SQF Start
QA – Status quo
As ERP systems move to the cloud, the frequency and scale of updates increase significantly.
Systems are tightly interconnected – changes no longer have an isolated impact, but affect entire process chains.
Quality assurance no longer scales with development.
Test automation itself is becoming increasingly maintenance-intensive.
Traditional QA is not designed for this level of dynamism.
QA needs to evolve fundamentally.
AI is becoming necessary to manage complex systems holistically.
A new approach to quality assurance
The modern approach to software testing can be expressed in a very simple formula:
QA + AI = SQF
The three building blocks of the formula | QA
QA + AI = SQF
QA is the key to quality – it is more than just testing.
Its strengths make QA indispensable – but also difficult to scale.
Understanding what needs to be tested and how.
Structuring tests based on requirements.
Building and evolving test logic over time.
Evaluating results in the business context.
The three building blocks of the formula | AI
QA + AI = SQF
AI offers the potential to overcome the limits of traditional QA.
However, these capabilities are often fragmented and require specialised expertise.
Can derive tests from requirements.
Can generate test data based on context.
Can analyse results across systems.
Can dynamically expand test coverage.
The three building blocks of the formula | SQF
QA + AI = SQF
SQF is a QA infrastructure made up of specialised AI agents,
enabling an end-to-end testing process,
without replacing existing data or system components.
It is a scalable and traceable system that works by orders of magnitude
faster and more reliably,
is cost-efficient to operate
and noticeably reduces the workload in everyday testing.
SQF as an interplay of specialised AI agents
The SQF AI agents take on
clearly defined roles within the testing process:
1. User Story Agent
2. Test Case Agent
3. Feature Extractor Agent
4. Crawler Agent
5. Test Data Agent
6. Test Automation Agent
7. Bug Reporter Agent
8. Security Agent
AI is changing QA. SQF is the way forward – towards measurable quality in operation.
SQF in everyday ERP operations
Example: a change to the “purchase order” workflow in the ERP system
What happens today:
Tests have to be carried out manually.
The impact on other processes is unclear.
Regression tests are incomplete and labour-intensive.
What the SQF AI agents do with it:
Automatic analysis of the change.
Relevant test cases are created directly.
Test automation is generated automatically.
Complete regression across systems.
Clear results instead of manual evaluation.
Operation in the cloud or on-premise
SQF within existing system landscapes
SQF integrates seamlessly without replacing existing workflows.
It is introduced in a targeted way and grows with the requirements.
Fits into existing system landscapes and connects systems without media discontinuities.
Integration with existing tools such as Jira and Git, as well as with existing teams.
Implementation by QA specialists in collaboration with the customer team – from targeted use through to a complete system.
Control and security are maintained
As systems move to the cloud, the pace of change increases significantly.
SQF makes this complexity manageable – without losing control.
Crawlers compare documentation with actual system behaviour.
AI takes over labour-intensive parts of test automation – in parallel rather than sequentially.
People can intervene, adjust and approve at any time.
Results are verifiable and auditable at all times.
SQF in a nutshell
SQF turns QA into an end-to-end process,
in which specialised AI agents take on clearly defined roles.
AI is changing QA.
SQF makes complex systems manageable.
SQF operates cost- and time-efficiently and grows with operational change.
From documentation to test cases
Thank you
Software Quality Framework
QA + AI =
