
• Any dependencies like preconditions and assumptions
• Test Data to be used

17. Maintenance and Update

All the test cases should be updated with the new requirements so it’s easier to execute them in the future if there is a need. Even if some other tester wants to use the test case he/she wouldn’t have to go through the details of the script.

Conclusion

The tester needs to have good domain knowledge and should write presentable test cases from the users perspective. A good test case template will make it easier for testers to write good test cases. If there are only a few test steps, consider making a checklist instead and have a look at some relevant test case examples before working on your test case. A test case example will be helpful in creating test case templates too. A test management tool will definitely help in improving the way test cases are created and managed.

Related Articles:

• Audio Interaction Testing On Remote Devices
• Selenium Testing For Effective Test Automation
• 5 Benefits of Parallel Testing
• How to Measure the Success of End to End Testing
• Mobile App Testing Strategies

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In the year 2028, there will be around 7.8 Billion mobile users which accounts for 70% of the world population. More mobile users mean more apps and more competition and to lead the competition we need to make sure that our app is flawless. If nearly half of the bugs in your mobile app are discovered by the users, your app’s ratings are going to decline and so are the downloads. This is why the right choice of mobile app testing techniques must be followed in the decision-making process.

Mobile App Testing Strategies

Today, the mobile app market is highly competitive. To be better every day and survive for long, the QA team has to follow a mix of plans that would be responsible for taking the right testing decisions. The testers have to formulate testing strategies to face every situation fearlessly and immaculately. Mobile apps have to be perfect before reaching to the end users so there have to be certain decisions to be taken regarding the testing plan. The following model of mobile app testing plans can be considered for better execution.

In the planning Stage, decisions like Selection of Device matrix, Test Infrastructure (In-house vs. Cloud, Simulator vs. Real device), Testing scope, Testing Tools, Automation (Framework/Tool) are taken. Since it is the first stage, it is the most important one as all the further stages would depend on these decisions. In the next stage which is execution and review, decisions regarding Test Case Design, Testing of user stories, testing types as per Sprint Objective, Progressive Automation, Regression Testing, Review and course correction are taken.

We are going to discuss the planning stage aspects more elaborately

Device Matrix:

It is an important factor, choosing the device as per your target audience’s behavior matters in decisions regarding resting. There are different approaches to the selection of the device matrix.

Approach 1- Selection of Devices based on market research.

Determine the set of devices with your target operating System that will have the highest occurrence of accessing your application by using app purchase data and analytics. For Example- if you support both Android and iOS, and your application will be used across millions of Samsung, Google Nexus and Moto G devices but only thousands of iPhones, you prioritize testing on the Google Nexus and Moto G above the iPhone device. So, this test plan will consist of testing on devices which are prioritized by your market analysis.

Approach 2: Categorize the devices based on Key mobile aspects

This approach highlights the categorization of the devices based on certain mobile aspects which can be considered in formulating the testing strategy. The categorization goes as:

Test infrastructure

This is another element of the planning stage. This focuses on Strategizing on the Infrastructure components like hardware, software, and network which are an integral part of test infrastructure. It ensures that the applications are managed in a controlled way.

Real device, Emulators or Mobile cloud-Where to test?

Choosing the right platform to test as per the testing needs is very important i.e whether to test on the Real device or an emulator or on the cloud

Real Devices

Testing on a real device is anytime more reliable than testing on a simulator. The results are accurate as real-time testing takes place on the device in a live environment. It carries its own disadvantages as it is a costly affair and not all the organizations are able to afford a complete real device laboratory of their own.

Pros:

Reliable- Testing on Real devices always gives you an accurate result

Live Environment- Testing on real devices enables you to test your application on the actual environment on which your target audience working on. You can test your application with different network technologies like HSPDA, UMTS, LTE, Wi-Fi, etc.

User experience- Testing on Real devices is the only way to test your Real-time User experience. It cannot be tested through Emulators or devices Available on Cloud.

Cons:
Maintaining the matrix- You cannot maintain such a huge matrix of mobile devices in your own test lab.
Maintenance- Maintaining these physical devices is a big challenge for organizations.
Network providers- There are more than 400 network providers all over the world. Covering all these network providers in their own test lab is impossible.
Locations- You cannot test how your application behaves when it is used in different locations.

Emulators

The emulator is another option to test mobile apps. These are free, open source and can be easily connected with the IDE for testing. The emulator simulates the real device environment and certain types of testing can be run on it easily. However, we cannot say that the results of emulators are as good as those of real devices. It is slower and cannot test issues like network connection, overheating, battery behavior, etc.

Pros:

Price- Mobile emulators are completely free and are provided as part of the SDK on every new OS release.

