Month: January 2017

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Introduction to Android Espresso Testing and Spoon

Espresso UI test automation framework is Google’s de-facto testing platform for Android app developers.

The way it is easily used from within Android Studio and IntelliJ IDEA IDE’s makes it a powerful tool that differentiates it from other open-source cross-platform solutions such as Appium and other commercial tools.

Before drilling into basic setup and execution of an Espresso simple test, let’s first understand some of the basics:

  • Espresso is an Android only test automation framework (not cross platform like Appium/Selenium)
  • Espresso requires a separate APK package running in parallel with the application under test
  • Espresso is not Dev-Language Free framework like Appium (that supports Java, JS, Python, C#, Perl)

Positive Motivations to Use Espresso

  • The Espresso framework is embedded into the entire dev workflow and IDE, and that makes the adoption and leverage higher
  • Espresso can be used to do a quick post-commit validation of a fix or new code implementation, and also as part of a larger test scale within the CI workflow.
  • Espresso provides fast feedback to its users which is a big advantage since it is running on the device/emulator side-by-side with the app
  • Espresso supports annotations to determine the test execution scope (small/medium/large) which organizes the overall testing cycle for both dev and test
  • Espresso has unique synchronization method in its core making the tests less flaky and more robust. It will pass to the next test step in the code only once the view is available on the device screen in opposed to other tools that can easily fail without having timers, validation points and more.

Basic Espresso Framework Methods:

Espresso framework allows the automation developer to manipulate the test using 3 concepts:

  1. View Matchers
  2. View Actions
  3. View Assertions

basicses

As seen in the above definition, onView(xxx) of a specific object on the app screen, an Action will be performed and an Assertion will be made to validate the end result.

Espresso Setup

The setup within Android studio is quite simple, and there is plenty of documentation in the google community around it.

The developer will edit his build.Gradle file for the application under test to include the Espresso framework dependency, the JUnit version, and the InstrumentationRunner (see below example)

gradlesample

Once the above is done, it is time to create for the corresponding app the test class.

This class will need to include through Import, few libraries that are required by the Espresso test (below example)

import

Test Code Implementation

In order to develop an Espresso UI automation, the developer must have the unique object identifiers for the application under test.

To study the app objects (Hamcrest Matchers) the developer can use various methods:

  1. UIAutomation.bat tool that is built into the Android Studio SDK
  2. All resource ID’s should be automatically be stored in a dynamically generated R.java file
  3. Object spy within tools that supports Espresso (Perfecto and others).

Looking at a simple TipCalculator application, you can see through the UIAutomator spy, that the text box object ID is named bill_value

uiautomator

In the R.java file, it will look like this (choose the best method you find comfortable)

rjava

When implementing the Espresso test code, we will leverage the ObjectID as part of the onView method to perform a Click prior to entering an input value to that text box.

code1

In order to perform a type of value into the above Total Bill text box, we will use the 2nd method provided by Espresso, that is. Perform:

code2

Once we are done with the action, we would like to assure that the result of that action is as expected, and this is when the developer will use the assertion method .Check

code3

Finally, once the entire test suite is implemented and ready – running the test from Android Studio is very simple.

Select the Test class from the Edit Configurations menu in Android studio and chose run. Select your target (ADB connected device, cloud devices, emulators).

code4

At the end of a test, a basic test report will be provided to the user.

Running Espresso Tests in Parallel – Using Spoon

No test engineer or developer will be quite unless it validates the functionality of his app on multiple devices and emulators. For that, there is another widely used tool called Spoon (there are also cloud-based solutions as mentioned above that support parallel execution on real devices). This tool, will collect all the target devices (that are visible via adb devices) test results and aggregate them into one HTML view that can be easily investigated.

example_main

In order to leverage Spoon, please download the Gradle for spoon plugin and install it. Post installation, configure as follows

gradlespoon

By default, Spoon will run your tests on all ADB connected devices, however, if you want to run concrete devices and skip others in order to reproduce a specific defect on 1 device, you can configure spoon accordingly

spoon2

Good Luck!

