The development of advanced technologies in a dynamic and competitive market has required vehicle manufacturers to launch new models with a short time to market and a larger number of functionalities. For this reason, there has been an increase in the number of Electronic Control Units (ECUs) in modern vehicles. T he huge amount of data related to artifacts produced during the development of these automobiles (e.g., requirements, design models, simulations, tests, fault documentation, etc.) are generally created by several teams, including teams from different companies due to OEM-Supplier relationships and consequently stored in various locations and databases. Only part of the faults found in automotive software are covered by the planned tests. The rest of the faults are correlated with unforeseen operating conditions, integration with other vehicle components or even degradation. For each functionality embedded on ECU, there are have several testing procedures within a large set of possibilities for new tests. So , it is important to create a method of prioritizing the automated test routines, guaranteeing a product with fewer failures and safer for society. This paper proposes to explore the stored data(Data Analytics) related to automotive ECU development and build a prioritization tool capable of suggesting which testing routines should preferably be performed (if they have already been implemented) or request a new develop(otherwise), resulting in an improvement of test efficiency due to: reduction in the time required for decision making, better efficiency in execution of verification and validation tests and also the opportunity to obt ain other unexpected information(Data Mining) that may add value to the Test Management process inside the Product Development Process itself as a whole.