Error Invariants for Concurrent Traces

Holzer, Andreas; Schwartz-Narbonne, Daniel; Befrouei, Mitra Tabaei; Weißenbacher, Georg; Wies, Thomas

doi:10.1007/978-3-319-48989-6_23

Cited by 5 publications

(6 citation statements)

References 34 publications

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“…The use of error invariants [7,13,18,29] is a closely-related fault-localization technique. Error invariants are computed from Craig interpolants along an error trace and capture which states will produce the error from that point on.…”

Section: Related Workmentioning

confidence: 99%

Semantic Fault Localization and Suspiciousness Ranking

Christakis

Heizmann

Mansur

et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

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Static program analyzers are increasingly effective in checking correctness properties of programs and reporting any errors found, often in the form of error traces. However, developers still spend a significant amount of time on debugging. This involves processing long error traces in an effort to localize a bug to a relatively small part of the program and to identify its cause. In this paper, we present a technique for automated fault localization that, given a program and an error trace, efficiently narrows down the cause of the error to a few statements. These statements are then ranked in terms of their suspiciousness. Our technique relies only on the semantics of the given program and does not require any test cases or user guidance. In experiments on a set of C benchmarks, we show that our technique is effective in quickly isolating the cause of error while out-performing other state-of-the-art faultlocalization techniques.

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Section: Related Workmentioning

confidence: 99%

Semantic Fault Localization and Suspiciousness Ranking

Christakis

Heizmann

Mansur

et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

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“…Other approaches for fault localization include spectrum-based (SBFL) [1,13,20,31,44], mutation-based (MBFL) [15,18,30,35] and formula-based (FBFL) [7,14,17,21,40]. Both SBFL and MBFL techniques compute the suspiciousness of a statement using coverage information from failing and passing test executions.…”

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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“…Interestingly, there are 338 problems that Vampire only solves using the (incomplete) strategy which blocks inferences mixing colors. 6 Our main focus in this experiment, however, is on comparing the first row to the second. We observe that also when restricted to local derivations AVATAR helps Vampire solving many problems, but there are slightly fewer newly solved problems (704 compared to 754) and slightly more problems which cannot be solved with AVATAR anymore (264 compared to 218).…”

Section: An Experimentsmentioning

confidence: 99%

“…In the context of computational logic, interpolation is an important operation with applications in formal verification, ranging from bounded model checking [16], invariant generation [10], and testing [12] to concurrency [6]. One of the state-of-the-art approaches to generating first-order interpolants is based on processing so called local proofs [7] and combining conclusions of color-eliminating inferences [8,5].…”

Section: Introductionmentioning

confidence: 99%