Fast- As Emulators are available on the local machine so they run faster and with less latency than Real devices connected to a local network or devices available on the cloud.

Cons:

The wrong impression- Even if you have executed all test cases on emulators, you cannot be 100 % sure it will actually work in the real environment.

Testing Gestures- Gestures like Pinching, Swipe or drag, long press using the mouse on simulators are different in using these gestures on real devices. We cannot test these functionalities on emulators.
Can’t test Network Interoperability- With the help of Simulators you cannot test your application with different network technologies. Like HSPDA, UMTS, LTE, Wi-Fi, etc.

Testing on Mobile Cloud

Mobile cloud testing can overcome the cost challenges like purchasing and maintaining mobile devices. It has all different sets of device types are available in the cloud to test, deploy and manage mobile applications. The tests run virtually with the benefit of choosing the right type device-OS combinations. Privacy, security, and dependency on the internet can be a challenge in this case but it has many benefits that can cater to different testing scenarios.

The organization can choose the right mix of above-mentioned platforms as every platform carries its own advantages and disadvantages. Sometimes a combination of real and emulators is preferred and sometimes all three can be considered as per the testing strategy.

Pros:

Devices Availability- Availability of Devices and network providers is a big gain for cloud users.
Maintenance- When you are using cloud services. Forget about maintenance. These providers take responsibility for maintaining these devices.
Pay per use- You don’t need to buy a device. You only have to pay for the duration you use that device.

Parallel Execution- You can test your complete test suite on multiple devices.

Cons:
Cost- Some providers are a bit costly

Automation Tools for Mobile App Testing on Android and iOS

Nowadays, there are so many automation tools available in the market. Some are expensive and some are freely available in the market. Every tool has its own pros and cons. Choosing the right tool for testing would reduce the QA team effort providing seamless performance at the same time. We will discuss the best mobile app testing automation tools for iOS and Android platforms in 2018.

1. Appium: It is one of the preferred MAT tools by testers. It is open source and free tool available for Android and iOS. It automates any mobile app across many languages and testing frameworks like TestNG. It supports programming languages like Java, C# and other Webdriver languages. It provides access to complete back end APIs and database of the test codes.
Top Features:
-Appium supports Safari on Ios and Other browsers on Android
-Many Webdriver compatible languages can be used such as Java, Objective-C, JavaScript to write test cases
-Support languages like Ruby, Java, PHP, Node, Python.

2. Robotium: It is a free Android UI testing tool. It supports in writing powerful black box test cases for Android Applications. It supports Android version 1.6 and above. The tests are written in Java language and basically, Robotium contains a library of unit tests. Apart from this, Robotium takes a little more effort in preparing tests, one must work with program source code to automate tests. Robotium does not have play record and screenshot function.

Top Features:
-The tests can be created with minimum knowledge of the project
-Numerous android exercises can be executed simultaneously.
-Syncronises easily with Ant or Maven to run tests.

3. Calabash: It is an open source MAT tool allowing testers to write and execute tests for Android and iOS. Its libraries enable the test codes to interact with native and hybrid apps. It supports cucumber framework which makes it understandable to non-tech staff. It can be configured for Android and Ios devices. It works well with languages like Ruby, Java, .NET, Flex and many others. It runs automated functional testing for Android and ios. It is a framework that is maintained by Xamarin and Calabash.

4. Espresso: It is a mobile app testing automation tool for Android. It allows writing precise and reliable Android UI tests. It is a tool targeted for developers who believer automated testing is an important part of CI CD process. Espresso framework is provided by the Android X Test and it provides APIs for writing UI tests to simulate user interactions on the target app. Espresso tests can run on Android 2.33 and above. Provides automatic sync of test actions with the app UI.

5. Selendroid: An open source automation framework which drives off the UI of Android native, hybrid and mobile web application. A powerful testing tool that can be used on emulators and real devices. And because it still reuses the existing infrastructure for web, you can write tests using the Selenium 2 client APIs.

6. Frank: Is an open source automation testing tool for the only iOS with combined features of cucumber and JSON. The app code needs not to be modified in this tool. It includes Symboite live app inspector tool and allows to write structured acceptance tests. It is tough to use directly on the device but is flexible for web and native apps. It can run test both on simulator and device. It shows the app in action by showing its recorded video of test runs.