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Mobile Testing: Difference Between BDD, ATDD/TDD

Last week I presented in the Joe Colantonio AutomationGuild online conference – Kudos to Joe for a great event!

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Among multiple interesting questions that I got post my session,  like what is the best test coverage for mobile projects? how to design effective non-functional and performance testing in mobile and RWD?, I also got a question about the differences between BDD and ATDD.

My session was around an Open Source test automation framework called Quantum that supports cucumber BDD (Behavior Driven Development) and this obviously triggered the question.

Definition: BDD and ATDD

ATDD – Acceptance Test Driven Development

Based on Wikipedia’s definition (referenced above), ATDD is a development methodology based on communication between the business customers, the developers, and the testers. ATDD encompasses many of the same practices as specification by example,behavior-driven development (BDD),example-driven development (EDD), and support-driven development also called story test–driven development (SDD).

All these processes aid developers and testers in understanding the customer’s needs prior to implementation and allow customers to be able to converse in their own domain language.

ATDD is closely related to test-driven development (TDD). It differs by the emphasis on developer-tester-business customer collaboration. ATDD encompasses acceptance testing, but highlights writing acceptance tests before developers begin coding.

BDD – Behavior Driven Development

Again, based on Wikipedia’s definition (referenced above), BDD is a software development process that emerged from test-driven development (TDD). Behavior-driven development combines the general techniques and principles of TDD with ideas from domain-driven design and object-oriented analysis and design to provide software development and management teams with shared tools and a shared process to collaborate on software development.

Mobile Testing In the Context of BDD and ATDD

The way to look at the 2 agile like practices of BDD, ATDD, TDD is from the context of higher velocity and quality requirements.

Organizations are aiming to release faster to market, with great quality, sufficient test coverage and in the same time of course – meet the business goals and customer satisfaction. To achieve these goals, teams ought to be strongly collaborative from the very beginning app development and design stages.

Once organizations have the customer product requirements, and they can start developing through user stories, acceptance criteria’s and such the product & the tests several goals can be met:

  • High customer-vendor alignment == Customer satisfaction
  • Faster time to market, app is tested along the SDLC
  • Quality is in sync with customer needs and there are much less redundant tests
  • There are no Communication gaps or barriers between Dev, Test, Marketing, Management

 

Looking at the below example of a BDD-based test automation test code, it is very easy to understand the functionality and use cases under test, the desired test outcome.

quantum123

As can be seen in the screenshot above, the script installs and launches on an available Samsung device the TestApp.APK file performs a successful login and presses on a menu item. As a final step, it also performs a mobile visual validation to assure that the test also passes, and also as an automaton anchor, the test code reached the expected screen.

It is important to mention that the test framework, tools that can support both TDD, ATDD and BDD can be in many cases similar, and in our case above – one can still develop and test from a BDD or ATTD standpoint by using a Cucumber test automation framework (Cucumber, Quantum).

If we would compare the above functional use case, or as stated in the cucumber language “Scenario” to a scenario that would fit an ATDD based approach – we would most likely need to introduce the known “3 amigos” approach  –> three perspectives of customer (what problem are we trying to solve?), development (how might we solve this problem?), and testing (what about…).

 

Since a real ATDD best practice will determine a Gherkin like app scenario’s before the development even starts, the above BDD example will be a precondition test for the app development team to make sure that they develop against acceptance criteria that in our example is a successful app install and log in.

An additional example of an acceptance test that also involves a layer of login/Register that I can reference would like this:

effective-testing-practices-in-an-agile-environment-28-638

I can understand that confusion between BDD and ATDD since as mentioned above, they can look a lot like the same.

Bottom line, and as I responded to the event last week – both BDD/ATDD/TDD are methods to better sync the various counterparts involved in shipping a working product to the market, faster, with higher quality and with the right functionality that would meet the customer requirements. Implementing it using Gherkin method makes a lot of sense due to the easy alignment and common language these counterparts uses during the SDLC workflow.

