Mimic: computing models for opaque code

Heule, Stefan; Sridharan, Manu; Chandra, Satish

doi:10.1145/2786805.2786875

Cited by 24 publications

(23 citation statements)

References 17 publications

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“…Past efforts in program synthesis have been quite diverse. Some approaches search through a space of programs using brute force [Bar-David and Taubenfeld 2003;Katayama 2005], MCMC search [Heule et al 2015;], genetic programming [Weimer et al 2009], or neural networks [Balog et al 2017;Devlin et al 2017]. Others use constraint-solving [Feng et al 2017a,b;Gulwani et al 2011a;Jha et al 2010;Srivastava et al 2010] or version space algebras [Gulwani 2011;Lau et al 2000].…”

Section: Related Workmentioning

confidence: 99%

“…As noted in Section 1, many component-based synthesizers cannot handle control structures, especially loops [Feng et al 2017b;Gulwani et al 2011a;Jha et al 2010;Menon et al 2013]. Beyond component-based synthesis, some approaches in the wider program synthesis literature do handle loops [Bar-David and Taubenfeld 2003;Barthe et al 2013;Heule et al 2015;Qi et al 2012;Srivastava et al 2010], but many do not [Alur et al 2017;Balog et al 2017;Devlin et al 2017;Lau et al 2000;]. Hence, handling combinations of control structures is a distinguishing aspect of FrAngel.…”

Section: Synthesis Techniquesmentioning

confidence: 99%

“…This provides a decomposition of the synthesis task, ultimately allowing FrAngel to synthesize programs using combinations of loops and conditionals. The concept of leaving a control structure condition unspecified has been applied to if and switch statements by several other works [Alur et al 2017;Gulwani 2011;Heule et al 2015;Yaghmazadeh et al 2016]. This is often presented as learning several programs and then learning the classifier that chooses which program to use for a given input.…”

Section: Synthesis Techniquesmentioning

confidence: 99%

“…We focus on synthesis from examples, where the user provides input-output examples that the synthesized program must satisfy. Although the desired program behavior can be specified with logical constraints [Gulwani et al 2011a;Srivastava et al 2010] or executables [Heule et al 2015;Jha et al 2010 We also identify and discuss a common property of programs called special-case similarity, hypothesizing that code fragments from partially-successful programs often appear in the desired program, which can justify why this learning approach works well in practice.…”

Section: Introductionmentioning

confidence: 99%

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FrAngel: component-based synthesis with control structures

Shi

Steinhardt

Liang

2019

Proc. ACM Program. Lang.

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In component-based program synthesis, the synthesizer generates a program given a library of components (functions). Existing component-based synthesizers have difficulty synthesizing loops and other control structures, and they often require formal specifications of the components, which can be expensive to generate. We present FrAngel, a new approach to component-based synthesis that can synthesize short Java functions with control structures when given a desired signature, a set of input-output examples, and a collection of libraries (without formal specifications). FrAngel aims to discover programs with many distinct behaviors by combining two main ideas. First, it mines code fragments from partially-successful programs that only pass some of the examples. These extracted fragments are often useful for synthesis due to a property that we call special-case similarity. Second, FrAngel uses angelic conditions as placeholders for control structure conditions and optimistically evaluates the resulting program sketches. Angelic conditions decompose the synthesis process: FrAngel first finds promising partial programs and later fills in their missing conditions. We demonstrate that FrAngel can synthesize a variety of interesting programs with combinations of control structures within seconds, significantly outperforming prior state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

Section: Synthesis Techniquesmentioning

confidence: 99%

Section: Synthesis Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FrAngel: component-based synthesis with control structures

Shi

Steinhardt

Liang

2019

Proc. ACM Program. Lang.

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“…Mimic [15] aims to synthesize models that perform the same computations as opaque or obfuscated Javascript code. Mimic uses random search inspired by machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing framework models for symbolic execution

Jeon

Qiu

Fetter-Degges

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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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.

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Automatic Modeling of Opaque Code for JavaScript Static Analysis

Park

Jordan²,

Ryu

2019

Fundamental Approaches to Software Engineering

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Static program analysis often encounters problems in analyzing library code. Most real-world programs use library functions intensively, and library functions are usually written in different languages. For example, static analysis of JavaScript programs requires analysis of the standard built-in library implemented in host environments. A common approach to analyze such opaque code is for analysis developers to build models that provide the semantics of the code. Models can be built either manually, which is time consuming and error prone, or automatically, which may limit application to different languages or analyzers. In this paper, we present a novel mechanism to support automatic modeling of opaque code, which is applicable to various languages and analyzers. For a given static analysis, our approach automatically computes analysis results of opaque code via dynamic testing during static analysis. By using testing techniques, the mechanism does not guarantee sound over-approximation of program behaviors in general. However, it is fully automatic, is scalable in terms of the size of opaque code, and provides more precise results than conventional over-approximation approaches. Our evaluation shows that although not all functionalities in opaque code can (or should) be modeled automatically using our technique, a large number of JavaScript built-in functions are approximated soundly yet more precisely than existing manual models.

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Mimic: computing models for opaque code

Cited by 24 publications

References 17 publications

FrAngel: component-based synthesis with control structures

FrAngel: component-based synthesis with control structures

Synthesizing framework models for symbolic execution

Automatic Modeling of Opaque Code for JavaScript Static Analysis

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