Above are a few promising, popular and most commonly used and mobile app testing automation tools. Choice of tools certainly resolves many testing-related problems faster and efficiently. Implementing these tools requires skill and experience and so an organization needs to have a proper testing team in place to make all of this possible.
Related Articles:

• Challenges in Mobile App Testing
• Start to End Guide for Mobile App Testing
• Moving Beyond Traditional App Testing with AI and DevOps
• Mobile App Testing Tools to Improve App Quality – pCloudy Tutorial
• 5 Reasons Why pCloudy is The Simplest App Testing Platform
• Types of Mobile Apps: Native, Hybrid, Web and Progressive Web apps

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We are moving toward a future where everything is going to be autonomous, fast and highly efficient. To match the pace of this fast-moving ecosystem, application delivery times will have to be accelerated, but not at the cost of quality. Achieving quality at speed is imperative and therefore quality assurance gets a lot of attention. To fulfill the demands for exceptional quality and faster time to market, automation testing will assume priority. It is becoming necessary for micro, small, and medium-sized enterprises (SMEs) to automate their testing processes. But the most crucial aspect is to choose the right test automation framework. So let’s understand what a test automation framework is.

What is a Test Automation Framework?

A Mobile Testing automation framework is the scaffolding that is laid to provide an execution environment for the automation test scripts. The framework provides the user with various benefits that help them to develop, execute and report the automation test scripts efficiently. It is more like a system that was created specifically to automate our tests. In a very simple language, we can say that a framework is a constructive blend of various guidelines, coding standards, concepts, processes, practices, project hierarchies, modularity, reporting mechanism, test data injections etc. to pillar automation testing. Thus, the user can follow these guidelines while automating applications to take advantage of various productive results.

The advantages can be in different forms like the ease of scripting, scalability, modularity, understandability, process definition, re-usability, cost, maintenance etc. Thus, to be able to grab these benefits, developers are advised to use one or more of the Test Automation Framework. Moreover, the need of a single and standard Test Automation Framework arises when you have a bunch of developers working on the different modules of the same application and when we want to avoid situations where each of the developers implements his/her approach towards automation. So let’s have a look at different types of test automation frameworks.

Types of Mobile Automated Testing Frameworks

Now that we have a basic idea about Automation Frameworks, let’s check out the various types of Test Automation Frameworks available in the marketplace. There is a divergent range of Automation Frameworks available nowadays. These frameworks may differ from each other based on their support to different key factors to do automation like reusability, ease of maintenance etc.

Types of Mobile testing automation frameworks:

Module Based Testing Framework

Module-Based Testing Framework, as the name implies, depends on a number of modules to function. In order to produce the greatest results from the automation test, you would need to develop unique scripts for each module and ensure that they work together. Changes to the application’s functionality won’t have an impact on the modules. The scripts are safe unless they are manually changed.
Given that a high level of modularization is produced by merging multiple modules, this provides a cost-effective management approach. Productivity is still at its highest level. But, if necessary, it can take a lot of time and effort to make modifications to the test data individually.

Library Architecture Testing Framework

Based on the modular foundation, the library architecture framework for automated testing offers several extra advantages. Instead of separating the programme under test into the many scripts that must be executed, related jobs inside the scripts are found and afterwards grouped by function, allowing the application to be eventually divided up into common goals. The test scripts can access this library of functions anytime they are required.

Data Driven Testing Framework

A number of tests must be run while testing an automation framework before a successful result can be determined. In these situations, you might need to alter the test results to try and draw a different conclusion. You can keep the test data on an external drive and access it later for adding a new script to the test case thanks to the Data-Driven Testing Framework.

Keyword Driven Testing Framework

The keyword-driven testing framework, which is frequently regarded as an extension of the data-driven testing framework, collects your test data from an external source and securely preserves the set of codes. These codes, which are also known as “keywords,” can be used to change the test script and draw additional conclusions from the test framework. Also, these keywords effectively determine what tasks each programme performs.

Hybrid Testing Framework

To maximize the effectiveness of the aforementioned frameworks, the hybrid testing framework combines the data-driven and keyword-driven frameworks. It provides more room for more efficiency and success, making it the ideal automation foundation.

Behavior Driven Development Framework

The goal of the Behavior Driven Development framework is to build a platform that encourages active participation from all users, including developers, testers, business analysts, etc. Also, it improves cooperation on your project between the developers and testers. For this behavior-driven testing, test specifications can be written in plain, non-technical language.

