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Collaborative Testing Effort for Improved Quality

The collaboration amongst the business, development and testing teams can reduce the risk during the entire software development and testing lifecycle and considerably improve the overall quality of the end application. As a testing practitioner, I believe that the testing teams need to begin collaboration at an earlier stage as described below rather than the conventional collaboration during the test strategy phase:

·         During the requirement analysis phase the business/product teams need to collaborate with the development teams to validate the requirements.

·         The test data needs to be available earlier and the testing teams need to collaborate with business/product teams to validate the test data for accuracy, completeness and check if it's in sync with the business requirements spelled out.

·         Collaborate with the development team and share the test data which can be used in the unit/integration testing phases.

·         Collaborate again with the business teams to formulate a combined acceptance test strategy which would help reduce time to market.

·         Collaborate with the development team to review the results of unit testing/integration testing and validate them.

·         Collaborate with business/product teams to validate the test results of the combined acceptance testing.

Testing at each lifecycle stage has its own set of challenges and risks. If the potential defects are not detected earlier they escalate further down in the SDLC. However, an experienced and competent test practitioner can identify these defects earlier on, when it originates, and address them in the same stage. Below are some examples which reinstate this fact.


·         A good static testing of the functional, structural, compliance and non-functional aspects of an application during the requirement phase can reduce 60% of the defects from cascading down to production.

·         Similarly, getting all the required test data (as specified by the business requirement) as early as towards the end of requirements analysis phase can inject the sense of testing early in lifecycle which would improve test predictability.

·         Planning ahead for performance, stability and scalability testing during the system design phase can help reduce the costs of the potential defect incurred later on. Also, proactive non-functional testing (as required by business) contributes significantly for faster time to market.

·         Test modeling during the test preparation phase helps avoid the tight coupling of the system that is being tested with the test environment. This eventually helps in achieving continuous progressive test automation.

·         Collaboration with the development teams ensure that they have used and benefited from the test data shared by the testing teams. This collaboration helps the testing teams validate the architecture, design and code by following simple practical in-process validations through static testing of the functional, structural and non-functional aspects of the application.

·         Mechanisms which help predict when to end testing is a key requirement during execution. One such mechanism is a stop test framework based on the understanding of the application carved around the defect detection rate.

All the approaches described above let testers save on time and focus more on the right metrics collection and maintaining dashboards during test execution. It also ensures that testing is not limited to just one phase but is a fine thread that runs across the entire SDLC in order to improve quality, reduce costs and time to market for all business applications.

The benefits of this collaborative approach are many. I have listed a few benefits based on my collaborative team experiences:

·         De-risks the entire software development process by embedding testing as an inherent part of each stage of the SDLC process.

·         Defects are found early in the life cycle which reduces the total cost of fixing the defect at a later stage. The cost ratio between finding the defect at the requirements stage vs finding the same at the production stage is 1:1000.

·         Shortens the time to market by using this approach which has a built in self-corrective mechanism at each.

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