Deep typechecking and refactoring

Abstract

Large software systems are typically composed of multiple layers, written in different languages and loosely coupled using a string-based interface. For example, in modern webapplications, a server written in Java communicates with a database back-end by passing in query strings. This widely prevalent approach is unsafe as the analyses developed for the individual layers are oblivious to the semantics of the dynamically constructed strings, making it impossible to statically reason about the correctness of the interaction. Further, even simple refactoring in such systems is daunting and error prone as the changes must also be applied to isolated string fragments scattered across the code base.We present techniques for deep typechecking and refactoring for systems that combine Java code with a database back-end using the Java Persistence API [10]. Deep typechecking ensures that the queries that are constructed dynamically are type safe and that the values returned from the queries are used safely by the program. Deep refactoring builds upon typechecking to allow programmers to safely and automatically propagate code refactorings through the query string fragments.Our algorithms are implemented in a tool called QUAIL. We present experiments evaluating the effectiveness of QUAIL on several benchmarks ranging from 3,369 to 82,907 lines of code. We show that QUAIL is able to verify that 84% of query strings in our benchmarks are type safe. Finally, we * Supported in part by the NSF grants CCF-0427202, CNS-0541606, and CCF-0546170.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. show that QUAIL reduces the number of places in the code that a programmer must look at in order to perform a refactoring by several orders of magnitude.

Abstracting Context in Event-Based Software

“…The lack of integration of such query languages with the programming language and thus compiler however makes it harder to statically check queries [53]. In addition, such languages can hardly support advanced features such as parametric guards [26], where guards contain program variables whose values are propagated across the network whenever they change.…”

Debugging mixed-environment programs with Blink

“…Tan and Croft use static analyses to study security issues in Java's standard library . Of course, static multilingual bug checkers are not restricted to Java and C. For example, Quail performs string analysis for Java/SQL safety . Static analysis is complementary to dynamic debugging, which helps find some bugs static analysis misses.…”

Jinn

“…Since the C code expresses Java type information in strings, standard static type checking cannot resolve the types and even sophis-ticated interprocedural analysis cannot always resolve them [10,28]. Consequently, the C compiler does not statically enforce typing constraints on the "Collections" and "sort" names or the ls and cmp parameters.…”

“…For example, JNI's use of string arguments to describe class names and method signatures cleanly abstracts over the underlying JVM and its implementation. Unfortunately, these strings prevent standard static type checking, and even advanced interprocedural static analysis is incomplete [10,28]. Similarly, complete static checking of pending exceptions and the count of local references is impossible.…”