Benefits of a Mobile Testing Automation Framework

Apart from the minimal manual intervention required in automation testing, there are many advantages of using a test automation framework. Some of them are listed below:

1. Faster time-to-market: Using a good test automation framework helps reduce the time-to-market of an application by allowing constant execution of test cases. Once automated, the test library execution is faster and runs longer than manual testing.
2. Earlier detection of defects: The documentation of software defects becomes considerably easier for the testing teams. It increases the overall development speed while ensuring correct functionality across areas. The earlier a defect is identified, the more cost-effective it is to resolve the issue.
3. Improved Testing efficiency: Testing takes up a significant portion of the overall development lifecycle. Even the slightest improvement of overall efficiency can make an enormous difference to the entire timeframe of the project. Although the setup time takes longer initially, automated tests eventually take up a significantly lesser amount of time. They can be run virtually unattended, leaving the results to be monitored toward the end of the process.
4. Better ROI: while the initial investment may be on the higher side, automated testing saves organizations many a lot of money. This is due to the drop in the amount of time required to run tests, which leads to a higher quality of work. This in turn decreases the necessity for fixing glitches after release, thereby reducing project costs.
5. Higher test coverage: In test automation, higher number of tests can be executed pertaining to an application. This leads to a higher test coverage, which in a manual testing approach would imply a massive team, limited heavily with their amount of time. An increased test coverage leads to testing more features and a better quality of application.
6. Reusability of automated tests: The repetitive nature of test cases in test automation helps software developers to assess program reaction, in addition to the relatively easy configuration of their setup. Automated test cases can be utilized through different approaches as they are reusable.

Top ten test automation frameworks

1. Robot Framework
Robot Framework is the best choice if you want to use a python test automation framework for your test automation efforts. The Robot Framework is Python-based, but you can also use Jython(Java) or IronPython(.NET). The Robot Framework uses a keyword-driven approach to make tests easy to create. Robot Framework can also test MongoDB, FTP, Android, Appium and more. It has many test libraries including Selenium WebDriver library and other useful tools. It has a lot of API’s to help make it as extensible as possible. The keyword approach used by Robot Framework is great for testers who are already familiar with other vendor-based, keyword-driven test tools, making the transition to open source much easier for them.

2. WebdriverIO
WebdriverIO is an automation test framework based in Node.js. It has an integrated test runner and you can run automation tests for web applications as well as native mobile apps. Also, it can run both on the WebDriver protocol and Chrome Devtools protocol, making it efficient for both Selenium Webdriver based cross-browser testing or Chromium based automation. As WebDriverIO is open source, you get a bunch of plugins for your automation needs. ‘Wdio setup wizard’ makes the setup simple and easy.

3. Citrus
Citrus is an open-source framework with which you can automate integration tests for any messaging protocol or data format. For any kind of messaging transport such as REST, HTTP, SOAP, or JMS, Citrus framework will be suited for test messaging integration. If you need to interact with a user interface and then verify a back-end process, you can integrate Citrus with Selenium. For instance, if you have to click on a “send email” button and verify on the back end that the email was received, Citrus can receive this email or the JMS communication triggered by the UI, and verify the back-end results, all in one test.

4. Cypress
Cypress is a developer-centric test automation framework that makes test-driven development (TDD) a reality for developers. Its design principle was to be able to package and bundle everything together to make the entire end-to-end testing experience pleasant and simple. Cypress has a different architecture than Selenium; while Selenium WebDriver runs remotely outside the browser, Cypress runs inside of it. This approach helps in understanding everything that happens inside and outside the browser to deliver more consistent results. It does not require you to deal with object serialization or over-the-wire protocols while giving you native access to every object. Cypress can synchronously notify you of every single thing that happens inside the browser as you’re pulling your app into it, so that you have native access to every DOM element. It also makes it easy to simply drop a debugger into your application, which in turn makes it easier to use the developer tools.

5. Selenium
One of the most popular open source test automation frameworks for web apps. Selenium also serves as a base for a lot of other testing tools as it has cross-platform and cross-browser functionality. Selenium supports a wide range of programming languages such as Java, C#, PHP, Python, Ruby, etc. It is easy to maintain as it has one of the largest online support networks. Selenium is highly extendable through a wide range of libraries and APIs to meet everyone’s needs and requirements. Selenium is preferred by testers as it is possible to write more advanced test scripts to meet various levels of complexity. It provides a playback tool for test authoring without the need to learn a specific scripting language.

6. Cucumber
It is a cross platform behavior driven development (BDD) tool which is used to write acceptance tests for web applications. Cucumber is quick and easy to set up an execution and allows reusing code in the tests. It supports languages like Python, PHP, Perl, .NET, Scala, Groovy, etc. Automation of functional validation in easily readable and understandable format. One good feature is that both specification and test documentation are uploaded in a single up-to-date document. Cucumber makes it easy for the business stakeholders, who are not familiar with testing, as they can easily read the code as test reports are written in business readable English. The code can be used together with other frameworks like Selenium, Watir, Capybara, etc.

