The process of software testing has become more complex in recent years, especially since applications often have to work in cooperation with other technologies and therefore compatibility plays an essential role. On the one hand, a cooperation can be a connection between hardware, an operating system, and a database. On the other hand, software nowadays often has to interact with other systems, for example, to exchange information or initiate further processing steps. Interfaces to other systems, in particular, represent a major challenge in verifying compatibility, as they usually cannot be influenced directly and information about a new release from manufacturers is often not actively communicated. Above all, this can be problematic if an interface has been changed and the compatibility can no longer be guaranteed. From the described problem the main question of this thesis was derived: To what extent can technology monitoring for new releases be automated? In order to answer this question, we have designed and implemented a system to support the domain expert in the process of monitoring technologies for new updates. This system extracts information from various data sources, each of which was analyzed differently. Texts extracted from email newsletters, RSS feeds and Twitter were analyzed with Natural Language Processing (NLP) and it turned out that it is capable of detecting release information in these texts. Online encyclopedias, from which information about previously published updates as well as preview versions of technologies could be obtained, can also help in detecting technology updates. Furthermore, the analysis of the search engine data has shown, that a technology release may cause an increase of the number of search requests extracted from Google Trends. In summary, it can be said that the mined data sources are suitable for the process of detecting technology releases on release date as well as in advance. However, since the process we have developed relies on a domain expert to undertake the setup of the data sources, and in particular the choice of keywords that are searched and can influence the results, a semi-automated system for monitoring technologies could be developed. To be more precise, the biggest advantage of the developed system is that the otherwise very complex process of data extraction and analysis could be automated.