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.
Technology and innovation are two sides of the same coin. Technology is the power that lets the mankind turn ideas into reality within no time. At pCloudy, the power of innovation has led us to stay aligned and be in pace with the dynamics of the mobile app industry. We cannot afford to let our customers stay deprived of any technological advancements happening in mobile app testing. Once again we are back with another new feature called QR code scanner and we are proud to announce that it is already launched on our platform.
Nowadays many mobile applications use QR code scanners for a product or user identification. The QR Code scanning feature in pCloudy enables users to test the QR code feature through mobile applications on multiple devices present in pCloudy. This feature ensures the smooth functioning of the QR code function present in the mobile application. To use this feature, all you need to do is upload the mobile app under test, a QR code image file, and with the help of some simple steps, you can smoothly perform testing of the QR code scanning function of your mobile application.
The app will scan the injected QR code. You can scan multiple QR codes while testing your app on different mobile devices. So from now on you can easily perform QR code testing in pCloudy. This feature is supported on Android 5.0 devices and later versions. Supported QR Code file formats are .png,.jpeg and jpg.
Android uses a centralized system for all logs and application programmers can also write custom log messages. The tooling to develop Android applications allows you to define filters for the log statements you are interested in. Log analysis is a phase of development and developers encounter it from time to time. Bug log helps in identifying the bugs in the Android application while in the development phase. Once the app is released into the market, the support engineers analyze the bug logs to resolve the issue. There are for different types of Logs available in the Android Eco-System – Application Logs, Android System Logs, Event Logs & Radio Logs.
The logging system consists of a kernel driver and kernel buffers for storing Android log messages, C, C++ and Java classes for making log entries and for accessing the log messages, a standalone program for viewing log messages (logcat) and the ability to view and filter the log messages from the host machine (via eclipse or ddms).
There are four different log buffers in the Linux kernel, which provide logging for different parts of the system. Access to the different buffers is via device nodes in the file system, in /dev/log. The four Android log buffers are main, events, radio and system. The main log is for the application, events is for system event information, radio is for phone related information and system is low level system messages and debugging.
Each message in the log consists of a tag indicating the part of the system or application that the message came from, a timestamp (at what time this message came), the message log level (or priority of the event represented by the message) and the log message itself (detail description of error or exception or information etc).
What Each Android Log Type Contains:
1. Application log
use android.util.Log class methods to write messages of different priority into the log
Java classes declare their tag statically as a string, which they pass to the log method
The log method used indicates the message “severity” (or log level)
Messages can be filtered by tag or priority when the logs are processed by retrieval tools (logcat)
2. System log
Use the android.util.Slog class to write message with different priority with its associated messages
Many Android framework classes utilize the system log to keep their messages separate from (possibly noisy) application log messages
A formatted message is delivered through the C/C++ library down to the kernel driver, which stores the message in the appropriate buffer(system buffer)
3. Event log
Event logs messages are created using android.util.EventLog class, which create binary-formatted log messages.
Log entries consist of binary tag codes, followed by binary parameters.
The message tag codes are stored on the system at: /system/etc/event-log-tags.
Each message has the string for the log message, as well as codes indicating the values associated with (stored with) that entry.
4. Radio log
Used for radio and phone (modem) related information
Log entries consist of binary tags code and message for Network info
Logging system automatically routes messages with specific tags into the radio buffer
Device Logs on pCloudy Platform.
Device Logs Filter Options
Log format on Android
A common log format in android:
tv_sectv_nsec priority pidtid tag messageLen Message
tag: log tag
tv_sec&tv_nsec: the timestamp of the log messages
pid: the process id of where log messages come from
tid: the thread id
Priority value is one of the following character values, ordered from lowest to highest priority:
V — Verbose (lowest priority)*
D — Debug*
I — Info*
W — Warning*
E — Error*
F — Fatal*
S — Silent (highest priority, on which nothing is ever printed)
Log-File locations
There are several directories where Android log (including those from crashes) stores and it are not standardized (i.e. some may be ROM-specific). I am putting some of common here.