7. Gauge
It is an open source tool agnostic test automation framework for Mac, Linux and Windows. People who work on TDD and BDD will appreciate Gauge’s focus on creating living/executable documentation. Specs – the Gauge automation tests are written using a markdown language with C#, Java and Ruby within your existing IDEs like Visual Studio and Eclipse. Gauge’s functionality can also be extended with its support of plugins. It was developed as a BYOT (Bring Your Own Tool) framework. So you can use Selenium or you can use anything else for driving your tests UI or API tests. If you want a readable non-BDD approach to automation, you should try Gauge.

8. Serenity
If you are looking for a Java-based framework that integrates with behavior-driven development (BDD) tools such as Cucumber and JBehave, Serenity might be the tool for you. It’s designed to make writing automated acceptance and regression tests easier. It also lets you keep your test scenarios at a high level while accommodating lower-level implementation details in your reports.

Serenity acts as a wrapper on top of Selenium WebDriver and BDD tools. It abstracts away much of the boilerplate code you sometimes need to write to get started which makes writing BDD and Selenium tests easier. Serenity also offers plenty of built-in functionality, such as handling running tests in parallel, WebDriver management, taking screenshots, managing state between steps, facilitating Jira integration, all without having to write a single line of code.

9. Carina
Carina is built using popular open-source solutions like Appium, TestNG and Selenium, which reduces dependence on a specific technology stack. You can test mobile applications (native, web, hybrid), WEB applications, REST services, and databases. Carina framework supports different types of databases like MySQL, SQL Server, Oracle, PostgreSQL, providing amazing experience of DAO layer implementation using MyBatis ORM framework. It supports all popular browsers and mobile devices and it reuses test automation code between IOS/Android up to 80%. API testing is based on the Freemarker template engine and it provides great flexibility in generating REST requests. Carina is cross-platform and tests may be easily executed both on Unix or Windows OS.

10. EarlGray
Developers often face difficulty with some of the existing test automation framework in synchronization of the app and the instrumentation. Also, executing tests on apps as synchronized and advanced only when UI elements are visible on the screen has caused issues for many developers. Google EarlGrey has built-in synchronization that makes test scripts wait for UI events to occur before the script tries to interact with the UI of the app. This type of implementation makes the test script concise as all steps of the test script shows how the test will proceed and UI gets synchronized with it. One more key aspect of EarlGrey is that all actions on UI elements happen only on visible elements. This provides a fast and robust approach to ensure UI testing goes through as clicks, gestures and other user interactions do not get done if the UI element is not fully shown.

In a nutshell

This list of top tools here represents the best tools that are mature, popular, and provide test automation capabilities using AI/ML to address the challenges that organizations are now facing to deliver Quality at Speed. This list also includes the tools that provide API and services testing which is essential for successful DevOps transformation. The emerging technologies like AI, codeless, big data and IoT testing, are making test automation more efficient while creating opportunities for the existing tools and new players to assert value to the testing communities.

The choice of automation tools should not only meet your current needs but should also focus on potential trends and improvements. An efficient test automation tool should support basic optimization, data generation, smarter solutions, and analytics. As of now, the level of test automation in organizations is low at between 14% and 18%. But organizations are working towards increasing the automation coverage upto 80%. API and services testing is also a trend that should see further development in the future.

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Today, there are many smartphone users in the world and so is the popularity of mobile apps. In order to be competent enough, mobile apps have to be unique and should provide the best user experience to increase the user base. With the users getting more informed and intelligent, the apps built should keep up with the pace. In order to be impeccable, the mobile app should undergo a rigorous testing process and during that process, the testing team faces many challenges in this aspect which will be covered in this blog. But before we dive in, let’s look at the different types of apps that are available in the market.

Types of mobile applications

The creation of mobile applications is a fantastic approach to boost brand recognition, attract new clients, and improve the user experience for existing customers. In light of this, let’s examine the three primary categories of mobile apps: native, web, and hybrid.

Native apps:

Native mobile applications are ones created exclusively for a given operating system. As a result, software created for one System cannot be used on another, and vice versa. Native applications are more effective, quick, and offer greater phone-specific functionality. Thus, the difficulties of testing mobile apps for compatibility with native user interfaces of devices involve ensuring that such traits are preserved strictly.

Web apps:

Similar to native apps, web applications do not require users to download them. Instead, the users’ web browsers on their phones can access these apps because they are incorporated within the website. So, it is envisaged that web applications will operate flawlessly across all platforms. Testing teams must carefully examine the application on a wide range of real devices and browsers to ensure high app quality. Yet in addition to taking a lot of time, this operation is essential because failing to work on a few devices can severely reduce the app quality and incur heavy losses when the app doesn’t function as required.

