Oracle-guided component-based program synthesis

Jha, Susmit; Gulwani, Sumit; Seshia, Sanjit A.; Tiwari, Ashish

doi:10.1145/1806799.1806833

Cited by 414 publications

(393 citation statements)

References 22 publications

(19 reference statements)

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“…We briefly introduce the recent advance in component-based program synthesis [11]. Given a set of input-output pairs, component-based program synthesis generates a program that satisfies all the given input-output pairs.…”

Section: B Component-based Program Synthesismentioning

confidence: 99%

“…To solve the repair constraint C, we leverage the recent advance in component based program synthesis [11].…”

Section: B Generating a Fixmentioning

confidence: 99%

“…The third idea is to use componentbased synthesis idea [11] to synthesize an expression that conforms to the specification discovered before. The inter-play of the second and third steps is the primary novelty of our repair tool.…”

Section: Introductionmentioning

confidence: 99%

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SemFix: Program repair via semantic analysis

Nguyen¹,

Qi²,

Roychoudhury³

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

353

293

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Abstract-Debugging consumes significant time and effort in any major software development project. Moreover, even after the root cause of a bug is identified, fixing the bug is non-trivial. Given this situation, automated program repair methods are of value. In this paper, we present an automated repair method based on symbolic execution, constraint solving and program synthesis. In our approach, the requirement on the repaired code to pass a given set of tests is formulated as a constraint. Such a constraint is then solved by iterating over a layered space of repair expressions, layered by the complexity of the repair code. We compare our method with recently proposed genetic programming based repair on SIR programs with seeded bugs, as well as fragments of GNU Coreutils with real bugs. On these subjects, our approach reports a higher success-rate than genetic programming based repair, and produces a repair faster.

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Section: B Component-based Program Synthesismentioning

confidence: 99%

“…To solve the repair constraint C, we leverage the recent advance in component based program synthesis [11].…”

Section: B Generating a Fixmentioning

confidence: 99%

See 1 more Smart Citation

SemFix: Program repair via semantic analysis

Nguyen¹,

Qi²,

Roychoudhury³

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

353

293

View full text Add to dashboard Cite

show abstract

“…For example, in [30,36], non-trivial bit-manipulating program snippets are synthesized from specifications by using SMT solvers to search through all possible combinations of bit-level operations (instructions). Similar to the counterexample generation described above, the goal is often to synthesize the shortest programs, or ones with the smallest cost.…”

Section: Applications Of Optimizationmentioning

confidence: 99%

Symbolic optimization with SMT solvers

Albarghouthi

Kincaid

et al. 2014

Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

101

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The rise in efficiency of Satisfiability Modulo Theories (SMT) solvers has created numerous uses for them in software verification, program synthesis, functional programming, refinement types, etc. In all of these applications, SMT solvers are used for generating satisfying assignments (e.g., a witness for a bug) or proving unsatisfiability/validity (e.g., proving that a subtyping relation holds). We are often interested in finding not just an arbitrary satisfying assignment, but one that optimizes (minimizes/maximizes) certain criteria. For example, we might be interested in detecting program executions that maximize energy usage (performance bugs), or synthesizing short programs that do not make expensive API calls. Unfortunately, none of the available SMT solvers offer such optimization capabilities.In this paper, we present SYMBA, an efficient SMT-based optimization algorithm for objective functions in the theory of linear real arithmetic (LRA). Given a formula ϕ and an objective function t, SYMBA finds a satisfying assignment of ϕ that maximizes the value of t. SYMBA utilizes efficient SMT solvers as black boxes. As a result, it is easy to implement and it directly benefits from future advances in SMT solvers. Moreover, SYMBA can optimize a set of objective functions, reusing information between them to speed up the analysis. We have implemented SYMBA and evaluated it on a large number of optimization benchmarks drawn from program analysis tasks. Our results indicate the power and efficiency of SYMBA in comparison with competing approaches, and highlight the importance of its multi-objective-function feature.

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“…By combining the strengths of machine learning and formal verification, it is feasible to algorithmically synthesize programs from various flavors of formal and informal specifications, including logics, inputoutput examples, and a library of program templates. Conceptually, the synthesis technique [2] leverages inductive learning techniques as a student generalizing from sets of behaviors to candidate programs while deductive formal methods serve as a teacher rectifying generalizations to slowly guide the automated synthesis technique towards provably correct programs.…”

mentioning

confidence: 99%