Data-driven synthesis for object-oriented frameworks

Yessenov, Kuat; Xu, Zhilei; Solar-Lezama, Armando

doi:10.1145/2048066.2048075

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…Another tool that is related to automated API completion is MATCHMAKER, which synthesizes "glue code" to allow framework classes to interact with each other [48]. Unlike SYPET where the query is a method signature, MATCHMAKER queries are of the form "How can I get type A and type B to interact with each other?"…”

Section: Related Workmentioning

confidence: 99%

Component-based synthesis for complex APIs

Martins

Wang

et al. 2017

SIGPLAN Not.

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Component-based approaches to program synthesis assemble programs from a database of existing components, such as methods provided by an API. In this paper, we present a novel type-directed algorithm for component-based synthesis. The key novelty of our approach is the use of a compact Petri-net representation to model relationships between methods in an API. Given a target method signature S, our approach performs reachability analysis on the underlying Petri-net model to identify sequences of method calls that could be used to synthesize an implementation of S. The programs synthesized by our algorithm are guaranteed to type check and pass all test cases provided by the user. We have implemented this approach in a tool called SYPET, and used it to successfully synthesize real-world programming tasks extracted from on-line forums and existing code repositories. We also compare SYPET with two state-of-the-art synthesis tools, namely INSYNTH and CODEHINT, and demonstrate that SYPET can synthesize more programs in less time. Finally, we compare our approach with an alternative solution based on hypergraphs and demonstrate its advantages.

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Section: Related Workmentioning

confidence: 99%

Component-based synthesis for complex APIs

Martins

Wang

et al. 2017

SIGPLAN Not.

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“…To find the most likely expressions desired by the programmer, these approaches also rely on ranking algorithms to handle the large numbers of potential candidates. As opposed to these (static) approaches, MatchMaker [42] synthesizes code based on observed API usage in dynamic executions of real-world programs. Code Search and Specification Mining There has been a lot of work on dynamic specification mining (e.g., [8,11,41]), most of it for extracting various forms of temporal specifications.…”

Section: Related Workmentioning

confidence: 99%

Code completion with statistical language models

Raychev

Vechev

Yahav

2014

Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation

351

156

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We address the problem of synthesizing code completions for programs using APIs. Given a program with holes, we synthesize completions for holes with the most likely sequences of method calls.Our main idea is to reduce the problem of code completion to a natural-language processing problem of predicting probabilities of sentences. We design a simple and scalable static analysis that extracts sequences of method calls from a large codebase, and index these into a statistical language model. We then employ the language model to find the highest ranked sentences, and use them to synthesize a code completion. Our approach is able to synthesize sequences of calls across multiple objects together with their arguments.Experiments show that our approach is fast and effective. Virtually all computed completions typecheck, and the desired completion appears in the top 3 results in 90% of the cases.

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“…Contributions The ability of DEMOMATCH to discover the setup code necessary to use a framework by demonstrating its functionality goes beyond what has been done before both in the fields of API discovery and programming by demonstration. Recent work on API discovery requires the programmer to provide either a desired type or a partial expression, both of which are hard to provide without some prior knowledge of the API [7,11,17,37]. On the other hand, recent work on programming by demonstration relies on careful design of a restricted language for possible programs as well as domain specific search procedures [9,19,38].…”

Section: Conf 'Yy Month D-d 20yy City St Country C 20yymentioning

confidence: 99%

DemoMatch: API discovery from demonstrations

2017

Self Cite

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We introduce DEMOMATCH, a tool for API discovery that allows the user to discover how to implement functionality using a software framework by demonstrating the functionality in existing applications built with the same framework. DEMOMATCH matches the demonstrations against a database of execution traces called SEMERU and generates code snippets explaining how to use the functionality. We evaluated DEMOMATCH on several case studies involving Java Swing and Eclipse RCP. CCS Concepts • Software and its engineering → Automatic programming; Object oriented frameworks; • Humancentered computing → User interface programming

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Data-driven synthesis for object-oriented frameworks

Cited by 18 publications

References 20 publications

Component-based synthesis for complex APIs

Component-based synthesis for complex APIs

Code completion with statistical language models

DemoMatch: API discovery from demonstrations

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