/data/anr : Dalvik writes stack traces here on ANR, i.e. “Application Not Responding” aka “Force-Close”
/data/dontpanic : contains some crash logs including traces
/data/kernelpanics :- Stores “kernel panic” related logs
/data/tombstones :- may hold several tombstone_nn files (nn is a number from 0 to 10 and after 10 again repeat it)
‘Log’ command line tool
To capture Android log from the devices/emulator Below is the some command line tool. In real life project there are log capture application/tool used to capture the logs on user device and shared it back to Developer/maintainer for analysis .
adb logcat (shows all type logs for current android system
adb logcat -v threadtime (it will include date and time)
adb logcat -v threadtime> logfile.txt (Store logs in logfile.txt)
Useful filter patterns
You can use below filter in your adb command to filter logs. You can also use this filter to search your logs file(Android logs provided by user device).
adb logcat -f Save all logs into a file
adb logcat “*:E” Get all errors and fatals
adb logcat | grep -i “foo.example.” #get all logs related to “foo.example.*” tagname
adb logcat “application_or_tag_name:*” “*:S” Get all logs by application name
adb logcat -b events “gsm_service_state_change” “*:S” Get all GSM state changes
adb logcat -b radio Get all Radio events
Log Analysis
In this blog we got the basic understanding of the Android Log System. While analyzing the Android logs from your app you can divide it in two parts, debug Logs – file coming during development and testing phase and production Log – file coming directly from end user. The best way to do it is by using useful Filter Patterns and by using Some Tool like GoogleLogTool and SonyLogTool.
We all understand the importance of software testing and how it transforms the goodwill of the business by enabling delivery of quality product to its clients in shorter delivery cycles. It becomes challenging to run the test cases manually by evaluating the quality of each line of code at every step of the continuous Delivery process. This is where Continuous testing in DevOps comes into picture.
Continuous testing in DevOps Pipeline
In traditional ways of testing the software used to pass through different development and QA phases which took more time until the final delivery of the product. According to a research by Gartner, Continuous testing in DevOps is aimed at providing early and quick detection of signs of risks related to the product release. DevOps Continuous Testing is an inevitable activity of the delivery process rather than just a mere stage in the delivery process. The main purpose is to inculcate quality into the CI/CD pipeline by utilizing the key benefits of continuous testing in DevOps.
DevOps Continuous testing is a critical aspect responsible for seamless Continuous Delivery. It involves the usage of agile development methods and processes into the QA process further providing a productive testing process.
Here are a few points to understand the benefits of Continuous testing in DevOps at the deeper levels:
a. Provides Sustained Risk Analysis:
There has never been any code build that is error free and in a ready-to-release form. Even if the final release candidate has come without any glitches, passes all the tests, it has to be ‘prepared well’ for the final release and approved by the business leaders. Continuous Testing evaluates the code at a deeper level to estimate all the possible risks connected to it so that a corrective action could be taken at the right time without breaking the ‘continuous delivery’ chain.
b. Cuts down the feedback cycle:
The key benefit of continuous testing is that it evaluates the code layer by layer at each stage of delivery pipeline, allowing testers to understand the source of the problem. The actionable insights helps the QA team to act on time and avoid longer queues
c. Broader Test Coverage:
A broad range of tests can be applied throughout the testing process with the help of continuous testing tools for DevOps such as Selenium, etc. It covers both functional and non-functional testing types to increase test coverage by emulating testing like cross-browser testing, API testing, regression testing, integrated testing, unit testing and non-functional testing like security, reliability, scalability, usability and many more.
d. Delivering High Quality Product:
‘Test-early-test-often’ is the key mantra of continuous testing which is self explicable. In order to ensure delivery of high-quality product to the end user, there needs to be a process to continuously monitor the progress along the entire delivery pipeline. Achieving high product quality is the result of continuous testing that strives to finding and addressing risks effectively and by gaining feedback at the early phases of software development lifecycle.
e. Faster Software Delivery:
The whole point of introducing Continuous Testing in DevOps is to speed up the product delivery cycle. Following a multi-point testing at different stages allows the QA team to detect the glitches early and take quick corrective actions so that the final delivery is not impacted.
f. Easy Integration with the DevOps Process:
Continuous testing seamlessly integrated with the DevOps process right from the early stages of the development process rather than functioning just before the release. Continuous integration into the software pipeline enables quick fixes so that the development process can be aligned with the business requirements on time.
