How can you track the most important metrics for Enterprise Software testing?

Enterprise applications serve as the digital backbone of business enabling business workflows and functionalities of the business functions. Domain knowledge is essential for testing as testers must understand the business functionality and purpose of the systems to write relevant test cases underlining business logic- e,g merchandise or maintenance. Plan wide coverage and ensure that your test plan has room for multiple test cast covering Functional testing, Business Process testing, Integration testing, Investigation & Validation Testing, Security testing, Compatibility testing, Performance Testing (Load, Stress, capacity, endurance), API testing and even the Smoke testing. User acceptance testing (UAT) is given. #sapcommunity #s4hana

A communication plan with key stakeholders to whiteboard and identify a solution plan to meet the corporate requirements and work towards a plan with an SOW and ROI

By developing detailed test plans and success criteria and an associated risk management analysis you can ensure there is coverage from and technical and functional perspective. In addition to that there needs to be clarity that different kinds of testing such as unit and regression testing are vital in each environment dev, test, staging, prod, etc.

Quality is a measurement of tolerance for defects and to some degree a cultural issue for the business and its customers. There are very few zero defect products because the balance of cost benefit and customer acceptance of defects allow companies to deal with issues through corrective action and non-conformance requests that are enacts but come out of less stringent testing. Whether they use excel or a quality management software can still amount to the same issue. With that said; all products and industries aren’t the same. Wouldn’t we want zero defect / side effect medicine?

This is where good production applications, ai, and ml have such promise. Ai and ml have the potential to speed up the process and reduce the defect rate while ensuring that all data across all test or defect areas have been tracked and measures correctly. Production testing should be a combination or live vision based ai and predictive processes so that things like mean time to failure are tracked and acted on. If you want to evolve to prescriptive processes you need to nail down predictive first.

The more involvement from all stakeholder groups - the more effective it will be however time and cost are where the commitment falls over as companies are more willing to cut corners. Tools and such as QMS, ai, and ml can certainly enable greater coverage and efficiency. But don’t forget the people - tacit knowledge can’t be achieved without your stakeholders.

Enterprise software will have one or more Integrations and Integration test is one of the important thing. For every integration point there needs to be a set of automation testcases and these have to be trested as part of the snoke test during the release of the enterprise software. Measure this data every sprint as these are the breaking points of enterprises software.

Effective tracking of crucial metrics is ?? essential for robust enterprise software testing. ?? Test coverage, measuring code exercised by tests, ensures comprehensive validation, while defect density indicates software robustness. ? Time-to-detect and time-to-resolve showcase issue identification and fixing efficiency. ?? Test pass rate reflects overall system health. ?? Resource utilization measures efficiency in testing resource usage. Continuous monitoring enables improvement identification, and refining testing strategies for high-quality, reliable software. ??? Adapting these metrics ensures ongoing optimization, resulting in resilient solutions. ?????