Hybrid apps:

The features of both online and native apps are available in hybrid apps. These are essentially web applications that mimic native apps in design. These applications are easy to maintain and load quickly. Teams that test mobile apps are in charge of making sure hybrid applications don’t lag on some devices. Any operating systems with the capacity to support the said features have access to all their functionality.

While each of these app types are slightly similar to each other the technical teams face a different challenge with each type of mobile application. Combining these challenges greatly increases the complexity, making the entire procedure laborious and time-consuming. Let’s quickly look into what some of these challenges are?

Different Operating Systems and their versions

There are different types of operating systems available in the market such as iOS, Android, Windows etc. Also, these OS have different versions too. So, it becomes challenging to test so many versions of the mobile app in a shorter period of time. One app that works well in one type of OS may not work well in the other. It is very important to test the application with all supported platforms and their version because we don’t know where the user is going to install the application. As per research, iOS users upgrade quickly as compared to Android but in Android the device fragmentation is larger. That means the developers have to support older versions and APIs and testers also have to test accordingly.

Device Variations: Based on Screen size

Android comes with a mix of features and variations in pixels densities and ratios which varies in each screen size. Even in the case of Apple, the screen new size was introduced with the launch of the iPhone 6. Now, it is not just about being picture perfect screen design rather designing an adaptive screen design. Well with such a variety in screen sizes, the role of the tester becomes serious as they need to check if all the features are working well in different screens and pixel and aspect ratios are maintained well.

Based on the number of Devices

The picture below shows the number of devices in the market by different brands. The number of device manufacturers has increased. According to OpenSignal, there are around 1294 distinct Android phone manufacturers alone, imagine if we add up other brands. The pace with which this data is increasing is a bit alarming for the testers as the testers have to check the app performances on different devices, they would probably need a device library to do the same. The challenge remains in context to functionalities like Complex user interactions on touch screen and keypad devices as well. Having a device library is certainly is a costly affair unless emulation is adopted which can simulate multiple device types and testing can run easily on it.

Image Source: venturebeat.com

Various Networks

The QA team also faces challenges when it has to test the devices connected to different networks. Generally, there are 2G, 3G,4G mobile data available. These provide different data transfer speed and transmission. These varying speeds of the networks by various providers remain a challenge for the testers even today. In this case, testers have to check that the app must perform well at different network speeds and connectivity quality and a check on bandwidth usage of the app. This remains a challenge as it is partially controllable based on different network providers and connectivity access in different geographies.

Frequent OS releases

Mobile Operating Systems keeps changing. Both Android and iOS have more than 10 versions of their operating systems. They keep enhancing and updating their versions for better performance and user experiences. This frequent OS release comes as a testing challenge as the testers needs to validate the complete application with each of new OS release. It is very important to test the application with the latest OS release otherwise the app performance would be a major issue and consequently loss of users using the app.

Script Execution

Another major challenge of mobile testing is what we call scripting, the method of defining a test. Script execution can either be manual or automated. You can write down the scripts in a document, which is then used by a test engineer who manually interacts with the test environment to determine the result, else you can run automated scripts that in turn drive interaction with the device and app, and record the results.

Automated scripting needs to be kept away from the device to be of any real use because there are so many different devices with different interface options. A script that follows strict keystrokes on an Apple iPhone would not have any chance of working on a Samsung device, because the UI is different. Fortunately, most real device automated testing software provides high-level scripting that operates on the text, image, or object layer. Device emulators can automate testexecution using a higher-level, abstracted scripting language that is not device dependent. When you use automated scripting, the cost of setting up the script will typically be higher than the cost of a single manual execution of a test. But if it is a test script that you run on a periodic basis, every time that you subsequently run the script, the more time and effort you will save. You will eventually recover the cost of initial scripting If you run the script enough.

So to conclude, to build a better user experience, an app tester needs to work had in overcoming the challenges of testing. By adopting some analytical skills and methods, testers can really cope up with these situations. For eg. Testing only those apps and OS which are mostly used by their user segment, by adopting a strong testing strategy to take situational decisions eg. Decisions regarding when to choose Automation and manual testing. Strategically, the challenges can be overcome.

Screen Size

The Android world is not simple. The variety of different aspect ratios and pixel densities can be overwhelming. With the launch of iPhone Xs Max which has a screen size of 6.5 inches, Apple brings new screen sizes to the iOS world as well. Though iOS developers are used to pixel perfect screen design, they now need to change their mindset to the adaptive screen design instead. For testing, it means that we need to check on various devices that all the necessary screen elements are accessible with different screen sizes and aspect ratios. There are many phones with a screen size of 5 inches which are still popular.