Tools for continuous testing in DevOps
For a seamless experience in the entire CI CD process, Continuous testing has to be supported by the best tools for continuous testing in DevOps. For performing smooth Continuous Testing, the Automation Framework has to be integrated with the CI tool, version control and various automated continuous testing DevOps tools to execute different types of testing at various stages of the process. For example, :
Using Selenium for performing functional testing
Load runner for performing load testing at its best,
Secure code analysis using Fortify and static code analysis using Sonar, etc.
Apart from these there are many other trusted DevOps Continuous testing tools available in the market for an efficient continuous testing, like JIRA, Jenkins, Bamboo, Docker, Appium, SoapUI, PagerDuty, CodeCluster etc. The concern is that not all the tools support comprehensive automation solution. Some organizations prefer to create self-made automation frameworks depending upon the version control used to enable complete automation of the CD pipeline.
Since Continuous testing is the most critical aspect to be covered by the business in order to achieve quality product release, it has to be backed by the choice of right, suitable tools and frameworks to achieve speedy and quality delivery.
Challenges in DevOps Continuous Testing and how to overcome them
Even after understanding the advantages of adopting continuous testing in the DevOps process, many organizations face the challenges of successful implementation of it. Establishment of an efficient automation framework is a daunting deal as it involves a huge investment, expertise and effort. Moreover, the organizations are not equipped with the scalable infrastructure to run tests continuously. Also, without a strong coordination among the product, development and testing team, this seems even tougher. Although there are challenges in successful adoption of Continuous testing, with a little sensibility one can overcome it. Here are a few of the key points to comply in order to overcome the challenges :
By proper team engagement, the cultural and communication barriers can be dealt with enabling the SDLC team to have better coordination, collaboration and understanding of the scenarios.
Reducing unnecessary and extensive testing plans, the focus should be on keeping it simple and logical. Instead, the energy could be diverted to more important test objects in order to create much better measurable results.
Automation would ease the successful implementation of continuous testing in DevOps. Well, complete automation isn’t achievable but more the process automation, easier it becomes to carry on the continuous testing.
Carry out testing at all stages of production and QA environment would continuously providing feedback to developers to improvise on the product quality.
Last but not the least, implementing artificial intelligence and other intelligence programs for problem solving and to think beyond automation at every stage of delivery would boost up the SDLC.
By now we all know how significant continuous testing in DevOps for any business. It essentially brings together all the stages of designing, developing and deploying the software. Merely providing a software product does not help but providing best products faster than the competitors would be the key differentiator of your business. If practiced earnestly, it provides consistent insights about the software development to ensure speedy delivery.
Some testing teams set up their own staging environment to test internal servers but as there is no public access which makes it difficult to perform local testing on remote devices. So how would you test your app which can be accessed through your internal network? We launched a feature called Wildnet, that enables you to test private or internal servers using pCloudy. You can test your privately hosted apps on any Android or iOS devices and implement continuous testing to ensure reduced release cycle.
Some times testers want to test their internal servers to protecting unsecured data and credentials, hide new features, and general security. But if an app hasn’t been made public, it should not delay testing across environments. When the Wildnet feature is enabled, a secure connection is created between your machine and pCloudy remote devices so you can perform testing even if the devices are connected remotely.
Let’s see how you can test local or private servers using Wildnet.
Prerequisite:
User should be registered on pCloudy platform
Executables can be downloaded from below links
Once “Wildnet” is enabled, the user can connect more than one device.
User will get authenticated as shown in the below screenshot
This is how users can initiate Wildnet.
