Connecting Program Synthesis and Reachability: Automatic Program Repair Using Test-Input Generation

Nguyen, Thanh Vu; Weimer, Westley; Kapur, Deepak; Forrest, Stephanie

doi:10.1007/978-3-662-54577-5_17

Cited by 14 publications

(7 citation statements)

References 42 publications

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“…Both of these target normal testing rather than synthesis. Nguyen et al [19] leverage symbolic execution over input-output test pairs to perform program repair. However, they use these tests to model individual expressions instead of modeling entire functions.…”

Section: Related Workmentioning

confidence: 99%

Program Sketching by Automatically Generating Mocks from Tests

Bragg

Foster

Roux

et al. 2021

Computer Aided Verification

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Sketch is a popular program synthesis tool that solves for unknowns in a sketch or partial program. However, while Sketch is powerful, it does not directly support modular synthesis of dependencies, potentially limiting scalability. In this paper, we introduce Sketcham, a new technique that modularizes a regular sketch by automatically generating mocks—functions that approximate the behavior of complete implementations—from the sketch’s test suite. For example, if the function f originally calls g, Sketcham creates a mock $$g_m$$ g m from g’s tests and augments the sketch with a version of f that calls $$g_m$$ g m . This change allows the unknowns in f and g to be solved separately, enabling modular synthesis with no extra work from the Sketch user. We evaluated Sketcham on ten benchmarks, performing enough runs to show at a 95% confidence level that Sketcham improves median synthesis performance on six of our ten benchmarks by a factor of up to 5$$\times $$ × compared to plain Sketch, including one benchmark that times out on Sketch, while exhibiting similar performance on the remaining four. Our results show that Sketcham can achieve modular synthesis by automatically generating mocks from tests.

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Section: Related Workmentioning

confidence: 99%

Program Sketching by Automatically Generating Mocks from Tests

Bragg

Foster

Roux

et al. 2021

Computer Aided Verification

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“…In the literature there is also a wide range of techniques for automated program repair using formal methods [4,10,19,22,29,32,33,42]. Both [11] and [37] also use fault localization followed by applying mutations for repair.…”

Section: Related Workmentioning

confidence: 99%

Must Fault Localization for Program Repair

Rothenberg

Grumberg

2020

Lecture Notes in Computer Science

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This work is concerned with fault localization for automated program repair. We define a novel concept of a must location set. Intuitively, such a set includes at least one program location from every repair for a bug. Thus, it is impossible to fix the bug without changing at least one location from this set. A fault localization technique is considered a must algorithm if it returns a must location set for every buggy program and every bug in the program. We show that some traditional fault localization techniques are not must. We observe that the notion of must fault localization depends on the chosen repair scheme, which identifies the changes that can be applied to program statements as part of a repair. We develop a new algorithm for fault localization and prove that it is must with respect to commonly used schemes in automated program repair. We incorporate the new fault localization technique into an existing mutation-based program repair algorithm. We exploit it in order to prune the search space when a buggy mutated program has been generated. Our experiments show that must fault localization is able to significantly speed-up the repair process, without losing any of the potential repairs.

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“…Start is non-deterministic; it contains four calls to an external function nd(), which returns a non-deterministically chosen Boolean value. The calls to nd() can be understood as controlling whether or not a production is selected from G 2 during a top-down, left-to-right generation of an expression-tree: lines (3)-( 8) correspond to "Start ::= Plus(Start, Start)," and lines (10), (11), (12), and (13) correspond to "Start ::= x," "Start ::= y," "Start ::= 1," and "Start ::= 0," respectively. The code in the five cases in the body of Start encodes the semantics of the respective production of G 2 ; in particular, the statements that are executed along any execution path of P [G 2 , E 1 ] implement the bottom-up evaluation of some expression-tree that can be generated by G 2 .…”

Section: Illustrative Examplementioning

confidence: 99%

“…Synthesis as Reachability. CETI [12] introduces a technique for encoding template-based synthesis problems as reachability problems. The CETI encoding only applies to the specific setting in which (i) the search space is described by an imperative program with a finite number of holes-i.e., the values that the synthesizer has to discover-and (ii) the specification is given as a finite number of input-output test cases with which the target program should agree.…”

Section: Related Workmentioning

confidence: 99%

Proving Unrealizability for Syntax-Guided Synthesis

Breck

Cyphert

et al. 2019

Lecture Notes in Computer Science

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We consider the problem of automatically establishing that a given syntax-guided-synthesis (SyGuS) problem is unrealizable (i.e., has no solution). Existing techniques have quite limited ability to establish unrealizability for general SyGuS instances in which the grammar describing the search space contains infinitely many programs. By encoding the synthesis problem's grammar G as a nondeterministic program PG, we reduce the unrealizability problem to a reachability problem such that, if a standard program-analysis tool can establish that a certain assertion in PG always holds, then the synthesis problem is unrealizable.Our method can be used to augment existing SyGuS tools so that they can establish that a successfully synthesized program q is optimal with respect to some syntactic cost-e.g., q has the fewest possible if-thenelse operators. Using known techniques, grammar G can be transformed to generate the set of all programs with lower costs than q-e.g., fewer conditional expressions. Our algorithm can then be applied to show that the resulting synthesis problem is unrealizable. We implemented the proposed technique in a tool called nope. nope can prove unrealizability for 59/132 variants of existing linear-integer-arithmetic SyGuS benchmarks, whereas all existing SyGuS solvers lack the ability to prove that these benchmarks are unrealizable, and time out on them.1 Grammar G2 only generates terms that are equivalent to some linear function of x and y; however, the maximum function cannot be described by a linear function. 2 The synthesis problem presented above is one that is generated by a recent tool called QSyGuS, which extends SyGuS with quantitative syntactic objectives [10]. The advantage of using quantitative objectives in synthesis is that they can be used to produce higher-quality solutions-e.g., smaller, more readable, more efficient, etc. The synthesis problem (ψmax2(f, x, y), G2) arises from a QSyGuS problem in which the goal is to produce an expression that (i) satisfies the specification ψmax2(f, x, y), and (ii) uses the smallest possible number of if-then-else operators. Existing SyGuS solvers can easily produce a solution that uses one if-then-else operator, but cannot prove that no better solution exists-i.e., (ψmax2(f, x, y), G2) is unrealizable.

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Connecting Program Synthesis and Reachability: Automatic Program Repair Using Test-Input Generation

Cited by 14 publications

References 42 publications

Program Sketching by Automatically Generating Mocks from Tests

Program Sketching by Automatically Generating Mocks from Tests

Must Fault Localization for Program Repair

Proving Unrealizability for Syntax-Guided Synthesis

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