Testing ecosystems have gone through a rapid evolution over the last decade as a result of introduction of new technology and process, virtualization and the focus on digital transformation. Testing has also advanced from being a product validation during the last stage of product lifecycle to a more meaningful continuous testing process across all phases. Test solutions, frameworks and approaches will continue to evolve, respond and align with market demands.
To address the key factors of speed, quality and cost, organizations have been at the forefront in identifying and implementing new test frameworks, approaches and technologies. Today, intelligent test solutions utilizing artificial intelligence and machine learning are being developed for various scenarios with positive results. But as more and more companies embrace intelligent test solutions, it’s important to understand best practices for deriving maximum business value.
Although organizations have initiated increased focus on intelligent test solutions for improving effectiveness, efficiency and productivity, they are still unsure of the tangible and intangible benefits expected. There are multiple factors to consider that will ensure seamless implementation and deliver positive long-run business results.
Here are top five key success factors to consider:
1. Focus on areas with maximum impact
Often, teams will attempt to implement intelligent testing across all test phases without the proper analysis, scope or understanding. This leads to results that are lower than expected and in the worst case, abandoning the solution. Intelligent testing solutions need to be applied in stages. First, roll it out where the potential for maximum impact is highest and then move to other areas, eventually bringing benefits across the entire product lifecycle.
For example, regression testing is a high-impact area where sufficient test coverage is lacking due to new requirements and the addition of test cases. An AI/ML-based solution applied in this case would help improve the release quality.
2. Quantifiable ROI, improved efficiency and effectiveness
One of the primary goals of using an AI/ML approach in intelligent testing is to achieve maximum efficiency with the least effort and cost, leading to a higher return on investment. Introducing an intelligent test solution lowers selection, acquisition and maintenance costs. There will be additional costs in some areas such as hardware and user training.
The timeframe for generating the required returns needs to be considered in the initial rollout phase. And equal consideration should be given to efficiency and effectiveness factors that lead to test-quality improvement.
3. Scalable solution
When applied across multiple phases of the testing lifecycle, the intelligent test solution should be able to handle large datasets and make smart decisions with agility. During the evaluation stage, it is important to plan based on the number of parallel test cycles being executed, the volume of test data being generated (defect data, test execution logs). For example, intelligent risk-based testing solutions require large amounts of user test-case data, test data and defect data to derive an optimized set of test cases for every test cycle.
4. User interface and engagement
AI and ML are evolving fast, and so are the algorithms, which are not foolproof. Implementation success also depends on user interface that provides users with visibility into decision criteria, propagation impact and enables them to understand issues that the AI system cannot solve. The user must be able to monitor and validate the suggestions based on scenario, modify/override the dataset and provide additional information to ensure seamless automation.
5. Adopt the right approach, not the tools
To deliver benefits, organizations need to take a holistic, end-to-end view when approaching intelligent test-based solutions. Tools are secondary; they only augment the test approach, process and methodologies. Understanding and analyzing the scenario and planning for future improvements requires first adopting the right approach. Once potential improvement areas have been identified, the next step is to identify a pilot use case that can be solved using an intelligent test solution. An initial small project will help the team gain insight and experience to demonstrate business value.
Altran is leading charge of innovation for testing solutions with intelligent testing offering focused on key industry segments including communications and technology systems, semiconductors, industrial equipment, enterprise software and consumer products. The Altran intelligent testing offering encompasses artificial Intelligence and machine learning based test automation solutions, broad industry knowledge and technical expertise. Our solution offers a compelling alternative to competing test solutions and platforms by delivering the following key benefits:
• Improved productivity with shorter and more effective test cycles for optimal test coverage
• Increased ROI with better defect detection leading to an improved customer experience
• Optimal test coverage with the right set of test cases
• Improved response time with on-demand test environment availability
• Significant improvement in reduced field defects with an AI-driven test approach
Some of the key accelerators and approaches that are part of the Altran intelligent test solution include test case minimization, selection and optimization, risk-based testing, and root-cause analysis, to name a few.