Flow-Sensitive Fault Localization

Christ, Jürgen; Ermis, Evren; Schäf, Martin; Wies, Thomas

doi:10.1007/978-3-642-35873-9_13

Cited by 30 publications

(50 citation statements)

References 21 publications

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“…We have provided an algorithm that automatically computes such automata for a given inconsistent program. This work can be seen as a generalization of the fault localization technique that we presented in [8]. In particular, error invariant automata can also be used for fault localization on a single error trace and thus provide a general tool to assist programmers in debugging.…”

Section: Resultsmentioning

confidence: 99%

“…Static fault localization tools such as Bug-Assist [26], and our approach based on error invariants [8,15], do fault localization for individual paths. In our example, we have two paths that connect line 6 and line 27 due to the conditional choice in line 16.…”

Section: Examplementioning

confidence: 99%

“…From this program fragment, we construct a first-order logic formula which is unsatisfiable because of the inconsistency in the original program. This encoding is similar to the extended path formulas shown in [8], however our approach encodes multiple paths into one formula.…”

Section: Error Invariant Automatamentioning

confidence: 99%

“…In our previous work on error invariants [8,15], we localize errors by computing Craig interpolants from symbolic representations of error traces. If the computed interpolants are inductive for a portion of the error trace, then that part of the trace is irrelevant for the error and can be abstracted.…”

Section: Related Workmentioning

confidence: 99%

“…Intuitively, an error invariant is a formula describing the reachable states at a given program location and it captures the reason why the program does not terminate normally if execution is continued from these states. Our algorithm builds on our previous work on fault localization for single error traces [8,15] in which we use Craig interpolation [10] to extract error invariants automatically from a symbolic encoding of the error trace.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Explaining inconsistent code

Schäf¹,

Schwartz-Narbonne

Wies

2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

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A code fragment is inconsistent if it is not part of any normally terminating execution. Examples of such inconsistencies include code that is unreachable, code that always fails due to a run-time error, and code that makes conflicting assumptions about the program state. In this paper, we consider the problem of automatically explaining inconsistent code. This problem is difficult because traditional fault localization techniques do not apply. Our solution relies on a novel algorithm that takes an infeasible code fragment as input and generates a so-called error invariant automaton. The error invariant automaton is an abstraction of the input code fragment that only mentions program statements and facts that are relevant for understanding the cause of the inconsistency. We conducted a preliminary usability study which demonstrated that error invariant automata can help programmers better understand inconsistencies in code taken from real-world programs. In particular, access to an error invariant automata tripled the speed at which programmers could diagnose the cause of a code inconsistency.

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Section: Resultsmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Error Invariant Automatamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Explaining inconsistent code

Schäf¹,

Schwartz-Narbonne

Wies

2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

Self Cite

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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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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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Flow-Sensitive Fault Localization

Cited by 30 publications

References 21 publications

Explaining inconsistent code

Explaining inconsistent code

Semantic Fault Localization and Suspiciousness Ranking

Must Fault Localization for Program Repair

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