OMrun is a framework for the data review from any heterogeneous data sources using SQL. The in-memory-comparisons are carried-out rule-based and automatically and any differences are reported at field level. Tolerances and ranges of values can be simply recorded and approved. It is possible to execute also "soft/blurry" checks in addition to "hard/sharp".
The compare process takes place in RAM directly to drive the execution as fast as possible. Who wants to use OMrun efficiently must have knowledge of SQL especially, which will last to automate all tests in OMrun.
Test results, which violate the rules, are shown in red color and are statistically recorded. The integrated fault analysis in OMrun helps to reduce troubleshooting time drastically. The results can be evaluated in OMrun itself, as well as in the supplied OMdashboard.
Of course, all results can be exported in MS Excel and reused anywhere.
All functions are documented and visualized in detail on the OMrun Help platform.
OMrun features an integrated process engine: various test steps are grouped into scenarios and can run triggered manually or via scheduler. OMrun can call other tools and programs as "Master" or can be called as "Slave". Interfaces to UC4 from Automic, the Tosca Testsuite, HP Application Lifecycle Management (ALM) or HP Quality Center Enterprise (QC) are well established. All test cases managed by OMrun can be reused in different scenarios. This allows highly complex processing steps and testing procedures to be performed automatically - we call this model "scenario of scenario".
In comparison to many other tools, OMrun focuses on data as the primary test objects. This is because we believe that data structures are usually very stable and therefore less maintenance effort is needed. We are also convinced that all changes to a software has an effect on the data and can be identified there. The oftentimes time comsuming manual creation of test data is made obsolete.
"You name it - we connect it!"
OMrun snaps by means of different pre-configured adaptors into many existing database systems and file types. More adaptors to measure data quality can be realized upon request. The following types have been integrated already:
The defined rules and tolerances from the IT or business specification are shown and stored in OMrun. These are applied during comparison to the source data of system A, what calculates the expected (target) value for the data check in advance.
The comparison of the predicted values with the values of the target system to be tested hereafter is s simple 1:1-comparison only. This approach allows OMrun to verify even very large amounts of data extremely performant and the time effort for integration and regression testing can be minimized significantly with the help of test automation using OMrun.
The solution approach with rule-based validation has yet another advantage: in this way, OMrun discovers amongst others the so-called "overhang". OMrun can identify all data values of the target system that no longer exist in the source system ("dead data bodies"). This is successful because OMrun emits all as "failed", what is not described by the defined rules.
So if a value in the target system is available, which was not expected, then either a rule has to be adapted or additionally recorded, to "legitimize" this value - or it is an real error. With this iterative approach a kind of re-engineering is made with marginal effort during testing, which can often occur in systems with incomplete documentation and provides additional added value. We call this "evolutionary testing".