Aligning Continuous Testing with Customer Journeys through AI

Gravity Testing
Aligning Continuous Testing with Customer Journeys through AI

Businesses are under unprecedented pressure to innovate quickly and deploy new features while ensuring high standards through continuous testing. Development cycles are shortening, customer journeys are becoming more complex, and user expectations are constantly evolving.

Traditional continuous testing approaches are reaching their limits to ensure the quality of their products and services. By the time the impact analysis and updates of tests are completed, the product and its journeys have already undergone new developments.

In this context, artificial intelligence (AI) is one of the disruptive innovations that companies are seeking to deploy. The adoption of this technology shows its strength in its coupling with human effort with activities to augment by AI.

The results of pilots carried out show productivity gains of around 30% on test prioritization activities and up to 80% on test creation. This article explores how these gains contribute to the continued alignment of testing with customer journeys.

The limits of automation

Development and test teams face a major challenge: maintaining constant synchronization between product developments and validation of user journeys.

The first step consists of setting up a non-regression campaign aligned with the user journeys to fill a validation gap and replace manual tasks. The second step is to industrialize the processes of analysis, implementation, and maintenance of automated tests carried out continuously.

Automated testing, although necessary, has intrinsic limitations:

  • Lack of flexibility: Often rigid and designed for specific scenarios, they struggle to adapt to dynamic and personalized user journeys
  • Complex maintenance: Their maintenance is time-consuming and requires specific skills, especially when applications evolve quickly
  • Limited visibility: Automated tests do not always provide a complete view of real user journeys, leading to coverage gaps.

This approach to continuous testing automation makes it possible to achieve a first level of performance. However, the evolution speed of user journeys, applications and test campaigns make this effort difficult to maintain in the long run.

The Challenge of Continuous Alignment

The ongoing alignment of tests and user journeys represents three significant challenges. These challenges are particularly acute in the context of continuous testing, where maintaining alignment with evolving customer journeys is crucial :

  1. Scope: Defining the tests to automate, prioritizing the journeys and ensuring consistency between the different sources of information is a complex and time-consuming task
  2. Implementation: Setting up and maintaining a journey analysis and automated testing infrastructure requires specific skills and significant investments
  3. Evolution : Faced with constantly evolving user journeys, automated tests must be regularly updated, which can quickly become unmanageable and with excessively high costs.

These challenges limit the ability of organizations to maintain an optimal level of automation. The goal is to guarantee complete coverage of user journeys while remaining within their resource limits of time and money.

Gravity AI maps continuously evolving user journeys. Aligning Continuous Testing with Customer Journeys
Gravity AI maps continuously evolving user journeys.

Faced with these challenges, artificial intelligence offers a solution by analyzing data intelligently and adapting to changes in applications, allowing development teams to work faster and with a better focus on activities with more added value.

User journey mapping continues

Marketing analytics and observability practices have made it possible to collect a massive amount of data on user behavior. However, this data is often scattered across different silos, making it difficult to get a cohesive overview of journeys.

The source analytical data is also at a level that is difficult to use by testers due to its fragmentation between low-level technical uses and too high-level functional uses for marketing monitoring. Since testers do not have the time or skills to carry out advanced processing, the potential value of the data is lost. 

The analysis of journey logs by an AI makes it possible to generate detailed and dynamic maps of user interactions with the application. For example, on an e-commerce platform, AI can identify the most frequent paths with clustering, and provide representations to identify friction points or unexpected behaviors encountered in the journeys.

Gravity maps the navigation elements of continuous journeys. (Aligning Continuous Testing with Customer Journeys)
Figure 2: Gravity maps the navigation elements of continuous journeys.

Generative AI goes beyond simply describing journeys by identifying trends and anomalies. A trained model learns to recognize abnormal behaviors that could signal underlying problems in the context of the company or more broadly (e.g. shopping cart abandonment rate in a given sector).

