Mobile App Development
Testing
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First QE, then QA

Test Automation strategy is designed beforehand, during SDLC, and then developers, operations & QE teams, all work together as a single unit
We define test case automation beforehand, script them, make the executables ready as a part of our development process
Shift left software paradigm is followed, wherein the attention to quality is paid, from very conceptualisation phase
QE includes Agile based processes and uses test-driven development and behaviour-driven development processes to define requirements that are meaningful to the business developers and the testing teams
Automated tests are culled from continuous integration and continuous deployment processes, and are validated as part of your build deployment
Validating the app becomes quick. Accelerating the release cycle, cost and time to market significantly reduce
Faster incorporation of user feedback helps help in getting higher user satisfaction and customer loyalty

Digital Transformation with Agile

Agile enables rapid iterations based on customer feedback which drives a broader cultural shift in an organisation to address the opportunities and risks in the new digital landscape.
Senior management focuses on creating, articulating, and communicating a compelling & transformative future digital vision. This helps businesses in mapping digital strategy that enables them to measure progress and make real-time adjustments to improve outcome.
Our focus is on building digital customer intimacy, in particular with the front end customer experience to drive engagement.
An integrated package doesn’t works for us. We cherry pick technologies and techniques to enable a digital ecosystem that automates the customer experience through social, data, cloud, and mobile.
Exposure to new growth opportunities – not by adding digital features to existing products, but by changing direction and considering how products and services adapt to the digital customer.
Use of secure digital platforms to support the vision. Powerful digital platforms like open APIs, open datasets, service catalogs, integration frameworks, solution guidance, and collaboration tools enables a business to quickly create it’s own market based on customer-focused solutions leveraging enterprise grade information and services.
Drive insight with Data Driven Visualisation through statistical graphics, plots, infographics, and dynamic tables and charts. It helps users in analyzing and reasoning data and evidence. It makes complex data more accessible, understandable and usable. It accelerates a stakeholder and executive's understanding and decision-making around technologies and capabilities, reducing rework and, ultimately, application development costs.
Embrace Digital Agility to create advantage. Digital agility initiatives are rooted in 30-day sprints with new iterations built better and faster. This allows us to consistently experiment and adjust – the concept of learn, launch, re-learn, re-launch – refining the approach in manageable iterations.

Machine Learning in Testing

Traditional testing methods fall out of equation when it comes to sheer amounts of data that testers need to handle. Not just with this use case, ML brings revolutionary changes with workflows and processes. Let’s see how.
Predictive Analysis
Test Suite Optimization
Log Analytics
Defect Analytics
Traceability
Chatbot Testing

IoT Testing

IoT testing approach can be different based on the system/architecture involved. We focus more on Test-As-A-User [TAAS] approach rather than testing based on the requirements.

IoT Tests broadly includes:-

  • Device level testing
  • Cloud-level checking
  • Mobile-level control
  • End-to-End testing

Big Data Testing

Big Data generates value from the processing of large quantities of data that is remarkable in terms of variety, volume, and velocity. As software test engineers, we largely focus on volume and speed of data generation, data source and format, selection of test data, individual module testing, end to end application testing, reliability and performance testing while planning the tests for big data applications.
Improve efficiency with live data integration testing

Big data apps requires LIVE data feed and real-time analysis. It thus requires a data centric testing approach. The volumes and varieties of data sources make LIVE integration a complex task. The objective of Data Integration testing is to verify that data moves as expected. At a high level, data integration can be divided into the following categories:

  • Reduce downtime by instant application deployment testing
  • Application performance testing
  • Ensure scalability
  • Functional consistency
  • Security Testing
  • Failover Testing
  • Automation Testing

Increased adoption of DevOps

With DevOps, we take the Agile model a step further by bringing closer the release and deployment activities to those of development and testing. Thus, the Agile team on it’s own is responsible for the development, testing and release of the app.
Our DevOps team provides a host of services that includes:-
DevOps readiness and maturity assessment

We assess the current state of DevOps adoption, processes, and tools against the DevOps maturity model

DevOps in QA implementation

We implement standardized process, frameworks and tools into the DevOps set up

DevOps readiness and maturity assessment

We assess the current state of DevOps adoption, processes, and tools against the DevOps maturity model

