Bootstrapping Library-Based Synthesis

Huang, Kangjing; Qiu, Xiaokang

doi:10.1007/978-3-031-22308-2_13

Static Analysis

2022

DOI: 10.1007/978-3-031-22308-2_13

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Bootstrapping Library-Based Synthesis

Kangjing Huang¹,

Xiaokang Qiu²

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2024

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Cited by 3 publications

References 37 publications

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Semantic Code Refactoring for Abstract Data Types

Pailoor,

Wang,

Dillig

2024

Proc. ACM Program. Lang.

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Modifications to the data representation of an abstract data type (ADT) can require significant semantic refactoring of the code. Motivated by this observation, this paper presents a new method to automate semantic code refactoring tasks. Our method takes as input the original ADT implementation, a new data representation, and a so-called relational representation invariant (relating the old and new data representations), and automatically generates a new ADT implementation that is semantically equivalent to the original version. Our method is based on counterexample-guided inductive synthesis (CEGIS) but leverages three key ideas that allow it to handle real-world refactoring tasks. First, our approach reduces the underlying relational synthesis problem to a set of (simpler) programming-by-example problems, one for each method in the ADT. Second, it leverages symbolic reasoning techniques, based on logical abduction, to deduce code snippets that should occur in the refactored version. Finally, it utilizes a notion of partial equivalence to make inductive synthesis much more effective in this setting. We have implemented the proposed approach in a new tool called Revamp ‍ for automatically refactoring Java classes and evaluated it on 30 Java class mined from Github. Our evaluation shows that Revamp can correctly refactor the entire ADT in 97% of the cases and that it can successfully re-implement 144 out of the 146 methods that require modifications.

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Semantic Code Refactoring for Abstract Data Types

Pailoor,

Wang,

Dillig

2024

Proc. ACM Program. Lang.

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Decomposition-based Synthesis for Applying Divide-and-Conquer-like Algorithmic Paradigms

Ji,

Zhao,

Xiong

et al. 2024

ACM Trans. Program. Lang. Syst.

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Algorithmic paradigms such as divide-and-conquer (D&C) are proposed to guide developers in designing efficient algorithms, but it can still be difficult to apply algorithmic paradigms to practical tasks. To ease the usage of paradigms, many research efforts have been devoted to the automatic application of algorithmic paradigms. However, most existing approaches to this problem rely on syntax-based program transformations and thus put significant restrictions on the original program. In this paper, we study the automatic application of D&C and several similar paradigms, denoted as D&C-like algorithmic paradigms, and aim to remove the restrictions from syntax-based transformations. To achieve this goal, we propose an efficient synthesizer, named AutoLifter , which does not depend on syntax-based transformations. Specifically, the main challenge of applying algorithmic paradigms is from the large scale of the synthesized programs, and AutoLifter addresses this challenge by applying two novel decomposition methods that do not depend on the syntax of the input program, component elimination and variable elimination , to soundly divide the whole problem into simpler subtasks, each synthesizing a sub-program of the final program and being tractable with existing synthesizers. We evaluate AutoLifter on 96 programming tasks related to 6 different algorithmic paradigms. AutoLifter solves 82/96 tasks with an average time cost of 20.17 seconds, significantly outperforming existing approaches.

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Superfusion: Eliminating Intermediate Data Structures via Inductive Synthesis

Ji,

Zhao,

Polikarpova

et al. 2024

Proc. ACM Program. Lang.

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Intermediate data structures are a common cause of inefficiency in functional programming. Fusion attempts to eliminate intermediate data structures by combining adjacent data traversals into one; existing fusion techniques, however, are based on predefined rewrite rules and hence are limited in expressiveness. In this work we explore a different approach to eliminating intermediate data structures, based on inductive program synthesis. We dub this approach superfusion (by analogy with superoptimization , which uses inductive synthesis for program optimization). Starting from a reference program annotated with data structures to be eliminated, superfusion first generates a sketch where program fragments operating on those data structures are replaced with holes; it then fills the holes with constant-time expressions such that the resulting program is equivalent to the reference. The main technical challenge here is scalability because optimized programs are often complex, making the search space intractably large for naive enumeration. To address this challenge, our key insight is to first synthesize a ghost function that describes the relationship between the original intermediate data structure and its compressed version; this function, although not used in the final program, serves to decompose the joint sketch filling problem into independent simpler problems for each hole. We implement superfusion in a tool called SuFu and evaluate it on a dataset of 290 tasks collected from prior work on deductive fusion and program restructuring. The results show that SuFu solves 264 out of 290 tasks, exceeding the capabilities of rewriting-based fusion systems and achieving comparable performance with specialized approaches to program restructuring on their respective domains.

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Bootstrapping Library-Based Synthesis

Cited by 3 publications

References 37 publications

Semantic Code Refactoring for Abstract Data Types

Semantic Code Refactoring for Abstract Data Types

Decomposition-based Synthesis for Applying Divide-and-Conquer-like Algorithmic Paradigms

Superfusion: Eliminating Intermediate Data Structures via Inductive Synthesis

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