2. Go to the Device page in pCloudy platform, connect the device and click on Enable Wildnet as shown in the screenshot:
Open any local URL that you wish to test and enjoy local testing on pCloudy platform.
3. To disable the service simply run ctrl+c to exit
Activating Wildnet for Appium Automation
1. Run the below mentioned executable command on the terminal:
For Windows:
.\pCloudyWildNetWindows -i Emailadress –k accesskey -u URL
For Mac:
./pCloudyWildNetOSX -i Emailadress –k accesskey -u URL
For Linux
./pCloudyWildNetLinux -i Emailadress –k accesskey -u URL
3. Once the capability is added, users can perform local testing on pCloudy devices and verify the execution in live view page.
The most important advantage of Wildnet is that you can be confident about the application as you can rectify all the defects prior to delivery. Try it yourself. Use Wildnet to find more bugs and defects in your internal server before deploying it to production.
As the world battles with turbulent, uncertain times, most of the workforce across the globe is working remotely. Organizations have acknowledged the importance of remote working as it helps in maintaining business continuity. But in some scenarios, it is difficult to maintain business continuity or distribute resources within the teams while the team is working remotely.
For instance, if you have some physical device infrastructure to test your app on multiple mobile devices, how would you do it? How would you share the devices with other testers and developers in your team working from different locations? Most importantly, how will you make sure that the app works smoothly on all the popular devices? We will address these issues in this blog, so buckle up for some interesting insights into the remote testing advantages that can ensure a better app experience for your users.
1. Abate device fragmentation and ensure better app compatibility with remote testing
Device fragmentation is any testers Achilles heel as it limits their potential of extensive testing. Testing from a physical device lab at this global lockdown situation is not feasible, and testing on a few devices won’t yield good results. But this issue can be rectified by testing on a device cloud. In pCloudy, users can test on multiple devices based on the popularity of devices in a particular region and its penetration to get the optimum device coverage.
Both manual and automation testing can be performed with unlimited parallel test runs remotely on hundreds of real devices. This is also convenient for globally distributed teams, as the users won’t have to wait for the devices to be available for testing.
2. Deliver Better Quality App with Rapid Automation
Enterprises can ensure better quality apps without missing out on any deliveries by leveraging remote devices for automation testing. pCloudy helps in speeding up automation testing with codeless scripting and test orchestration using integrated tools like Jenkins. Capability configurator is a feature in pCloudy that generates the desired capability based on a set of filters, which saves time and effort while performing test automation. Integration with popular automation and collaboration tools like Appium, Espresso, Jira, etc., makes it convenient for users to perform automated testing on remote devices.
3. Better collaboration and continuous feedback
In pCloudy, users can manage teams and distribute credits among themselves. The user management feature allows managers to become the system administrator and create teams to allocate the credits to the members according to the task assigned. This helps in user and task as the hierarchy is maintained to distribute workload systematically.
Once the tests are complete, detailed test reports are generated automatically, which can be easily shared across the team. The progressive reports also show the tests failed, passed, and those with errors. This helps in focusing only on the tests that failed and doing a root cause analysis to rectify the issues. Continuous access to a range of devices available for remote testing will provide stability to your CI/CD pipeline.
4. Assured data privacy and security
Enterprise-grade security gives assurance to our users that their data is safe on the cloud platform. Our data centers comply with internationally recognized security standards like ISO27001, SOC2, and SSAE-16. Keeping your security issues in concern, we have another useful feature called Wildnet. This feature enables you to test your internal sites or apps on your local network, keeping all your data and information secure.
5. Advanced features to improve manual testing
Take advantage of next-gen features like Certifaya, an AI-powered autonomous testing bot to save time and effort. FollowMe is another feature that enables the user to run a test on multiple devices in parallel. This will save your resources while reducing the testing time by multifold. Apart from this, there are many features in pCloudy, like taking screenshots, recording the test video, cross-browser testing, etc. that will make manual app testing a piece of cake.