Security Issues

Traditional testing tools like selenium and QTP weren’t designed with cross-platform in mind. Automation tools for web apps and mobile apps are different. Operating systems especially Android further adds to the complexity with API level fragmentation. The most common automation testing tools for mobile app automation testing are Appium and calabash. Each tool has it’s own advantages and disadvantages and you need to choose on the basis for your app’s functioning.

Weak Hosting Controls is one of the most common issues. The server on which your app is hosted should have security measures to prevent unauthorized users Weak Encryptions can lead to data theft which will impact the trust factor of the users. Most of the mobile apps require user data such as email ID, password, age, location etc. This data should be encrypted and stored with proper security. Hackers often use this kind of data to get money out of users account online. Encryption will make it difficult for anyone unauthorized to intrude and retrieve that data rather than keeping it in plain text.

Power consumption and battery life

We haven’t seen much innovations in the mobile battery but the mobile usage and specifications are increasing rapidly. People are using more apps nowadays and the apps are more complex than ever. This is why testers need to test the apps power consumption because if the apps use lots of CPU cycles and some apps will also run in the background than the battery will drain out quickly. We need to make sure that the app uses less battery power so that users can use it for a longer period of time.

Conclusion

Mobile apps are evolving with device technology and user expectations. Developers are emphasizing on reducing the app size and battery usage. Testers play a major role to ensure that the app works smoothly and does not crash or have bugs. This is why testers must be aware of the latest trends in mobile app testing to deal with the mobile app testing challenges.

Related Articles:

• Key Challenges in Testing Mobile Applications
• Mobile App Testing Strategies
• 6 Most Common Problems in Mobile App Testing
• 5 Best Practices App Testing Companies Must Follow for Success
• Cross Platform Mobile Test Automation Using Appium

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Appium Mobile Test Automation

As we all know that Appium is the most preferred test automation tool for mobile applications. It is the first choice of the testers because of its flexibility i.e. it is open source, it has the best supported and highly active community of experts, Appium works across different platforms and works well with different scripting languages. Even after gaining such popularity and having a strong community base, surprisingly, the users still make mistakes while running the mobile test automation with Appium.

Here are a few common mistakes that Appium users encounter while using Appium Mobile Test Automation Tool:

1.Unrestricted X Path Usage:

Over usage of X Path could be found in case of Selenium as well but when it comes to Appium world, it has more outrageous effects because X Path is a more dynamic way to unearth the elements. But the biggest stumbling block in this scenario is its huge performance cost due to which it becomes elusive an area. This is because Google and Apple do not provide XML or XPath related queries in a way we would need them to be. All of this hospitalizes an unmanageable cost finding elements using X Path. Undoubtedly, X Path is the most trusted method but there are several other better locator methods like ‘Accessibility IDs’ that can be used in this sitch.

2. Neglected usage of Accessibility IDs:

The accessibility ID locator strategy is formulated to read a unique identifier for a UI element. For both iOS and Android, getting an element by their accessibility ID is the best method. Most importantly, it is preferable because it is quicker. It should be noted that semantically accessible IDs are different than the Web IDs. However, the two shouldn’t be combined. In many cases, the accessibility IDs are used only for the purpose of testing even though they have a larger purpose. So, in order not to spoil the accessibility of the applications just for the sake of testing, the bigger purpose of the accessibility IDs should be known. If the accessible IDs are set up on the elements to make the apps testable, the app accessibility also increases, provided that the accessibility labels are sensible to the users using them. But the foremost criteria not to make automation efforts a failure is to make the application testable in the first place.

3. Not making a testable App:

Developing an app should be a forecasted move where in the developers, even before writing the first line of code, plan to develop an app keeping the automation in mind. This they can achieve by keeping in mind the hooks and the unique IDs for the elements in order to make the app more testable. This strategic approach would be a reason for a successful mobile app test automation. Apart from this, there should also be a concentration on the different testing scenarios in order to elude the chances of overlapping before even getting into Appium coding. An open discussion forum with the development teams to discuss the plotting of right accessibility IDs, labels or Unique IDs for the application’s elements would reduce many test automation reliability concerns.

4. Disregarding Application View States:

One of the challenge faced in Mobile Test Automation is not setting up the application. Most of the developers do not set up the application in order to access specific views and user states rapidly. To quote an example given by Jonathan Lipps,one of the key contributors to the Appium project.

A shopping cart functionality of the app might have ten different tests, and ninety percent of the tests might go through the same steps of logging in and searching items to be put in the cart which is a huge waste of time.

So, your team should be able to set up an app’s state and start the test in that apposite state, straightaway. This is highlighted in Appium scenario due to the fact that the mobile simulators and emulators are slow and take longer than usual to reach to the right position on the test.

