Program Synthesis for Program Analysis

David, Cristina; Kesseli, Pascal; Kroening, Daniel; Lewis, Matt

doi:10.1145/3174802

Cited by 9 publications

(6 citation statements)

References 80 publications

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“…The interpreter loops through all the instructions in the proдram (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. If the skip instruction flag is set, the interpreter skips the next instruction and resets the flag (lines 5-8).…”

Section: Vocabularymentioning

confidence: 99%

“…Lines 10-15 attempt to prove the Original function and program have the same effect on a fresh symbolic string of up to length max_ex_size on all possible paths. To be able to reason about all these paths at once, we merge them using StartMerge() and End-Merge() (lines [12][13][14][15][16].…”

Section: Counterexample-guided Synthesismentioning

confidence: 99%

“…Understanding the influence of the vocabulary and choosing the best possible vocabulary is then equivalent to exploring the behaviour of s. While we could exhaustively evaluate s, it would take a long time, as we would need to 2 13 = 8192 experiments. Therefore, we use instead Gaussian Processes [29] to effectively explore s.…”

Section: Optimizing the Vocabularymentioning

confidence: 99%

See 2 more Smart Citations

Computing summaries of string loops in C for better testing and refactoring

Kapus

Ish-Shalom

Itzhaky

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Analysing and comprehending C programs that use strings is hard: Using standard library functions for manipulating strings is not enforced and programs often use complex loops for the same purpose. We introduce the notion of memoryless loops that capture some of these string loops and present a counterexample-guided inductive synthesis approach to summarise memoryless string loops using C standard library functions, which has applications to testing, optimization and refactoring.We prove our summarization is correct for arbitrary input strings and evaluate it on a database of loops we gathered from a set of 13 open-source programs. Our approach can summarize over two thirds of memoryless loops in less than 5 minutes of computation time per loop. We then show that these summaries can be used to (1) enhance symbolic execution testing, where we observed median speedups of 79x when employing a string constraint solver, (2) optimize native code, where certain summarizations led to significant performance gains, and (3) refactor code, where we had several patches accepted in the codebases of popular applications such as patch and wget.

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Section: Vocabularymentioning

confidence: 99%

Section: Counterexample-guided Synthesismentioning

confidence: 99%

See 1 more Smart Citation

Computing summaries of string loops in C for better testing and refactoring

Kapus

Ish-Shalom

Itzhaky

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

View full text Add to dashboard Cite

show abstract

“…Further developments in synthesis typically fall into one of two categories. The first comprises innovative search algorithms to search the space more efficiently; for instance, genetic algorithms [16], reinforcement learning [28], or partitioning the search space in creative ways [6]. The second category comprises extensions to the communication paradigm permitted between the synthesis and the verification phase.…”

Section: Introductionmentioning

confidence: 99%

Satisfiability and Synthesis Modulo Oracles

Polgreen

Reynolds

Seshia

2022

Lecture Notes in Computer Science

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In classic program synthesis algorithms, such as counterexample-guided inductive synthesis (CEGIS), the algorithms alternate between a synthesis phase and an oracle (verification) phase. Many synthesis algorithms use a white-box oracle based on satisfiability modulo theory (SMT) solvers to provide counterexamples. But what if a white-box oracle is either not available or not easy to work with? We present a framework for solving a general class of oracle-guided synthesis problems which we term synthesis modulo oracles. In this setting, oracles may be black boxes with a query-response interface defined by the synthesis problem. As a necessary component of this framework, we also formalize the problem of satisfiability modulo theories and oracles, and present an algorithm for solving this problem. We implement a prototype solver for satisfiability and synthesis modulo oracles and demonstrate that, by using oracles that execute functions not easily modeled in SMT-constraints, such as recursive functions or oracles that incorporate compilation and execution of code, SMTO and SyMO are able to solve problems beyond the abilities of standard SMT and synthesis solvers.

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“…Synthesizers, on the other hand, can accept various types of high-level specifications, such as logic formula, domain-specific language, grammar, natural language, and even partial program source code. Instead of applying direct translation into low-level executable code, a suitable search technique is applied over some space [2] in order to achieve the solution language (final code) or proof (logical…”

Section: Introductionmentioning

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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Program Synthesis for Program Analysis

Cited by 9 publications

References 80 publications

Computing summaries of string loops in C for better testing and refactoring

Computing summaries of string loops in C for better testing and refactoring

Satisfiability and Synthesis Modulo Oracles

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

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