In a Nutshell
Remote testing is convenient, and it will help you save big bucks while you deliver a better quality app in less time. Continuous access to numerous devices helps in accelerating automation testing, as the app can be tested on multiple devices in parallel. All these advantages of remote testing make it the optimum choice for enterprises.
Remote working is going to be a new normal and remote access tools and platforms will play a key role in maintaining productivity. Working from home has many advantages both for enterprises and teams. There are many tools that help teams to collaborate and share all the information conveniently over the net. pCloudy is one such tool that gives you remote access to hundreds of real devices to test your app.
So let’s look at the 10 ways how pCloudy could help in maintaining business continuity while working remotely.
1. Remote access to devices: Test your apps from home by accessing remote devices on pCloudy. You don’t have to worry about touching or managing the devices.
2. Device access to geographically distributed teams: Since testing professionals are working remotely, the problem is sharing the limited number of devices for app testing among the team members. With pCloudy offering a range of devices over cloud, members across different locations can access hundreds of real devices on the cloud for their app testing needs.
3. All in one: Manual and automation testing can be performed remotely on real devices. The platform is well suited for continuous testing and optimizes your DevOps process.
4. Autonomous testing bot: use our AI power autonomous testing bot to minimize the effort and time spent on testing.
5. Team management: Manage platform access to the team members and share detailed app test reports.
6. Parallel testing: Run automation tests in parallel on multiple devices to increase the testing efficiency.
7. Latest devices: No need to wait for the delayed shipments of the new devices as all the latest devices are available on pCloudy.
8. Fully integrated: pCloudy is integrated with most of the popular tools and frameworks used for task management, version control, test automation, etc.
9. Device tunnel: Developers can use features like Device tunnel to access devices in their Dev environments like Android Studio or Xcode
10. Quality support: Achieve quality@speed in mobile app testing while working from home. Our technical support team will be available to help you 24/7.
11. Device management: Hassle-free app testing for our users and our cloud infra team will take care of the continuous availability and maintenance of the devices.
It has been speculated that around 30% of the service sector workforce across the globe will be working from home by 2030. You can stay ahead of others in this initiative and adopt flexible work preferences. Contact us if you need more information on how pCloudy could help in enhancing productivity while working from home.
Automation testing forms the core of any CI/CD pipeline and enterprises are keen to practice test automation to enhance the efficiency of the development process. Test automation saves resources and reduces the cost of any project in the long run. But there are some important points to keep in mind while testing to avoid automation failure. Let’s have a look at these salient points.
Leverage Parallel Execution
Once you are done automating the test cases, the challenge will be the complex test suites taking a long time to get executed. It affects the quality of the test queue in the test automation framework or IDE. This leads to queue timeout issues and test cases being halted abruptly due to the sequential execution of the test cases.
Parallel execution in different test environments is preferred over sequential execution as it saves a lot of time. Although in automated testing, unintended code interactions could happen. This is why you need a thorough reporting mechanism to debug the causes of test failure.
Pick The Right Tools
Choosing the right tool for test automation is critical to the success of automation testing. There has to be a set of clear requirements/parameters on the basis of which the tools have to be selected. Some important points that are to be kept in mind while selecting the tool are:
The team should be clear about the test tool requirements.
The testing requirements of the application under test (AUT) should be analyzed thoroughly.
The team’s skill set should be accessed accurately.
The cost-benefit analysis should be performed to calculate the return on investment.
Tool vendor and capability should be evaluated as technical support might be required while using the tools.
One tool might not be enough to meet any organization’s automation needs. Also, test automation engineers have to be a part of the tool evaluation process so that they can help in selecting the right set of tools. For example, you can use Appium for test automation but you need pCloudy to perform automation testing on multiple real devices in parallel.
Analyze The Test Reports
Test reports provide insights into the underlying issues that are to be resolved. A detailed test report gives an idea of the efficiency of the test automation and the automation team can analyze the report to look for the scope of improvement. While selecting an automation tool you need to make sure that the tool generates test reports to be analyzed by the test automation engineer. There will always be some tests that will fail to execute and it is necessary to analyze the test report to get an understanding of the scenario.
