Synthetic Test Data (STD) is a specification that evaluates the performance of a specific, real-life product or service against agreed specifications. In this sense, it becomes an important part of quality management. It also saves costs by reducing the number of standardization, which, in turn, translates to more profitability and efficiency. It can specify test results in both unit and per-unit basis. This is especially useful when companies are looking to standardize on services rather than products. The accuracy of STDs depends greatly on data accuracy.
How does Synthetic Test Data become an important part of STDs? Companies can use STDs to compare two different systems, one based on a set of predefined test conditions and the other assuming the same conditions but with varying inputs. In the former case, a company can measure how its new service or product performs in response to the same set of inputs. In the latter case, a company can compare two real-life systems using only the predefined test criteria, which eliminates the need for establishing and implementing a new test method. Both cases allow companies to leverage testing at its maximum effectiveness and capacity.
How do STDs differ from actual test data? Well, as previously mentioned, STDs are based on agreed-test conditions, while actual test data are usually generated based on the specific requirements of each individual testing situation. In this respect, there are some significant differences between STDs and real-life data. But when both are used together, they offer similar, more efficient ways of evaluating test methods and their outputs.
As part of the testing process, each product or service has to be subjected to a wide variety of conditions. Some of these conditions may not necessarily be favorable for the implementation of the product or service under consideration. Hence, in STDs, the comparison of performance under specific and existing test conditions becomes important. Synthetic testing data, on the other hand, are based on known and proven characteristics of the product or service under evaluation.
Besides providing an unbiased comparison between two or more products or services under investigation, synthetictestdata also provides insights that would otherwise be difficult or impossible to obtain otherwise. For example, it is easy to determine whether or not two or more samples showed the same exact results when the same test conditions are applied to them. However, in real life, it can be a different story.
When using synthetic data, it is also easier to control for extraneous variables. For example, there is no need to statistically control for the number of days users spent in the garage, the number of miles driven per week, or the period of use for any given product. All that needs to be controlled is the duration of time the product was used, the average mileage covered, and the average temperature during usage. With synthetictestdata, the number of days spent in a hot climate, the number of days spent in a cold climate, and the average temperature during usage can be easily analyzed.
Another significant advantage of synthetictestdata is that it can help reveal performance issues that would otherwise be missed by other tests. For example, the tester can compare two performance intervals and look for a significant deviation from the ideal or predicted values. These values can then be compared to the actual results and the cause of the deviation can be easily determined. This enables quick identification of areas of concern and improvement directions can be adjusted immediately.
Synthetic test data offers a great deal of assistance to testers and investigators. It simplifies the process and allows information to be shared rapidly between the testers and researchers. It also allows quick identification of problematic areas and helps to reduce unnecessary rework by eliminating correlated data. In many cases, synthetic test data allows quick identification of problem areas that would otherwise require a large amount of testing time and money. Because synthetic test data allows for controlled, reproducible data, it allows users to conduct quality control in an efficient and cost-effective manner.