Happy Testing!

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What You Need To Know When Planning Your Test Lab in 2017

As we kick-off 2017, I am thrilled to release the most updated 6th edition of the Digital Test Coverage Index report, a guide to help you decide how to build your test lab. 2016 was an exciting year in the Digital space, and as usual, Q4 market movement is sure to impact 2017 development and testing plans. And it doesn’t appear that the market is slowing down, with continued innovation expected this year. In this post, I will summarize the key insights we saw last quarter, as well as few important things that are projected for 2017 that should be applied when building your test lab.

dtci

Key Takeaways

  • Beta OS versions remain an important aspect of your test coverage strategy. With Apple releasing 5 different minor versions of iOS 10 since it’s release in September 2016, iPhone/iOS 10 beta are a “must-include in your test lab” device/OS combination. On the browser side, Chrome and Firefox beta versions are also critical test targets for sustaining the quality of your mobile web/responsive websites.
  • The Android fragmentation trend is changing, with Google putting pressure on device manufacturers to keep pace with the latest OS versions. As evidence, we already see that Android 6.x has the greatest market share as of Q42016, with roughly 27%, followed by Android Lollipop. With Google releasing its first Android Pixel devices, the market is already starting to see a boost in Android 7 Nougat adoption which is expected to grow within Q12017 to between 2-5% market share.
  • Galaxy S7 and S7 Edge were a turning point for Samsung: Over the last year, Samsung has seen a revenue slowdown due, in part, to competition from both Apple and emerging Android manufacturers OnePlus, Xiaomi, and Huawei. With the launch of Samsung S7 & S7 Edge, the company is regaining its position. We can see in this edition of the Index (and the previous one,) that Samsung is the leading brand in many countries, which should impact the test coverage plans in Brazil, India, Netherlands, UK, Germany and U.S.
  • The mobile app engagement methods are evolving, with various enterprises counting on the mobile platform to drive more revenues and attract more users. We are seeing greater adoption of external application integration either through dedicated OS-level applications like the iOS iMessage or through other solutions like the Google app shortcuts that were recently introduced as part of Android 7.1. These changes represent a challenge from a testing perspective, since there is now additional outside-of-app dependencies that the Dev and QA teams need to manage.
  • Test Lab size is expected to slightly grow YoY as the market matures:   Looking at the annual growth projection below, we see a slight growth in the need for a 10, 25 and 32 device lab, based on new the devices that are being introduced into the market faster than old devices are retired. What we see is an annual introduction of around 15 leading devices per year with an average retirement of 5-7 per year (due to decreased usage, terminated support by vendor etc.). Integrating these numbers into the 30%-80% model would bring the annual growth as demonstrated in the following graph.

annual_growth

 

2017 Trends

As this is the first Index for 2017, here are the most important market events that will impact both Dev and QA teams in the digital space, in the categories of Mobile, Web or both.

New Players

The most significant player to joins the mobile space in 2017 is Nokia. After struggling for many years to become a relevant vendor, and being unsuccessful under the Windows Phone brand, Nokia is now back in the game with a new series of Android-based devices that are supposed to be introduced during MWC 2017. A second player that is going to penetrate the mobile market is Microsoft who is supposed to introduce the first Microsoft Surface Phone during H1 2017.

Innovative Technologies

During 2017 we will definitely continue to see more IoT devices, smartwatches, and additional features coming from both Google and Apple, in the mobile, automotive and smart home markets. In addition, we might see the first foldable touch smartphone released to the market by Samsung under the name “Samsung X”. In addition, we should see a growing trend of external App interfaces in various forms such as bots, iMessages, App Shortcuts and Voice based features. The market refers to these trends as result of “App Fatigue” which is causing organizations to innovate and change the way their end-users are interacting with the apps and consuming data. From a testing perspective, this is obviously a change from existing methods and will require re-thinking and new development of test cases. In a recent blog, I addressed the above – feel free to read more about it here.

