R&D
❯
Automated QA
❯Predictive testing
Predictive testing
Scope:
Automatic detection of inaccurate test outcomes in an application development
process
Goal:
Other
Automated Quality Assurance
For:
Quality Engineers
Predictive data quality
Scope:
A solution for assessing data quality in data collection systems.
Goal:
Other
Anomaly detection in sensor data using deep learning techniques
For:
Maintenance and support functions, monitoring, procurement
Scope:
Temporal data captured from sensors.
Goal:
Other
Predictive testing
Goal:
OtherProblem addressed
The goal of the project is the improvement of the automation level in the
application testing process. This is achieved by the automatic identification of
inaccurate test outcomes, reducing the number of failure alerts.
Scope of use case
Automatic detection of inaccurate test outcomes in an application development
process
Description
The testing phase represents a critical point for many
companies, with a strong technological impact. The test
execution is often not completely automated, thus requiring
a significant effort in terms of people and time.
The event log analysis of tests can prevent the presence of
false positives (failed tests not related to failures in the target
application), can help in the identification of the stage in
which the error occurred and can help identifying the actual
outcome of the test.
The solution consists in adopting machine learning
methodologies to analyse the available data (coming from
different applications and sources involved in the tests),
identify correlations and patterns in order to identify false
positives, automate testing phases and recommend
mitigation actions.