A New Approach to Evaluating Nullability Inference Tools

Null-pointer exceptions are serious problem for Java, and researchers have developed type-based nullness checking tools to prevent them. These tools, however, have a downside: they require developers to write nullability annotations, which is time-consuming and hinders adoption. Researchers have therefore proposed nullability annotation inference tools, whose goal is to (partially) automate the task of annotating a program for nullability. However, prior works rely on differing theories of what makes a set of nullability annotations good, making comparing their effectiveness challenging. In this work, we identify a systematic bias in some prior experimental evaluation of these tools: the use of “type reconstruction” experiments to see if a tool can recover erased developer-written annotations. We show that developers make semantic code changes while adding annotations to facilitate typechecking, leading such experiments to overestimate the effectiveness of inference tools on never-annotated code. We propose a new definition of the “best” inferred annotations for a program that avoids this bias, based on a systematic exploration of the design space. With this new definition, we perform the first head-to-head comparison of three extant nullability inference tools. Our evaluation showed the complementary strengths of the tools and remaining weaknesses that could be addressed in future work.