Outcome: Transparent test coverage, quantify quality for decision making, reduced defects, improved CSAT & overall trained team for continuity

STAGE - 1 [Day Zero]

We don't often have a scenario, where management proactively prioritises quality and invest time and resources in its enhancement, not to mention engaging directly in quality-related matters. Typically, we find ourselves in the position of having to persuade stakeholders that the effort is worthwhile, especially when it comes to boosting velocity. However, in this particular instance, the management reached out to address the quality challenge. This was a significant advantage as it spared us the need to invest time and energy in convincing them of the vital connection between quality and improved velocity 

STAGE - 2 [3 days]

We embarked on a thorough exploration of the business, its organizational structure, functional intricacies, application layers, technology stack, and the nature of quality-related challenges. We delved into the pain points and issues affecting the system, examined the delivery methods in place, and differentiated between premium and all customers. Additionally, we conducted an in-depth assessment of the existing test cases. This comprehensive groundwork laid the foundation for our strategic approach. This took us about 3 days and a couple of important people who have the in-depth understanding.

STAGE - 3 [2 Days]

In response to our initial findings, we crafted a tailored orientation and training program designed to equip the team with the necessary knowledge and skills. This program aimed to elucidate the "Why" behind the entire endeavor, outline the specific steps to be taken, and establish clear methodologies for measuring the outcomes achieved. This phase was pivotal in ensuring that our team was fully aligned and empowered to drive the project forward. - 4 hour training for a batch of X people

STAGE - 4 [3 Weeks - 16 hours a week]

With all the inputs and the application flow, list out the most critical module as well as the most troublesome one. In this case it happened to be the same. there were 1200 tests in the sanity and we set out to optimise it. Ofcourse this was like on the job training as well with their application!

1. Uncover test parameters

• Collected all known parameters and paths of the module in question

• Sorted them into classes, options and criticality

• Post this we should be able to generate the cartesian product combinations

2. Reduction

• Reduced the valid parameters and paths- Using the functional rules

- constraints 

- Interaction Levels

3. Selection

• Using the tribal knowledge, heuristics 

- Selected the Critical Path ones embedded with all data variations

- Required ones, must haves, trouble causing ones etc

4. Generation of Test Script

• Script template to adjust and generate test cases that makes sense and inline with steps 1,2,3

5. Optimization

• Remove redundancies

• Include all validation requirements

• Create required test data

6. Segregate to sanity & the rest

7. Execute for correctness, robustness and repeat the iteration to fine-tune.

STAGE - 5

Measure the outcomes

- We started with ~1200 sanity for a module and after stage 1 to 4, we were able to deduce 8 tests as sanity with 100% coverage for all known parameters.

- Execution resulted in some validation gaps identified by the long timers, they were bridged to solidify the suite. Even after all the additions it was around 170 tests for full suite for that module!

- Timetaken to execute was drastically reduced, less test uncovering more defects, visibility into what failed, less tests to automate!

The synergy of efficiency and comprehensiveness resulted in a more robust testing process, ensuring the highest quality for critical healthcare Pharma systems. By harnessing combinatorial techniques, we efficiently generated optimised test scenarios by encompassing a myriad of input combinations, guaranteeing thorough and robust testing. The results were truly remarkable, including heightened test coverage, enhanced defect detection, and, most importantly, an overall increase in the quality of healthcare software.