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Static analysis is a mature field with applications to bug detection,security analysis, and code optimization, etc. To facilitate theseapplications, static analysis frameworks play an essential role by providing aseries of fundamental services such as program abstraction, control flow graphconstruction, and points-to/alias information computation, etc. However,despite impressive progress of static analysis, and this field has seen severalpopular frameworks in the last decades, it is still not clear how a staticanalysis framework should be designed in a way that analysis developers couldbenefit more: for example, what a good IR (for analysis) ought to look like?What functionalities should the module of fundamental analyses provide to easeclient analyses? How to develop and integrate new analysis conveniently? How tomanage multiple analyses?  To answer these questions, in this work, we discuss the design trade-offs forthe crucial components of a static analysis framework, and argue for the mostappropriate design by following the HBDC (Harnessing the Best Designs ofClassics) principle: for each crucial component, we compare the design choicesmade for it (possibly) by different classic frameworks such as Soot, WALA,SpotBugs and Doop, and choose arguably the best one, but if none is goodenough, we then propose a better design. These selected or newly proposeddesigns finally constitute Tai-e, a new static analysis framework for Java.Specifically, Tai-e is novel in the designs of several aspects like IR, pointeranalysis and development of new analyses, etc., leading to an easy-to-learn,easy-to-use and efficient system. To our knowledge, this is the first work thatsystematically explores the designs and implementations of various staticanalysis frameworks, and we believe it provides useful materials and viewpointsfor building better static analysis infrastructures.

Tai-e: A Static Analysis Framework for Java by Harnessing the Best Designs of Classics