MUT-APR: MUTation-Based Automated Program Repair Research Tool

Assiri, Fatmah Yousef; Bieman, James M.

doi:10.1007/978-3-030-03405-4_17

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…But, unlike this work, fault localization is applied only to the original program. The tool MUT-APR [5] fixes binary operator faults in C programs, but only targets faults that require one line modification. The tools FORENSIC [7] and MAPLE [43] repair C programs with respect to a formal specification, but they do so by replacing expressions with templates, which are then patched and analysed.…”

Section: Related Workmentioning

confidence: 99%

Automated Program Repair Using Formal Verification Techniques

Frenkel

Grumberg

Rothenberg

et al. 2022

Lecture Notes in Computer Science

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We focus on two different approaches to automatic program repair, based on formal verification methods. Both repair techniques consider infinitestate C-like programs, and consist of a generate-validate loop, in which potentially repaired programs are repeatedly generated and verified. Both approaches are incremental -partial information gathered in previous verification attempts is used in the next steps. However, the settings of both approaches, including their techniques for finding repairs, are quite distinct. The first approach uses syntactic mutations to repair sequential programs with respect to assertions in the code. It is based on a reduction to the problem of finding unsatisfiable sets of constraints, which is addressed using an interplay between SAT and SMT solvers. A novel notion of must-fault-localization enables efficient pruning of the search space, without losing any potential repair. The second approach uses an Assume-Guarantee (AG) style reasoning in order to verify large programs, composed of two concurrent components. The AG reasoning is based on automata-learning techniques. When verification fails, the procedure repeatedly repairs one of the components, until a correct repair is found. Several different repair methods are considered, trading off precision and convergence to a correct repair.

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Section: Related Workmentioning

confidence: 99%

Automated Program Repair Using Formal Verification Techniques

Frenkel

Grumberg

Rothenberg

et al. 2022

Lecture Notes in Computer Science

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show abstract

“…2 In this paper, we focus on two repair schemes that are frequently used for automated program repair: the arbitrary scheme (S arb ) and the mutation scheme (S mut ). Both schemes only manipulate program expressions, but the mutation scheme is more restrictive than the arbitrary scheme: S arb (st) is the set of all options to replace the expression of st 3 with an arbitrary expression, while S mut (st) only contains statements where the expression in st is mutated according to a set of simple syntactic rules. The rules we consider are replacing a + operator with aoperator, and vice versa, replacing a < operator with a > operator, and vice versa, and increasing or decreasing a numerical constant by 1.…”

Section: Must Fault Localizationmentioning

confidence: 99%

“…Also, neither the Tarantula fault localization used in [ 11 ] nor the dynamic slicing used in [ 37 ] carries the guarantee of being a must fault localization. The tool MUT-APR [ 3 ] fixes binary operator faults in C programs, but only targets faults that require one line modification. The tools FoREnSiC [ 5 ] and Maple [ 34 ] repair C programs with respect to a formal specification, but they do so by replacing expressions with templates, which are then patched and analysed.…”

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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MUT-APR: MUTation-Based Automated Program Repair Research Tool

Cited by 2 publications

References 36 publications

Automated Program Repair Using Formal Verification Techniques

Automated Program Repair Using Formal Verification Techniques

Must Fault Localization for Program Repair

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