Reasoning about string variables, in particular program inputs, is an important aspect of many program analyses and testing frameworks. Program inputs invariably arrive as strings, and are often manipulated using high-level string operations such as equality checks, regular expression matching, and string concatenation. It is difficult to reason about these operations because they are not well-integrated into current constraint solvers.We present a decision procedure that solves systems of equations over regular language variables. Given such a system of constraints, our algorithm finds satisfying assignments for the variables in the system. We define this problem formally and render a mechanized correctness proof of the core of the algorithm. We evaluate its scalability and practical utility by applying it to the problem of automatically finding inputs that cause SQL injection vulnerabilities.

We present a framework based on Genetic Programming (GP) for automatically simplifying procedural shaders. Our approach computes a series of increasingly simplified shaders that expose the inherent trade-off between speed and accuracy. Compared to existing automatic methods for pixel shader simplification [Olano et al. 2003;Pellacini 2005], our approach considers a wider space of code transformations and produces faster and more faithful results. We further demonstrate how our cost function can be rapidly evaluated using graphics hardware, which allows tens of thousands of shader variants to be considered during the optimization process. Our approach is also applicable to multi-pass shaders and perceptualbased error metrics.

Modern compilers typically optimize for executable size and speed, rarely exploring non-functional properties such as power efficiency. These properties are often hardware-specific, time-intensive to optimize, and may not be amenable to standard dataflow optimizations. We present a general post-compilation approach called Genetic Optimization Algorithm (GOA), which targets measurable non-functional aspects of software execution in programs that compile to x86 assembly. GOA combines insights from profile-guided optimization, superoptimization, evolutionary computation and mutational robustness. GOA searches for program variants that retain required functional behavior while improving non-functional behavior, using characteristic workloads and predictive modeling to guide the search. The resulting optimizations are validated using physical performance measurements and a larger held-out test suite. Our experimental results on PARSEC benchmark programs show average energy reductions of 20%, both for a large AMD system and a small Intel system, while maintaining program functionality on target workloads.

We present a method for automatically repairing arbitrary software defects in embedded systems, which have limited memory, disk and CPU capacities, but exist in great numbers. We extend evolutionary computation (EC) algorithms that search for valid repairs at the source code level to assembly and ELF format binaries, compensating for limited system resources with several algorithmic innovations. Our method does not require access to the source code or build toolchain of the software under repair, does not require program instrumentation, specialized execution environments, or virtual machines, or prior knowledge of the bug type.We repair defects in ARM and x86 assembly as well as ELF binaries, observing decreases of 86% in memory and 95% in disk requirements, with 62% decrease in repair time, compared to similar source-level techniques. These advances allow repairs previously possible only with C source code to be applied to any ARM or x86 assembly or ELF executable. Efficiency gains are achieved by introducing stochastic fault localization, with much lower overhead than comparable deterministic methods, and low-level program representations.When distributed over multiple devices, our algorithm finds repairs faster than predicted by naïve parallelism. Four devices using our approach are five times more efficient than a single device because of our collaboration model. The algorithm is implemented on Nokia N900 smartphones, with inter-phone communication fitting in 900 bytes sent in 7 SMS text messages per device per repair on average.

We present a method for automatically repairing arbitrary software defects in embedded systems, which have limited memory, disk and CPU capacities, but exist in great numbers. We extend evolutionary computation (EC) algorithms that search for valid repairs at the source code level to assembly and ELF format binaries, compensating for limited system resources with several algorithmic innovations. Our method does not require access to the source code or build toolchain of the software under repair, does not require program instrumentation, specialized execution environments, or virtual machines, or prior knowledge of the bug type. We repair defects in ARM and x86 assembly as well as ELF binaries, observing decreases of 86% in memory and 95% in disk requirements, with 62% decrease in repair time, compared to similar source-level techniques. These advances allow repairs previously possible only with C source code to be applied to any ARM or x86 assembly or ELF executable. Efficiency gains are achieved by introducing stochastic fault localization, with much lower overhead than comparable deterministic methods, and low-level program representations. When distributed over multiple devices, our algorithm finds repairs faster than predicted by naive parallelism. Four devices using our approach are five times more efficient than a single device because of our collaboration model. The algorithm is implemented on Nokia N900 smartphones, with inter-phone communication fitting in 900 bytes sent in 7 SMS text messages per device per repair on average.