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The Open Testing Platform – SD Times

This is a rather unique time in the evolution of software testing.  Teams worldwide are facing new challenges associated with working from home. Digital transformation initiatives are placing unprecedented pressure on innovation.  Speed is the new currency for software development and testing. The penalty for software failure is at an all-time high as news of outages and end-user frustration go viral on social media. Open-source point tools are good at steering interfaces but are not a complete solution for test automation.

Meanwhile, testers are being asked to do more while reducing costs.

Now is the time to re-think the software testing life cycle with an eye towards more comprehensive automation. Testing organizations need a platform that enables incremental process improvement, and data curated for the purpose of optimizing software testing must be at the center of this solution. Organizations that leverage multiple open-source or proprietary testing tools must consider an Open Testing Platform to keep pace with Agile and enterprise DevOps initiatives.   

What is an Open Testing Platform?
An Open Testing Platform (OTP) is a collaboration hub that assists testers to keep pace with change. It transforms observations into action – enabling organizations to inform testers about critical environment and system changes, act upon observations to zero in on ‘what’ precisely needs to be tested, and automate the acquisition of test data required for effective test coverage.

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The most important feature of an Open Testing Platform is that it taps essential information across the application development and delivery ecosystem to effectively test software. Beyond accessing an API, an OTP leverages an organization’s existing infrastructure tools without causing disruption—unlocking valuable data across the infrastructure. An OTP allows any tester (technical or non-technical) to access data, correlate observations and automate action. 

Model in the middle
At the core of an Open Testing Platform is a model. The model is an abstracted representation of the transactions that are strategic to the business. The model can represent new user stories that are in-flight, system transactions that are critical for business continuity, and flows that are pivotal for the end-user experience.

In an OTP, the model is also the centerpiece for collaboration. All tasks and data observations either optimize the value of the model or ensure that the tests generated from the model can execute without interruption.  Since an OTP is focused on the software testing life cycle, we can take advantage of known usage patterns and create workflows to accelerate testing. For example, with a stable model at the core of the testing activity:

  •   The impact of change is visualized and shared across teams
  •   The demand for test data is established by the model and reused for team members
  •   The validation data sets are fit to the logic identified by the model
  •   The prioritization of test runs can dynamically fit the stage of the process for each team, optimizing for vectors such as speed, change, business-risk, maintenance, etc.

Models allow teams to identify critical change impacts quickly and visually. And since models express test logic abstracted from independent applications or services, they also provide context to help testers collaborate across team boundaries.

Data curated for testing software
Automation must be driven by data. An infrastructure that can access real-time observations as well as reference a historical baseline is required to understand the impact of change. Accessing data within the software testing life cycle does not have to be intrusive or depend on a complex array of proprietary agents deployed across an environment. In an overwhelming majority of use cases, accessing data via an API provides enough depth and detail to achieve significant productivity gains.  Furthermore, accessing data via an API from the current monitoring or management infrastructure systems eliminates the need for additional scripts or code that require maintenance and interfere with overall system performance.

 Many of the data points required to optimize the process of testing exist, but they are scattered across an array of monitoring and infrastructure management tools such as Application Performance Monitoring (APM), Version Control, Agile Requirements Management, Test Management, Web Analytics, Defect Management, API Management, etc.

An Open Testing Platform curates data for software testing by applying known patterns and machine learning to expose change. This new learning system turns observations into action to improve the effectiveness of testing and accelerate release cycles. 

Why is an Open Testing Platform required today?
Despite industry leaders trying to posture software testing as value-added, the fact is that an overwhelming majority of organizations identify testing as a cost center. The software testing life cycle is a rich target for automation since any costs eliminated from testing can be leveraged for more innovative initiatives.

If you look at industry trends in automation for software testing, automating test case development hovers around 30%.  If you assess the level of automation across all facets of the software testing life cycle, then automation averages about 20%.  This low average automation rate highlights that testing still requires a high degree of manual intervention which slows the software testing process and therefore delays software release cycles.

But why have automation rates remained so low for software testing when initiatives like DevOps have focused on accelerating the release cycle? There are four core issues that have impacted automation rates:

  •   Years of outsourcing depleted internal testing skills
  •   Testers had limited access to critical information
  •   Test tools created siloes
  •   Environment changes hampered automation

Outsourcing depleted internal testing skills
The general concept here is that senior managers traded domestic, internal expertise in business and testing processes for offshore labor, reducing Opex . With this practice known as labor arbitrage, an organization could reduce headcount and shift the responsibility for software testing to an army of outsourced resources trained on the task of software testing. This shift to outsourcing had three main detrimental impacts to software testing: the model promoted manual task execution, the adoption of automation was sidelined and there was a business process “brain-drain” or knowledge drain. 

 With the expansion of Agile and the adoption of enterprise DevOps, organizations must execute the software testing life cycle rapidly and effectively. Organizations will need to consider tightly integrating the software testing life cycle within the development cycle which will challenge organizations using an offshore model for testing.  Team must also think beyond the simple bottom-up approach to testing and re-invent the software testing life cycle to meet increasing demands of the business. 

Testers had limited access to critical information 
Perhaps the greatest challenge facing individuals responsible for software testing is staying informed about change. This can be requirements-driven changes of dependent application or services, changes in usage patterns, or late changes in the release plan which impact the testers’ ability to react within the required timelines. 

Interestingly, most of the data required for testers to do their job is available in the monitoring and infrastructure management tools across production and pre-production. However, this information just isn’t aggregated and optimized for the purpose of software testing. Access to APIs and advancements in the ability to manage and analyze big data changes this dynamic in favor of testers. 

