FrAngel: component-based synthesis with control structures

Shi, Kensen; Steinhardt, Jacob; Liang, Percy

doi:10.1145/3290386

Cited by 43 publications

(26 citation statements)

References 36 publications

(76 reference statements)

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“…User intent can be described via a set of input/output examples, for example in [94], or even as a set of test cases, for example, in the work presented in [92]. In addition, the FrAngel approach [178] is able to synthesize loops and other control structures using a desired signature.…”

Section: Inductive Program Synthesis Paradigmmentioning

confidence: 99%

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Subahi

2020

Computers

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Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.

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Section: Inductive Program Synthesis Paradigmmentioning

confidence: 99%

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Subahi

2020

Computers

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“…It also adopts several optimizing strategies to promote the performance of it, which is not discussed in this paper. If interested, please refer to FrAngel paper [34].…”

Section: Basic Synthesis Procedures Of Frangelmentioning

confidence: 99%

“…It have widely been used in many domain-specific applications including bit-vector programs [16], geometry constructions [17], deobfuscating [20], string manipulation [31], data structure transformation [10], and table transformation [8], etc. Unlike these work, our work and SyPet [9] as well as FrAngel [34] are designed for synthesizing general Java programs with various intents, and the components here refer to Java APIs. Program Sketching is a synthesis methodology where the intent of a programming task is given by a program sketch and leave the rest of the implementation to the synthesis procedure [37][38][39].…”

Section: Related Work 61 Program Synthesismentioning

confidence: 99%

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Boosting component-based synthesis with control structure recommendation

Liu

Dong

Zhang

et al. 2020

Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Langu

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Component-based synthesis is an important research field in program synthesis. API-based synthesis is a subfield of componentbased synthesis, the component library of which are Java APIs. Unlike existing work in API-based synthesis that can only generate loop-free programs constituted by APIs, state-of-the-art work FrAngel can generate programs with control structures. However, for the generation of control structures, it samples different types of control structures all at random. Given the information about the desired method (such as method name and input/output types), experienced programmers can have an initial thought about the possible control structures that could be used in implementing the desired method. The knowledge about control structures in the method can be learned from high-quality projects. In this paper, we propose a novel approach of recommending control structures for API-based synthesis based on deep learning. A neural network that can jointly embed the natural language description, method name, and input/output types into high-dimensional vectors to predict the possible control structures of the desired method is proposed. We integrate the prediction model into the synthesizer to improve the efficiency of synthesis. We train our model on a codebase of high-quality Java projects from GitHub. The prediction results of the neural network are fed to the API-based synthesizer to guide the sampling process of control structures. The experimental results on 40 programming tasks show that our approach can effectively improve the efficiency of synthesis.

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“…Sypet [Feng et al 2017] is similar, but also includes I/O examples as a harness. FrAngel [Shi et al 2019] builds on SyPet, using angelic execution to find programs that satisfy test cases and then instantiating the angelic control-structure conditions for those programs. Unlike JLibSketch, none of these systems support template-based synthesis, nor do they support algebraic library specifications.…”

Section: Related Workmentioning

confidence: 99%

Program synthesis with algebraic library specifications

Mariano

Reese

et al. 2019

Proc. ACM Program. Lang.

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A key challenge in program synthesis is synthesizing programs that use libraries, which most real-world software does. The current state of the art is to model libraries with mock library implementations that perform the same function in a simpler way. However, mocks may still be large and complex, and must include many implementation details, both of which could limit synthesis performance. To address this problem, we introduce JLibSketch, a Java program synthesis tool that allows library behavior to be described with algebraic specifications, which are rewrite rules for sequences of method calls, e.g., encryption followed by decryption (with the same key) is the identity. JLibSketch implements rewrite rules by compiling JLibSketch problems into problems for the Sketch program synthesis tool. More specifically, after compilation, library calls are represented by abstract data types (ADTs), and rewrite rules manipulate those ADTs. We formalize compilation and prove it sound and complete if the rewrite rules are ordered and non-unifiable. We evaluated JLibSketch by using it to synthesize nine programs that use libraries from three domains: data structures, cryptography, and file systems. We found that algebraic specifications are, on average, about half the size of mocks. We also found that algebraic specifications perform better than mocks on seven of the nine programs, sometimes significantly so, and perform equally well on the last two programs. Thus, we believe that JLibSketch takes an important step toward synthesis of programs that use libraries. CCS Concepts: • Software and its engineering → Automatic programming; Source code generation.

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FrAngel: component-based synthesis with control structures

Cited by 43 publications

References 36 publications

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Boosting component-based synthesis with control structure recommendation

Program synthesis with algebraic library specifications

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