Technical debt (TD), its impact on development and its consequences such as defects and vulnerabilities, are of common interest and great importance to software researchers and practitioners. Although there exist many studies investigating TD, the majority of them focuses on identifying and detecting TD from a single stage of development. There are also studies that analyze vulnerabilities focusing on some phases of the life cycle. Moreover, several approaches have investigated the relationship between TD and vulnerabilities, however, the generalizability and validity of findings are limited due to small dataset. In this study, we aim to identify TD through multiple phases of development, and to automatically measure it through data and text mining techniques to form a comprehensive feature model. We plan to utilize neural network based classifiers that will incorporate evolutionary changes on TD measures into predicting vulnerabilities. Our approach will be empirically assessed on open source and industrial projects.