5. Query for every element visibility:

Not querying for the visibility of every element is another way to speed up the run-time of the Appium test scripts. This leads to an additional load of calls and waiting time for Appium while in the every activity of retrieving an element. The lag can be reduced by only requesting the element attributes which are important in perspective of the test code.

6. Native Testing tools – Always better?

According to some developers, usage of the native testing tools is the best way to get unfailing mobile tests. For example- Espresso in case of Android and XCUITest in case of iOS. This is not good advice as neither of Google or Apple are going to release and new automation technology. When the question is of stability, the stability of code should be chosen rather than technology and in this scenario, Appium is the best choice!

As an exception, if the development team writes the tests and it’s most comfortable in mobile SDK language, using Google and Apple providers to build development environments. Also, when the constricted test-app or test code-app code integration is needed, then Appium might not be of much help. The utmost value is that Appium provides the WebDriver layer on top of the technology, which means the code can be written in any language and acts as a stable interface to that specific automation technology. Also, being a cross-platform tool, Appium allows to saves a lot of code and architecture in case of testing both iOS and Android devices.

7. Appium is slow:

Appium might be slower in some circumstances and also there are points in Appium codes where it does not seem efficient. Appium backs upon technologies that are slower than Appium and the curators of Appium have strategically chosen to utilize slower strategies for specific instances. For eg: Appium will certainly be slower if you are relying on XPath. The efficiency of the tool depends on how it is being used. Mostly, Appium is favored because of stability than the speed.

8. Not Using Appium Documentation:

The earlier Appium docs were not very user friendly. As a result, they weren’t used as much as they needed to be but the new Appium documents have been completely redesigned and reorganized. Information about the API reference to Appium, client libraries, supported drivers and Appium commands, code examples which was not provided before is all documented in the updated version. It deserves a revisit due to its newness and can be accessed at Appium.io.

9. Not creating reusable code

Repetitive or duplicate code can cause several issues for Appium mobile testing process. When the same code is repeated in several test scripts, any updates or modifications must be copied across all instances. This increases the chance of failures and complicates the debugging procedure. It also increases the maintenance cost for the organization.

Duplicate code makes identifying the primary reason for a test failure challenging. It can be difficult to pinpoint which occurrence of the code caused a test to fail if the same code is used repeatedly throughout several test scripts. This could increase the duration required for debugging the software and expand the market time.

Therefore, it’s crucial to use appropriate coding principles to prevent these issues. This includes creating simple reusable code, utilizing libraries and frameworks, and modifying code to remove redundancies.

10. Ignoring the Test-driven Deployment

Test-driven deployment is an Appium mobile test automation approach that stresses developing automated test cases before writing application code.

It lets the developer test all the application features before being deployed. This way, they can detect and resolve any bug in the early stage. This saves not only the organization’s time but also valuable resources.

Not just this, Test-driven deployment also helps developers in developing more reliable and enduring codes. By allowing them to understand the needs and provide a solution accordingly. Which lets them create more modular, maintainable, and error-free codes.

11. Choosing the right tool for API testing

APIs may have several endpoints, each with a unique set of parameters, methods, and authentication requirements. Writing and maintaining tests that accurately reflect the behavior of the API can be challenging due to this complexity.

Another difficulty is the requirement to simulate various scenarios and inputs. Testing for different response codes, error messages, and payloads might be part of this. To ensure the API can manage a high volume of queries, developers might also need to simulate load and stress testing.

Developers use a testing framework or library like RestAssured and Postman, which provides integrated support for API testing. These tools streamline and simplify the testing process by providing pre-build methods for common API testing scenarios, these tools streamline and simplifies the testing process. Alternatively, they can mimic various scenarios and inputs using mock data or a staging environment.

Developers can test their API in a safe setting before releasing it for production. Moreover, they can evaluate the API’s capacity by simulating heavy requests using load-testing tools like JMeter or LoadRunner.

12. Need to follow a good design pattern.

In Appium mobile test automation, the Page Object Model (POM) and the Page Automation Layer Model (PALM) are popular architectural patterns. Both techniques increase automated tests’ maintainability, scalability, and reusability.

POM is a design pattern that emphasizes the creation of reusable and modular code by isolating the application’s user interface from the test automation code, making it simpler to update tests when UI changes are made. POM builds an object repository that holds all the web elements and methods.
PALM is another design pattern that builds upon the principles of POM. But unlike POM, which focuses on UI elements, PALM emphasizes creating an abstraction layer between the test automation code and the application’s business logic. This approach distinguishes the test automation code from the underlying implementation. Thus making it easier to modify the test code without affecting the business logic.

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