DevOps in QA implementation

We implement standardized process, frameworks and tools into the DevOps set up

A mix of manual & automated tests

Big Data generates value from the processing of large quantities of data that is remarkable in terms of variety, volume, and velocity. As software test engineers, we largely focus on volume and speed of data generation, data source and format, selection of test data, individual module testing, end to end application testing, reliability and performance testing while planning the tests for big data applications.
Analyze your existing test suites
  • Not all manual tests are required for each sanity or regression test. We scope out irrelevant manual tests per specific test cycles
  • We eliminate tests that consistently pass and don’t add value
  • We identify and consolidate duplicate tests
  • We write data driven tests or tests that rely on timely setup of environments. i.e. we focus on manual tests that are better-suited for automation
Shift more test automation into the Dev Cycle
When thinking about your existing test automation code and it’s code coverage, there may be a functional coverage overlap between the automated and manual testing. If the automation scripts are shared across the SDLC and are also executed post-commit on every build, this can shrink some of the manual validation work the testing team needs to do.
Using tools and automation fit nicely into a testing strategy. But actually running them is just a small part of the value, and it is always wrapped up in a blanket of testing. Tools and testing go hand in hand and feed into each other, one helps make the other more useful and when balanced just right, the tester learns more about their product.

API and Micro Services Test Automation

Organizations that dream about building and deploying systems that are just not easy to develop, but also scalable, flexible and adaptable keenly look forward to the micro service architecture. It consists of fine grained independent modular processes, that together create a complete application or task. All services, are hence, easier to deploy and manage as small composable components. Because of this, they are also independently testable.
Unit Testing

Unit tests are typically written at the level of the class or small collection of classes. The goal is to exercise the smallest pieces of software to validate that they behave as expected, before moving on to larger chunks of functionality. Being often numerous and internal to a microservice, unit tests are great candidates for automating and – even in a microservices context – can leverage any one of a number of existing unit-testing frameworks relevant to your language of choice.

Contract, the Service layer, and test automation

Contract testing treats each service as a black box and all the services are called independently and their responses are verified. Any dependencies of the service are treated as stubs that allows the service to function but not to interact with any other service. This helps in avoiding any complication that may be caused by external calls and turn the focus on performing the tests on a single device.

A “contract” is however a service call where a specific output is expected for certain inputs. Every consumer receives the same outputs over time, even if the service changes. Architecture is flexible to add more functionality as required to the Responses later on. However, these additions do not break the service functionality. The service stays robust over longer durations and the consumers do not require to modify their code to take into account the changes made later on.

Integration Testing

Verification of the services that have been individually tested is performed. This critical part of microservice architecture testing relies on the proper functioning of inter-service communications. Service calls are made with integration to external services, including error and success cases. Integration testing thus validates that the system is working together seamlessly and that the dependencies between the services are present as expected.

End-To-End Testing

End-to-end testing verifies that the entire process flows work correctly, including all service and DB integration. Thorough testing of operations that affect multiple services ensures that the system works together as a whole and satisfies all requirements. Frameworks like JBehave help automate Functional testing by taking user stories and verifying that the system behaves as expected.

UI/Functional Testing

User interface testing tests the system as an end-user would use it. All the databases, interfaces, internal and third-party services work together seamlessly to produce the expected results.

Shortened Delivery Cycle

The key to shortened delivery cycle is “Test Driven Development”. The core objective is to build smaller test cycles and bring more agility in the process.
With TDD, we start by scripting the test cases for a new functionality, followed by production code required to pass the test, post that we refactor the code to increase its sustainability.
TDD is used both for designing the software and testing it. The automated Unit tests are written before the code, which enables fast confirmation on whether the code behaves as expected.
At DevsLane, we assure TDD by following assured best practices:-
  • Prepare a road-map of tests
  • Define Rules/Guidelines
  • Treat implementation separately
  • Similarly name test classes
  • Write New Code only when a test fails

Increased adoption of Open Source tools

Key Drivers for high adoption of Open Source Test Automation Tools :-
Highly Economical
Bundled with features
User Friendliness
Easy to Customize
Competitive Necessity
Availability
Touch Support Community
Quicker Updates
Compatibility & Integration
Supports Continuous Integration
Easy to Learn
Easy to Train new Testers
Speed of Execution
Improved Reliability
Reporting Capabilities
No Lock in
Easy to Recruit