Browsing by Author "Furr, Michael"
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Item Checking Type Safety of Foreign Function Calls(2005-11-15T20:58:38Z) Furr, Michael; Foster, Jeffrey S.We present a multi-lingual type inference system for checking type safety across a foreign function interface. The goal of our system is to prevent foreign function calls from introducing type and memory safety violations into an otherwise safe language. Our system targets OCaml's FFI to C, which is relatively lightweight and illustrates some interesting challenges in multi-lingual type inference. The type language in our system embeds OCaml types in C types and vice-versa, which allows us to track type information accurately even through the foreign language, where the original types are lost. Our system uses a representational type that can model multiple OCaml types, because C programs can observe that many OCaml types have the same physical representation. Furthermore, because C has a low-level view of OCaml data, our inference system includes a dataflow analysis to track memory offsets and tag information. Finally, our type system includes garbage collection information to ensure that pointers from the FFI to the OCaml heap are tracked properly. We have implemented our inference system and applied it to a small set of benchmarks. Our results show that programmers do misuse these interfaces, and our implementation has found several bugs and questionable coding practices in our benchmarks.Item Combining Static and Dynamic Typing in Ruby(2009) Furr, Michael; Foster, Jeffrey S; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many popular scripting languages such as Ruby, Python, and Perl are dynamically typed. Dynamic typing provides many advantages such as terse, flexible code and the ability to use highly dynamic language constructs, such as an eval method that evaluates a string as program text. However these dynamic features have traditionally obstructed static analyses leaving the programmer without the benefits of static typing, including early error detection and the documentation provided by type annotations. In this dissertation, we present Diamondback Ruby (DRuby), a tool that blends static and dynamic typing for Ruby. DRuby provides a type language that is rich enough to precisely type Ruby code, without unneeded complexity. DRuby uses static type inference to automatically discover type errors in Ruby programs and provides a type annotation language that serves as verified documentation of a method's behavior. When necessary, these annotations can be checked dynamically using runtime contracts. This allows statically and dynamically checked code to safely coexist, and any runtime errors are properly blamed on dynamic code. To handle dynamic features such as eval, DRuby includes a novel dynamic analysis and transformation that gathers per-application profiles of dynamic feature usage via a program's test suite. Based on these profiles, DRuby transforms the program before applying its type inference algorithm, enforcing type safety for dynamic constructs. By leveraging a program's test suite, our technique gives the programmer an easy to understand trade-off: the more dynamic features covered by their tests, the more static checking is achieved. We evaluated DRuby on a benchmark suite of sample Ruby programs. We found that our profile-guided analysis and type inference algorithms worked well, discovering several previously unknown type errors. Furthermore, our results give us insight into what kind of Ruby code programmers ``want'' to write but is not easily amenable to traditional static typing. This dissertation shows that it is possible to effectively integrate static typing into Ruby without losing the feel of a dynamic language.Item Polymorphic Type Inference for the JNI(2005-11-10T19:07:44Z) Furr, Michael; Foster, JeffWe present a multi-lingual type inference system for checking type safety of programs that use the Java Native Interface (JNI). The JNI uses specially-formatted strings to represent class and field names as well as method signatures, and so our type system tracks the flow of string constants through the program. Our system embeds string variables in types, and as those variables are resolved to string constants during inference they are replaced with the structured types the constants represent. This restricted form of dependent types allows us to directly assign type signatures to each of the more than 200 functions in the JNI. Moreover, it allows us to infer types for user-defined functions that are parameterized by Java type strings, which we have found to be common practice. Our inference system allows such functions to be treated polymorphically by using instantiation constraints, solved with semi-unification, at function calls. Finally, we have implemented our system and applied it to a small set of benchmarks. Although semi-unification is undecidable, we found our system to be scalable and effective in practice. We discovered 155 errors 36 cases of suspicious programming practices in our benchmarks.Item Profile-Guided Static Typing for Dynamic Scripting Languages(2009-04-08) Furr, Michael; An, Jong-hoon (David); Foster, Jeffrey S.Many popular scripting languages such as Ruby, Python, and Perl include highly dynamic language constructs, such as an eval method that evaluates a string as program text. While these constructs allow terse and expressive code, they have traditionally obstructed static analysis. In this paper we present PRuby, an extension to Diamondback Ruby (DRuby), a static type inference system for Ruby. PRuby augments DRuby with a novel dynamic analysis and transformation that allows us to precisely type uses of highly dynamic constructs. PRuby's analysis proceeds in three steps. First, we use run-time instrumentation to gather per-application profiles of dynamic feature usage. Next, we replace dynamic features with statically analyzable alternatives based on the profile. We also add instrumentation to safely handle cases when subsequent runs do not match the profile. Finally, we run DRuby's static type inference on the transformed code to enforce type safety. We used PRuby to gather profiles for a benchmark suite of sample Ruby programs. We found that dynamic features are pervasive throughout the benchmarks and the libraries they include, but that most uses of these features are highly constrained and hence can be effectively profiled. Using the profiles to guide type inference, we found that DRuby can generally statically type our benchmarks modulo some refactoring, and we discovered several previously unknown type errors. These results suggest that profiling and transformation is a lightweight but highly effective approach to bring static typing to highly dynamic languages.