It has the potential to go further by creating AI chains that automate many tasks related to user journey mapping. For example, an AI can generate automated test scenarios based on the most frequent paths, while another AI will be able to analyze the results of these tests to identify regressions.

This chaining capacity allows addressing the significant challenge of continuous maintenance of automated tests with user journeys.

Continuous measurement and realignment of deviations

Continuous journey mapping makes it possible to address a second challenge, that of keeping the tests aligned with the journeys actually carried out by users. This is the key issue that Gravity addresses to increase testers in test prioritization and creation activities.

Gravity’s AI-based technology creates a continuous feedback loop between automated tests and real user behaviors. This keeps the tester at the center of the improvement process. Freed from low-level data analysis, they can focus on aligning gaps effectively.

Gravity provides a dashboard for measuring journey coverage by tests.  (Aligning Continuous Testing with Customer Journeys)
Figure 3: Gravity provides a dashboard for measuring journey coverage by tests. 

Concretely, AI provides the following capabilities:

  • Semantic data analysis: AI can analyze navigation data (case of Gravity), error logs and user feedback to identify the critical paths and the most frequent scenarios
  • Automatic generation of test scripts: AI can create test scripts from existing models or by analyzing user interfaces
  • Self-healing tests: AI can detect changes in the user interface and automatically adapt test scripts.

Thanks to AI, the tester can focus on high-value tasks: AI takes care of repetitive and tedious tasks, such as test generation and maintenance, freeing up the tester to focus on tasks. More strategic activities, such as analyzing results, designing new test scenarios collaboratively.

Aligning Continuous Testing with Customer Journeys - Gravity flows
Figure 4: Gravity automatically identifies necessary test realignments.
Gravity automatically identifies necessary test realignments.
Figure 4.1: Gravity automatically identifies necessary test realignments.

Following the identification of user journeys, AI assists the tester in practice with:

  1. Personalized reports generation, detailed and visual, highlighting key trends and anomalies in journeys and testing
  2. Human-machine collaboration to validate the results and define the corrective actions to be implemented
  3. Generate test scenarios: Create automated tests to verify that features related to the payment process are working correctly.

AI also provides the tester with a global and detailed vision of user journeys to identify risk areas and prioritize actions to be taken. It can thus develop more efficient and adaptive exploratory tests, based on learning.

The test documentation generated by Gravity.
Figure 5: The test documentation generated by Gravity.

The role of the QA tester is thus evolving towards a more strategic profile and oriented towards data analysis. It becomes a true partner of development teams, helping them guarantee product quality while accelerating development cycles.

Increased acceleration capacity for the organization

Artificial intelligence is revolutionizing the world of software development through the automation of repetitive tasks and supporting complex analytics. As a result, teams can focus on higher value-added activities.

Continuous testing supported by AI represents the necessary shift towards teams augmented by AI agents to meet the challenges of speed, efficiency and quality of software production activities that companies need to remain competitive.

For the company, the gains are multiple, from improving customer satisfaction, to the quality of digital offerings, to the acceleration of development cycles. The implementation of platforms integrating these capabilities is in full development, driven by business demand. 

The challenge is to find a mature solution that can supporter organizations. Gravity is the platform supporting continuous alignment of customer journeys and tests with AI-augmented activities. Pilot users demonstrate gains of 30% on test prioritization and around 80% on test creation and maintenance.

These capabilities are available for testing in your context, and thus evaluate your productivity gains and ultimately, improvement of your customer experience.

By integrating advanced AI capabilities, you can redefine your continuous testing processes to better align with customer journeys and improve your overall efficiency.

Author

Passionate about system-thinking, architecture, and technology. Convinced by the value of transversality to improve our software industry. Author of The Systemic CTO and On Defining Quality Engineering.

VP of Architecture at StepStone Group. Previous experience from software engineer to CTO at La Redoute, Grupo Lusiaves. Founder of the QE Unit, the Quality Engineering community, and MAMOS, the Quality Engineering Framework.

Website: qeunit.com  

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