Key Device Launches to Consider for an Updated Test Lab

Most of the below can be seen in the market calendar for 2017, but the highlights are listed here as well:

  • Samsung S8/S8 Edge flagship devices from Samsung are due by February 2017 and should be the successors of the highly successful S7/S7 Edge devices
  • iPhone 8/iPhone 8 Plus together with iOS 11 launch in MID-September 2017 will mark the 10th anniversary for the Apple iPhone series. This launch is expected to be a groundbreaking one for iOS users.
  • Huawei Mate 9/Mate 9 Pro, and in general, the Huawei smartphone portfolio is continuing its global growth. 2017 should continue the growth trend both in China and India, but also as seen in this Index report in many European countries where we are already seeing devices like Huawei P8, P9, and others in use.

From a web perspective, we are not going to see any major surprises from the leading browsers like Chrome, FireFox, and Safari. However, from Microsoft Edge browser, we expect a significant market share uptick as more and more users adopt Windows 10 and abandon legacy Windows OS machines.

cal2017

 

In the Index report, you may find all the information necessary to better plan for 2017, as well as market calendars for both mobile and the web, plus a rich collection of insights and takeaways. DOWNLOAD HERE.

Happy Testing in 2017!

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My 2017 Continuous Quality Predictions

A guest post by Amir Rozenberg, Sr. Director of product management at Perfecto Mobile & Yoram (Perfecto CTO)
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As 2016 winds down and we look into 2017, I’d like to share few thoughts on trends in delivering high-quality digital applications. This post is organized in two parts: Start with a collection of observations of key market trends and drivers; followed with continuous quality implications. While this article focuses on examples and quotes from the banking vertical, the discussion is certainly applicable more broadly.

2017 – Year of accelerated digital transformation with user experience in focus:

eiu-temenos-report_the-3-rs-of-retail-banking-regulate-revise-re-envisage-8-238x180
Image courtesy: Banking Technology

 

 

    1. Increased formal digital engagement: Consumers want independence and access, ‘self-serve’ or ‘Direct Banking’ in the banking space, at a time and location of their preference. As A.T. Kearney reports , many transactions done today by the bank will be done by the customer. That is a big opportunity that many banks capitalize on via their online apps. atkearney
    2. Informal digital presence: Implementation of multi-channel approach inclusive of social networks is proliferating as a complementary touch point with the customer. Activities include proactively scanning social networks for disgruntled customers and addressing their challenges individually, marketing and advertising new services and streamlining services. For example, allowing users to log into their online bank account using their social network presence. One bank reports a short-term marketing effort in those channels increased 13% mobile app enrollments, doubling their social activity following etc. (Source)
    3. Improved operating efficiency: Another strong driver for the digital transformation is introducing efficient processes and leveraging new channels to better manage expenditure. According to McKinsey, digital transformation can enable retail banks to increase revenues in upward of 30% and decrease expenditure by 20%-25%.
      In addition to slashing branches for efficient online service (Ally Bank: “Instead of spending money on expensive branches, we pass the savings on to you” ), DBS also treated customer care flows and improved their efficiency.
  • User Experience & efficiency: functional and delightful experience are top of mind for both customers as well as vendors: “Our customers don’t benchmark us against banks,” said Hari Gopalkrishnan, BOFA CIO of client-facing platform technology, in an interview with InformationWeek. “They benchmark us against Uber and Amazon.”. On the application side, there is a strong emphasis on the end user efficiency as they try to accomplish the task at hand. At DBS, 250 million customer hours wasted each year were saved in 2016 by improving bank-side processes and enabling more online self-serve transactions by customers.
    Further, investments are made in the area of streamlining user flows. One example is text entry replacement by using the onboard sensors: location-via GPS, check and barcode scanning via the camera, or speech dictation via Siri, Google speech etc. “Solutions that combine the ability to find, analyze and assemble data into formats that can be read in natural language will improve both the speed and the quality of business content delivery. Personal assistants such as Apple’s Siri and Microsoft’s Cortana — as well as IBM Watson, with its cognitive technology — provide richer and more interactive content.”- From Gartner’s report “Top Strategic Predictions for 2016 and Beyond: The Future Is a Digital Thing“