Test Automation Metrics
Test automation metrics will help you gauge the quality of the tests performed based on some essential parameters like test duration, unit test coverage, path coverage, number of defects found, percentage of broken builds, etc. The test metrics will give you a clear picture of how well the code is tested. In an agile process, there are frequent iterations to the builds and it becomes important to track the quality of each build. With test automation metrics you can figure out what is the percentage of your tests that passed and what was the reason behind the failed tests.
Optimum Device Coverage
Test automation is effective when the tests are executed on multiple devices in parallel. Device coverage is the most prevalent challenge as we have witnessed growing device fragmentation across the world. To ensure the smooth functioning of mobile apps on all the devices, you need to perform automation testing on hundreds of device-OS combinations.
Test automation should be designed to make the app compatible with most of the popular devices. The frequent release of new versions of OS from both Android and iOS is a major factor that drives device fragmentation. The only way to overcome this challenge is by testing the app on a cloud-based testing platform. In pCloudy, you will get the benefit of testing the app on more than 5000 device browser combinations in parallel ensuring optimum device coverage.
Summing It Up
Test automation has many benefits like better test coverage, faster feedback, and accelerated results which reduces the time to market of any application. Using the practices mentioned above you can ensure coherent test automation and increased productivity. Apart from these points, there are many other things you can do like writing original code and not copying it as the code taken from other sources might not work in your test environment. But you will always find new roadblocks which you will have to deal with spontaneously.
Intelligent Digital Mesh is the entwining of people, devices, content, and services enabled by digital models, business platforms and a rich, intelligent set of services to support digital business. We have witnessed the implementation of AI in every technology to leverage the benefits of autonomous systems. Enterprises are now focusing on using AI with technologies like blockchain and immersive technology which will create new categories of apps. In this type of environment, attaining optimum device coverage will be essential to ensure quality services. Now let’s understand the fundamentals of the intelligent digital mesh.
Intelligent
In the near future, most of the mobile applications and services will use artificial intelligence or machine learning at some level. AI will be the inconspicuous force of most of the popular app categories while creating some new ones. Intelligent apps also create a new intelligent layer between people and systems as seen in enterprise advisors and virtual user assistants. Augmented analytics is also gaining ground and helping enterprises in enhancing business intelligence and data analytics using ML and NLP. Another use of AI and ML is in intelligent things like smart vacuums, drones, autonomous farming vehicles. Intelligent devices are getting smarter to serve better and reduce human dependency to a minimum.
Source: Gartner.com
Digital
When we talk about digital, we mean digital twins, cloud to the edge, conversational platforms, and Immersive Experience. A digital twin is a digital representation of real-world objects. It offers information on the state of the counterparts, improves operations and adds value to the operations by responding to the changes. In the near future, all the aspects of human life and the real world will be interconnected with their digital representation capable of advance simulation, analysis, and operation. This combined with immersive technologies like AR, VR, and MR will take extended reality to a new level.
Mesh
Mesh is the connection between devices, people, businesses, services, and content to build a digital ecosystem that yields high-quality results. Here mesh refers to technologies like Blockchain, Event-driven, and continuous adaptive risk and trust (CARTA). Enterprises are keen to find new ways to sense the new business events to get the most out of it. A business event can be a change in the status of the deal like finalizing a deal. Using new technologies like AI, it will be easier to detect a business event and analyze it in greater detail.
Security is one of the most important and ever-evolving processes in digital businesses. There is a need to think beyond infrastructure and parameter protection. Continuous adaptive risk and trust assessment is a people-centric security approach that allows for real-time risk and trust-based decision making. New methodologies like DevSecOps and adaptive honeypots should be implemented to strengthen the security of digital businesses.
Automation Testing For Intelligent Apps
Intelligent apps are at the core of the intelligent digital mesh. Nowadays most of the apps use artificial intelligence, machine learning or predictive analysis to make suggestions to the customers. The apps use real-time and historical data from user interactions and other sources to predict the needs of their users.
