Category: Intelligent Automation

Intelligent Test Automation in a DevOps World

The importance of intelligent test automation

Digital transformation has disrupted time to market like never before. Reducing the cycle time for releasing multiple application versions through the adoption of Agile and DevOps principles has become the prime factor for providing a competitive edge. However, assuring quality across application releases is now proving to be an elusive goal in the absence of the right amount of test automation. Hence it is no surprise that according to the Data Bridge Market research, the automation testing market will reach an estimated value of USD 19.9 billion by 2028 and grow at a CAGR of 14.89% in the forecast period of 2021 to 2028.

Test automation is a challenge, not only because an organization’s capabilities have traditionally been focused on manual testing techniques but also because it’s viewed to be a complex siloed activity. Automation engineers are expected to cohesively bind the vision of the business team, functional flows that the testers use, along their own core component of automation principles and practices. Production continuum can only become a reality when there is a disruption to automation as a siloed activity, ably supported by maximum collaboration and convergence of skillsets. Even then, without the adoption of the right test automation techniques, it becomes near impossible to realize the complete value.

Outlined below are steps towards making test automation initiatives more effective and results-oriented.

Comprehensive coverage of test scenarios

Test automation, to a large extent, focuses on the lower part of the test pyramid addressing viz – unit testing and component testing but neglecting the most crucial aspect of testing business-related areas. The key to assuring application quality is to identify those scenarios that are business relevant and automate them for maximum test coverage. The need of the hour is to adopt tools and platforms that cover the entire test pyramid and not restrict it to any level.

Read more: The right testing strategies for AI/ML applications

A test design-led automation approach can help in ensuring maximum coverage of test scenarios. However, given that this is a complex area, aggravated by the application complexity itself, what tools can help with is to handle the sequence of test scenarios, expressing the business rules and associating data-driven decision tables attached to the workflow, thereby providing complete coverage of all high-risk business cases. By adopting this sequence, complexity can be better managed, modifications can be applied much faster, and tests can be structured to be more automation friendly. 

This approach helps to analyze functional parameters of the test in a better way and helps to define what needs to be tested with sharp focus, i.e., enable a sharper prioritization of the test area. It aggregates various steps involved in test flow along with the conditions each step can have and prioritizes the generation of steps along with risk association.

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Sharp focus on test design

The adoption of Test Driven Development (TDD) and Behavior Driven Development (BDD) techniques aims to accelerate the design phase in Agile engagements. However, these techniques come at the cost of incomplete test coverage and test suite maintenance-related issues. Test design automation aims to overcome these challenges by concentrating on areas like requirements engineering, automated test case generation, migration, and optimization. Automation focus at the test design stage contributes to tremendous value-add downstream by removing the substantial load from scripting test cases and generating them. 

Adoption of the right toolsets accelerates the inclusion of test design automation during the earlier stages of the development process, making it key to Agile engagements. Most test design automation tools adopt visual-based testing. They make use of graphical workflows that can be understood by all project stakeholders – testers, business stakeholders, technical experts, etc. Such workflows can be synchronized with any requirements management toolsets and collaboratively improved with inputs from all stakeholders. User stories and acceptance criteria are contextualized so that everyone can see the functional dependency between the previous user stories and the ones that were developed during the current sprint.

Collaboration is key

Collaboration is the pillar of Agile development processes. By bringing collaboration into test design, risk-based coverage of test cases can be effectively addressed, along with the generation of automated scripts on a faster note. Automation techniques steeped in collaboration provide the ability to organize tests by business flows, keywords, impact and ensure depth of test coverage by leveraging the right test data. 

By integrating test automation tools into Agile testing cycles, a collaborative test design can be delivered with ease. With such tools, any changes to user stories can be well reflected; users can comment on the flows or data, identify and flag risks much earlier. These tools also enable the integration of test cases into test management tools of choice like Jira and generate automation scripts that can work under different automation tools like selenium.

Making legacy work

Most organizations suffer from a huge backlog of legacy cases – there is a repository of manual test cases that are critical for business. Organizations need them to be a part of the agile stream. For this to happen, automation is mandatory. Manual test cases of legacy applications are very rich in application functionality and make good sense to get retrofitted into test automation platforms.

New age test design automation frameworks and platforms can address legacy tests that are already documented, parse them, and incorporate them as a part of the automation test suite. Many of these tools leverage AI to retro engineer manual test cases into the software platform – graphical workflow, test data, and test cases themselves can be added to the tool. 

You may also like: Uncovering nuances in data-led QA for AI/ML applications

A closer look at the current test automation landscape outlines a shift from the siloed model that existed earlier. Clearly visible is the move towards automation skillsets, coding practices, and tools-related expertise. Automation tools are also seen moving up the maturity curve to optimize the effort of test automation engineers, at the same time enabling functional testers with minimal exposure to automation stacks to contribute significantly to automation effort. All in all, such shifts are accelerating the move towards providing organizations the ability to do more automation with existing resources.

Trigent’s partnership with Smartesting allows us to leverage test design automation by Integrating these tools in your Agile testing cycles, thus being able to quickly deliver collaborative test design, risk-based coverage of test cases, and faster generation of automated scripts. We help you organize tests by business flows, keywords, risks, depth of coverage, leveraging the right test data, as well as generate and integrate test cases into test management tools of your choice (JIRA, Zephy, Test Rail, etc.).

Our services will enable you to take on your documented legacy tests, parse them and bring them into such tools very quickly. Further, we help you generate test automation scripts that can work under different automation tools like Selenium & Cypress. Our services are delivered in an As-A-Service Model, or you can leverage our support to implement the tools and the training of your teams to achieve their goals.