Challenges & Implications

Having looked at some of the trends, the implication of accelerated digital transformation, focus on user experience now are met with competitive pressure and the need to deliver product faster to market. Many organizations are adopting agile methodologies, and from a continuous quality perspective, let’s discuss some challenges and implications:
  • (Simplified) Automation at scale: With an ever-growing matrix of test cases and shrinking test cycle, I believe (/hope) attention will be given in 2017 to designing/implementing automation at scale in organizations. There are many challenges, such as the skill set of testers/developers, cross-team collaboration, tooling, timing, and budgets. But everyone needs to agree that having over 20%-25% of manual testing, or spending too much time maintaining test script maintenance is simply blocking coverage, quality and eventually business success.
    • Always-on lab: A robust and stable lab is an absolute requirement to remove fragility, the biggest reason for test failure. Almost always this means a lab in the cloud: Device on a desk or a local lab will break the regression test in the critical moment.
    • Scripts: Need to be based on core functions which are robust, mature and reusable. Handle unplanned cases (popups), apply retry mechanism, apply baseline for the environment (Database in the right place, servers are operational, WiFi is on, no popups, etc.)
    • Switch to “always green” mode: if you need to review your results every morning and spend 1-2 hours on it, you’re doing something wrong and you can’t really scale your automation. Prefer green over coverage. A false negative is the worse disease of automation. Unless something really bad happens, your scripts should end with green status, no excuses.
    • Test automation framework: This is the building block that will drive sustainability and scale. There are many frameworks out there, some are offered as open source, some by system integrators. Here are some thoughts on selecting your test framework:
      1. Skill set and tools match: Testers skills vary. We typically see many manual testers who are supported by a core team of advanced coders. Those who code, operate in Java, Javascript, C#, Python, Ruby etc. The foundation for automation at scale is a set of sustainable and reusable automation assets (so your time spent on maintenance is limited): A solid object repository, scripts, test orchestration, and planning etc. A good framework will allow multi-language support (in a way that supports your organization) and multi-skill-level: Java-like scripting for codes, BDD-like scripting (ex.: Cucumber) for those new to coding.
      2. Open source and modular: There are significant benefits to adopting technology with the wide community behind it. We recommend selecting a solution that is made of architectural components that are best in class in its function. Shameless plug: Perfecto and Infostretch came with an open source framework named Quantum. The objective is to provide a complete package where experienced as well as non-coders can write test scripts based on smart XPath and a smart object repository via Java and Cucumber. Test orchestration and reporting are also available via TestNG. The framework is made of best-of-breed open source components, we welcome the community and our customers to try it out and give feedback.
    • Efficient, role-driven reporting: Considering automation at scale, it is mandatory to provide a strong reporting suite. The tester needs to quickly recognize trends in last nights’ regression test (hopefully made of thousands of executions), and drill down from the trends to a single test execution to analyze the root cause and compare it against yesterday’s execution or another platform. By the same token, quality visibility (‘build health’) mandates transparency. (another shameless plug:) Perfecto’s new set of dashboards enables the application team as well as executives to understand build weaknesses and establish an informed go/no-go decision.
 Next, on the challenges list, let’s discuss the client side:
  • Client side increased capabilities… and vulnerabilities: The focus to drive more functionality and streamline the user experience drive a larger coverage matrix. We’re seeing thick client applications strengthening and enriching the experience. As such, demand for test coverage and process change are needed
    1. Coverage: The proliferation of using onboard sensors and connected devices (see below) will drive the need to expand the test environment and capabilities to include those. In 2016 we saw increased use of image injection scenarios as well as touch ID. I believe in 2017 speech input will gain momentum as well as ongoing innovation around augmented reality (perhaps less in banking, rather other verticals). All of these scenarios need to be covered.
      • Of particular interest is the IoT space: This is an area that’s been growing rapidly over the last few years, whether consumer products, medical or industrial applications. “The relationships between machines and people are becoming increasingly competitive, as smart machines acquire the capabilities to perform more and more daily activities“. In Gartner’s IoT forecast, we estimate that, by 2020, more than 35 billion things will be connected to the Internet. Particularly in banking, IoT represents an interesting opportunity. For example, authenticating the customer in the branch with biometric sensing accessories will streamline experiences and increase security. Other examples include contactless payments and access to account functions from a wearable accessory (Source)
    2. Accessibility: since 2015 over 240 businesses have been sued over accessibility compliance according to WSJ. TechCrunch advice is to plan, design and implement accessibility in the app,  and work closely with a council on the regulations. We too are seeing growing demand for accessibility related coverage. This is certainly an area we’re going to pay close attention to in the near future.
Lastly, process and maturity changes:
  • Process changes and (quality) maturity growth: As we work with our customer and the market is maturing, we are fortunate to observe market trends that are happening (some slower than others, but still)
    1. CoE collaboration with the app team: As agile is implemented in many of our customers, we’re witnessing first hand the autonomy and independence driven by the application team. While the application team creates, builds and tests code, they still may need centralized perspective on quality practices and tooling needed for success. Some of these teams consider and curious about the application usage and behavior in production (more below). Our recommendation to the various teams is to seek and drive collaboration: for example, establish a slim, robust and stable acceptance test to build a common language between the tests that are run in the cycle and those running after.
    2. DevOps: Teams are seeking efficiencies and transparency in managing quality across the SDLC. One area is shifting testing earlier in the cycle, covered nicely by my colleague Eran. devops1The second is using the same (testing) tool and approach for production (‘Testing in production’). This approach reduces delays in time to launch (no need to wait until production monitoring scripts are created) and enables visibility to usage, behavior and weaknesses of the app in production. I believe traditional production-dedicated APM tools will need to find ways to merge into the cycle to survive.
    3. New entrants in the developer/quality workflow: I believe new opportunities and startups will emerge in areas that simplify/automate testing and predict the impact of code changes in advance. Imagine proactive code scanning tools integrated with production insight that direct developers about the risk associated with the area of code you’re about to touch, or automated test code/plan generators. This area has plenty of room for growth.