To ensure the quality of apps it is important to test the apps using futuristic tools. Manual testing is just enough and even automation needs to be scalable to get better results. Testing the app on a cloud-based app testing platform is the best choice as you can use as many devices as you want to test your app. Also, parallel testing increases app testing efficiency by multifold.
pCloudy’s AI-powered autonomous testing bot steals the show when it comes to testing intelligent apps. The bot tests the app on real devices with just a single click and generates a detailed report based on the test result.
Conclusion
Mobile devices, by and large, are the focal point of most of the innovations that are happening around the intelligent digital mesh. Whether it is Ai driven development, autonomous things or immersive experience, mobile apps still used as a foundation to provide the technology to the masses. But the growing complexities of intelligent apps makes it crucial to implement new methods of app testing. A cloud-based app testing platform like pCloudy is suitable to ensure quality at speed in mobile app testing. The freedom of accessing hundreds of real devices from anywhere at any time and perform manual or automation testing using futuristic features is the correct way to test intelligent apps.
Asia’s leading software testing conference, QAI STC 2019 concluded on December 6th, 2019. The theme of this year’s STC was architecting continuous quality: think, transform, and thrive in an Intelligent future. The STC is a platform for experts to display ideas, experiments, and experiences to explore challenges and suggest techniques and innovations to overcome common problems.
Avinash’s keynote focused on the future of testing which was lauded by the top minds and became the highlight of the event.
pCloudy was the title sponsor for the event and so we got a bigger platform to showcase our contribution in shaping the future of mobile app testing.
More than 500 experts from around 130 software companies gathered to learn new trends in testing, share their ideas and grow their network. There were 14 keynotes from the industry leaders and 50 professionals got a chance to take the stage and share their views about the emerging technologies.
Let’s have a look at the major learnings from this event.
Future of Testing
The STC started with a keynote by Avinash which provided insights about the future of work, applications, and testing. How “We Working” will be the primary organizational model and algorithmic management will take over the middle management to some extent. He also talked about how people will have to constantly upskill to work with the ever-changing technology and the work-life challenges will increase in the future. A digital mesh architecture will allow enterprises to build an agile, flexible, and cloud-ready ecosystem. This will enable real-time connectivity of employees, business processes, business data, and services to help address high volumes of traffic and become cloud-native and mobile-first.
Quality Engineering and Digital Transformation
There was a great emphasis on quality engineering at the STC as most of the organizations are trying to take QE to the next level. Quality engineering focuses on the end-to-end management and the basic principle is the all the teams should bear the responsibility of maintaining the quality in the process. Software QE is the assurance of high standards in the software development life cycle while implementing DevOps and Agile.
The main role is played by testers who create, implement and maintain systems used to control the quality of production processes. These people need to have a deep understanding of all the technological activities and evaluation principles.
Quality engineering methodology is even bigger in scale than the traditional QA approach and that’s the reason that QEs cannot work in silos. In quality system engineering, people in multiple roles like IT architects, designers, test engineers, project managers, business architects, etc., must cooperate to meet customer expectations. Quality engineering is driven by emerging technologies such as AI and Big Data analytics. Automation is the driving force behind turning the traditional testing into a more effective quality support model.
The quality engineering team usually partners along with the business users and the product managers for having a better understanding of the required product details to match up the problems since the starting of the product to the last stage.
Artificial intelligence, Machine learning, and IoT
The recent development in AI, ML and, IoT were the buzz creators. Experts elaborated on how augmented analytics will be utilized for creating, developing, and consuming analytics by combining these technologies. An augmented analytics engine can identify, filter, and analyze data, and then recommend what needs to be done next without the need of an IT team. These technologies will make data-driven insights accessible to a much larger set of workers.
The conference turned out to be really productive with good insights about emerging technologies and tools. It was a great opportunity to connect with software testing experts and professionals from around the globe. To say the least, it was a remarkable event where we got a great response and positive feedback from the crowd.
