Symbolic execution is a powerful program analysis technique, but it is difficult to apply to programs built using frameworks such as Swing and Android, because the framework code itself is hard to symbolically execute. The standard solution is to manually create a framework model that can be symbolically executed, but developing and maintaining a model is difficult and error-prone. In this paper, we present Pasket, a new system that takes a first step toward automatically generating Java framework models to support symbolic execution. Pasket's focus is on creating models by instantiating design patterns. Pasket takes as input class, method, and type information from the framework API, together with tutorial programs that exercise the framework. From these artifacts and Pasket's internal knowledge of design patterns, Pasket synthesizes a framework model whose behavior on the tutorial programs matches that of the original framework. We evaluated Pasket by synthesizing models for subsets of Swing and Android. Our results show that the models derived by Pasket are sufficient to allow us to use off-the-shelf symbolic execution tools to analyze Java programs that rely on frameworks.

Sketch-based synthesis, epitomized by the Sketch tool, lets developers synthesize software starting from a partial program, also called a sketch or template. This paper presents JSketch, a tool that brings sketch-based synthesis to Java. JSketch's input is a partial Java program that may include holes, which are unknown constants, expression generators, which range over sets of expressions, and class generators, which are partial classes. JSketch then translates the synthesis problem into a Sketch problem; this translation is complex because Sketch is not object-oriented. Finally, JSketch synthesizes an executable Java program by interpreting the output of Sketch.

Unless the speed gap between CPU and memory disappears, efficient memory usage remains a decisive factor for performance. To optimize data usage of programs in the presence of the memory hierarchy, we are particularly interested in two compiler techniques: pool allocation and field layout restructuring . Since foreseeing runtime behaviors of programs at compile time is difficult, most of the previous work relied on profiling. On the contrary, our goal is to develop a fully automatic compiler that statically transforms input codes to use memory efficiently. Noticing that regular expressions , which denote repetition explicitly, are sufficient for memory access patterns, we describe how to extract memory access patterns as regular expressions in detail. Based on static patterns presented in regular expressions, we apply pool allocation to repeatedly accessed structures and exploit field layout restructuring according to field affinity relations of chosen structures. To make a scalable framework, we devise and apply new abstraction techniques, which build and interpret access patterns for the whole programs in a bottom-up fashion. We implement our analyses and transformations with the CIL compiler. To verify the effect and scalability of our scheme, we examine 17 benchmarks including 2 SPECINT 2000 benchmarks whose source lines of code are larger than 10,000. Our experiments demonstrate that the static layout transformations for dynamic memory can reduce L1D cache misses by 16% and execution times by 14% on average.