 

Advanced areas

  • Shifting, even more, testing left: In further mature teams we find that automation drives further test cases into the nightly regressions test, because it provides high value (as opposed to finding bugs late) and it’s frankly, possible. The area of introducing real user conditions in the cycle provides critical insight. Other areas such as small-scale multi user test (for code efficiency and multi-threading behavior), some level of security, accessibility tests etc.
  • Transitioning testing to user journey: Lastly, an advanced topic I’d like to mention is changing the perspective from a matrix of test cases X browsers/devices/OS/version X real user conditions into diagonal, if you will, user journeys across platforms. To go by example, take a typical journey for bank loan research: consumers are likely to begin their engagement on a large screen where they research rates, terms etc. They may summarize findings and take decisions using excel (local/online). They may apply for a loan over their desktop browser or on their tablet, and then continue the interaction on their mobile device. In those ‘diagonal’ test journeys one could then classify different journeys into different personas: There’s the consumer, the loan officer, the customer care professional etc. All of whom go through different journeys on different screens. Being able to provide a quality score per build for specific persona’ journey would be very meaningful to the business to make decisions. The point being, in a limited time available for quality activities, one could consider creating user journeys across specific screens rather than trying the complete rows and columns across test cases and screens matrix.
To summarize, I see an exciting 2017 coming with lots of changes and innovation in delivering digital applications that work. Certainly looking forward to taking part!