Synthesis of loop-free programs

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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“…al. [16], program synthesis is more efficient than enumeration on all possible compositions of the components.…”

Section: B Generating a Fixmentioning

confidence: 99%

SemFix: Program repair via semantic analysis

Nguyen¹,

Qi²,

Roychoudhury³

et al. 2013

2013 35th International Conference on Software Engineering (ICSE)

365

293

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“…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%

“…To mention a few, in verification, SMT solvers have been used for device driver verification [5,34], checking complex verification conditions [22,38,48], and improving precision of invariant generation [33,45]; in testing and bug finding, they have been instrumental in making symbolic execution [15,26], fuzzing [28], and bounded model checking techniques [18,24] practical; in program synthesis, they have been used to search for programs satisfying a given specification [30,58]; in functional programming, they have been used to support strong typing guarantees with refinement types [12,52].…”

Section: Introductionmentioning

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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“…Over the last few years, constraint-based approaches to synthesis based on sketches or templates have become quite popular [11,19,26,28,29]. In these approaches, the user specifies her intent with a template of the desired solution leaving parts of the code unspecified (a sketch), and the synthesizer finds the unknown code fragments such that the completed template conforms to a given set of behavioral constraints (the spec).…”

Section: Introductionmentioning

confidence: 99%

“…This problem in principle can be solved by the traditional Cegis [24] algorithm but Cegis performs poorly in practice. We present a new algorithm Cegis+ that combines the Cegis algorithm with an approach that selectively uses existential quantification for some inputs [11] for efficiently solving this problem. The sketch will use the function model in place of a more complex function that itself may have unknowns.…”

Section: Introductionmentioning

confidence: 99%

Modular Synthesis of Sketches Using Models

Singh

et al. 2014

Lecture Notes in Computer Science

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Abstract. One problem with the constraint-based approaches to synthesis that have become popular over the last few years is that they only scale to relatively small routines, on the order of a few dozen lines of code. This paper presents a mechanism for modular reasoning that allows us to break larger synthesis problems into small manageable pieces. The approach builds on previous work in the verification community of using high-level specifications and partially interpreted functions (we call them models) in place of more complex pieces of code in order to make the analysis modular. The main contribution of this paper is to show how to combine these techniques with the counterexample guided synthesis approaches used to efficiently solve synthesis problems. Specifically, we show two new algorithms; one to efficiently synthesize functions that use models, and another one to synthesize functions while ensuring that the behavior of the resulting function will be in the set of behaviors allowed by the model. We have implemented our approach on top of the open-source Sketch synthesis system, and we demonstrate its effectiveness on several Sketch benchmark problems.

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Synthesis of loop-free programs

Cited by 35 publications

References 0 publications

SemFix: Program repair via semantic analysis

SemFix: Program repair via semantic analysis

Symbolic optimization with SMT solvers

Modular Synthesis of Sketches Using Models

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