This is the artifact package of the ICST 2023 paper Model Generation For Java Frameworks by Linghui Luo, Goran Piskachev, Ranjith Krishnamurthy, Julian Dolby, Eric Bodden, Martin Schäf Paper abstract: Modern applications often rely on rich frameworks to provide functionality. Android, for instance, handles many aspects of building a mobile app. But these frameworks also have costs. Given the importance of application security and tools to ensure it, one major cost is that framework complicate tools based on static analysis: (1) They hurt analysis quality by including large amounts of complex, dynamic, and native library code. (2) Frameworks like Android become the main program, making whole program analysis the app problematic. Mechanisms such as Averroes have been developed to handle unknown library code for Java, and have proven effective for some analyses. However, they have two main limitations in the context of our complications: (1) They do not provide the precision required for security analysis. (2) They assume a main program, which is not the case for frameworks. To address this, we present GenCG, which extends Averroes to support taint analysis for Android and Spring. Evaluation with real-world Android applications shows that call graphs using the models generated by GenCG cover significantly more code of the app, improves recall of a client security analysis, and, at the same time, does not introduce more false positives.