Although each organization is structurally and culturally unique, the one commonality found among Agile teams is that the practice of testing software has become siloed. The silo is usually constrained to the team or constrained to a single application that might be built by multiple teams. These constraints create barriers since tests must execute across componentized and distributed system architectures.

Ubiquitous access to best-of-breed open-source and proprietary tools also contributed to these silos. Point tools became very good at driving automated tests. However, test logic became trapped as scripts across an array of tools. Giving self-governing teams the freedom to adopt a broad array of tools comes at a cost:  a significant degree of redundancy, limited understanding of coverage across silos, and a high amount of test maintenance. 

The good news is that point tools (both open-source and proprietary) have become reliable to drive automation. However, what’s missing today is an Open Testing Platform that assists to drive productivity across teams and their independent testing tools.  

Environment changes hampered automation
Remarkably, the automated development of tests hovers at about 30% but the automated execution of tests is half the rate at 15%. This means that tests that are built to be automated are not likely to be executed automatically – manual intervention is still required. Why? It takes more than just the automation to steer a test for automation to yield results. For an automated test to run automatically, you need: 

  •   Access to a test environment
  •   A clean environment, configured specifically for the scope of tests to be executed
  •   Access to compliant test data
  •   Validation assertions synchronized for the test data and logic

 As a result, individuals who are responsible for testing need awareness of broader environment data points located throughout the pre-production environment. Without automating the sub-tasks across the software testing life cycle, test automation will continue to have anemic results.

An Open Testing Platform levels the playing field 
Despite the hampered evolution of test automation, testers and software development engineers in test (SDETs) are being asked to do more than ever before. As systems become more distributed and complex, the challenges associated with testing compounds. Yet the same individuals are under pressure to support new applications and new technologies – all while facing a distinct increase in the frequency of application changes and releases. Something has got to change. 

An Open Testing Platform gives software testers the information and workflow automation tools to make open-source and proprietary testing point tools more productive in light of constant change.  An OTP provides a layer of abstraction on top of the teams’ point testing tools, optimizing the sub-tasks that are required to generate effective test scripts or no-code tests. This approach gives organizations an amazing degree of flexibility while significantly lowering the cost to construct and maintain tests.

 An Open Testing Platform is a critical enabler to both the speed and effectiveness of testing.  The OTP follows a prescriptive pattern to assist an organization to continuously improve the software testing life cycle.  This pattern is ‘inform, act and automate.’ An OTP offers immediate value to an organization by giving teams the missing infrastructure to effectively manage change. 

The value of an Open Platform

Inform the team as change happens
What delays software testing? Change, specifically late changes that were not promptly communicated to the team responsible for testing. One of the big differentiators for an Open Testing Platform is the ability to observe and correlate a diverse set of data points and inform the team of critical changes as change happens. An OTP automatically analyzes data to alert the team of specific changes that impact the current release cycle.

 Act on observations

Identifying and communicating change is critically important, but an Open Testing Platform has the most impact when testers are triggered to act. In some cases, observed changes can automatically update the test suite, test execution priority or surrounding sub-tasks associated with software testing. Common optimizations such as risk-based prioritization or change-based prioritization of test execution can be automatically triggered by the CI/CD pipeline.  Other triggers to act are presented within the model-based interface as recommendations based on known software testing software algorithms.

Automate software testing tasks 
When people speak of “automation” in software testing they are typically speaking about the task of automating test logic versus a UI or API.  Of course, the scope of tests that can be automated goes beyond the UI or API but also it is important to understand that the scope of what can be automated in the software testing life cycle (STLC) goes far beyond the test itself.  Automation patterns can be applied to:

  •   Requirements analysis
  •   Test planning
  •   Test data
  •   Environment provisioning
  •   Test prioritization
  •   Test execution
  •   Test execution analysis
  •   Test process optimization

Key business benefits of an Open Testing Platform
By automating or augmenting with automation functions within the software testing life cycle, an Open Testing Platform can provide significant business benefits to an organization. For example:

  • Accelerating testing will improve release cycles
  • Bringing together data that had previously been siloed allows more complete insight
  • Increasing the speed and consistency of test execution builds trust in the process
  • Identifying issues early improves capacity
  • Automating repetitive tasks allows teams to focus on higher-value optimization
  • Eliminating mundane work enables humans to focus on higher-order problems, yielding greater productivity and better morale

Software testing tools have evolved to deliver dependable “raw automation.” Meaning that the ability to steer an application automatically is sustainable with either open-source or commercial tools.  If you look across published industry research, you will find that software testing organizations report test automation rates to be (on average) 30%.  These same organizations also report that automated test execution is (on average) 16%.  This gap between the creation of an automated test and the ability to execute it automatically lies in the many manual tasks required to run the test. Software testing will always be a delay in the release process if organizations cannot close this gap.  

Automation is not as easy as applying automated techniques for each of the software testing life cycle sub-processes.  There are really three core challenges that need to be addressed:

  1. Testers need to be informed about changes that impact testing efforts.  The requires interrogating the array of  monitoring and infrastructure tools and curating data that impacts testing.
  2. Testers need to be able to act on changes as fast as possible. This means that business rules will automatically augment the model that drives testing – allowing the team to test more effectively.
  3. Testers need to be able to automate the sub-tasks that exist throughout the software testing lifecycle.  Automation must be flexible to accommodate each team need yet simple enough to make incremental changes as the environment and infrastructure shifts.

Software testing needs to begin its own digital transformation journey. Just as digital transformation initiatives are not tool initiatives, the transformation to sustainable continuous testing will require a shift in mindset.  This is not shift-left.  This is not shift-right. It is really the first step towards Software Quality Governance.  Organizations that leverage multiple open-source or proprietary testing tools must consider an Open Testing Platform to keep pace with Agile and enterprise DevOps initiatives.  

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