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The Best Test Data Management Practices in an Increasingly Digital World

A quick scan of the application landscape shows that customers are more empowered, digitally savvy, and eager to have superior experiences faster. To achieve and maintain leadership in this landscape, organizations need to update applications constantly and at speed. This is why dependency on agile, DevOps, and CI/CD technologies has increased tremendously, further translating to an exponential increase in the adoption of test data management initiatives. CI/CD pipelines benefit from the fact that any new code that is developed is automatically integrated into the main application and tested continuously. Automated tests are critical to success, and agility is lost when test data delivery does not match code development and integration velocity.

Why Test Data Management?

Industry data shows that up to 60% of development and testing time is consumed by data-related activities, with a significant portion dedicated to testing data management. This amply validates that the global test data management market is expected to grow at a CAGR of 11.5% over the forecast period 2020-2025, according to the ResearchandMarkets TDM report.

Best Practices for Test Data Management

Any organization focusing on making its test data management discipline stronger and capable of supporting the new age digital delivery landscape needs to focus on the following three cornerstones.

Applicability:
The principle of shift left mandates that each phase in an SDLC has a tight feedback loop that ensures defects don’t move down the development/deployment pipeline, making it less costly for errors to be detected and rectified. Its success hinges to a large extent on close mapping of test data to the production environment. Replicating or cloning production data is manually intensive, and as the World Quality Report 2020-21 shows, 79% of respondents create test data manually with each run. Scripts and automation tools can take up most heavy lifting and bring this down to a large extent when done well. With production quality data being very close to reality, defect leakage is reduced vastly, ultimately translating to a significant reduction in defect triage cost at later stages of development/deployment.

However, using production-quality data at all times may not be possible, especially in the case of applications that are only a prototype or built from scratch. Additionally, using a complete copy of the production database is time and effort-intensive – instead, it is worthwhile to identify relevant subsets for testing. A strategy that brings together the right mix of product quality data and synthetic data closely aligned to production data models is the best bet. While production data maps to narrower testing outcomes in realistic environments, synthetic data is much broader and enables you to simulate environments beyond the ambit of production data. Usage of test data automation platforms that allocates apt dataset combinations for tests can bring further stability to testing.

Tight coupling with production data is also complicated by a host of data privacy laws like GDPR, CCPA, CPPA, etc., that mandate protecting customer-sensitive information. Anonymizing data or obfuscating data to remove sensitive information is an approach that is followed to circumvent this issue. Usually, non-production environments are less secure, and data masking for protecting PII information becomes paramount.

Accuracy:
Accuracy is critical in today’s digital transformation-led SDLC, where app updates are being launched to market faster and need to be as error-free as possible, a nearly impossible feat without accurate test data. The technology landscape is also more complex and integrated like never before, percolating the complexity of data model relationships and the environments in which they are used. The need is to maintain a single source of data truth. Many organizations adopt the path of creating a gold master for data and then make data subsets based on the need of the application. Adopting tools that validate and update data automatically during each test run further ensures the accuracy of the master data.

Accuracy also entails ensuring the relevance of data in the context of the application being tested. Decade-old data formats might be applicable in the context of an insurance application that needs historic policy data formats. However, demographic data or data related to customer purchasing behavior applicable in a retail application context is highly dynamic. The centralized data governance structure addresses this issue, at times sunsetting the data that has served its purpose, preventing any unintended usage. This also reduces maintenance costs for archiving large amounts of test data.

Also important is a proper data governance mechanism that provides the right provisioning capability and ownership driven at a central level, thereby helping teams use a single data truth for testing. Adopting similar provisioning techniques can further remove any cross-team constraints and ensure accurate data is available on demand.

Availability:
The rapid adoption of digital platforms and application movement into cloud environments have been driving exponential growth in user-generated data and cloud data traffic. The pandemic has accelerated this trend by moving the majority of application usage online. ResearchandMarkets report states that for every terabyte of data growth in production, ten terabytes are used for development, testing, and other non-production use cases, thereby driving up costs. Given this magnitude of test data usage, it is essential to align data availability with the release schedules of the application so that testers don’t need to spend a lot of time tweaking data for every code release.

The other most crucial thing in ensuring data availability is to manage version control of the data, helping to overcome the confusion caused by conflicting and multiple versioned local databases/datasets. The centrally managed test data team will help ensure single data truth and provide subsets of data as applicable to various subsystems or based on the need of the application under test. The central data repository also needs to be an ever-changing, learning one since the APIs and interfaces of the application keeps evolving, driving the need for updating test data consistently. After every test, the quality of data can be evaluated and updated in the central repository making it more accurate. This further drives reusability of data across a plethora of similar test scenarios.

The importance of choosing the right test data management tools

In DevOps and CI/CD environments, accurate test data at high velocity is an additional critical dimension in ensuring continuous integration and deployment. Choosing the right test data management framework and tool suite helps automate various stages in making data test ready through data generation, masking, scripting, provisioning, and cloning. World quality report 2020-21 indicates that the adoption of cloud and tool stacks for TDM has witnessed an increase, but there is a need for more maturity to make effective use.

In summary, for test data management, like many other disciplines, there is no one size fits all approach. An optimum mix of production mapped data, and synthetic data, created and housed in a repository managed at a central level is an excellent way to go. However, this approach, primarily while focusing on synthetic data generation, comes with its own set of challenges, including the need to have strong domain and database expertise. Organizations have also been taking TDM to the next level by deploying AI and ML techniques, which scan through data sets at the central repository and suggest the most practical applications for a particular application under test.

Need help? Partner with experts from Trigent to get a customized test data management solution and be a leader in the new-age digital delivery landscape.

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