On Incremental Core-Guided MaxSAT Solving

Si, Xujie; Zhang, Xin; Manquinho, Vasco; Janota, Mikoláš; Ignatiev, Alexey; Naik, Mayur

doi:10.1007/978-3-319-44953-1_30

Cited by 5 publications

(6 citation statements)

References 24 publications

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“…This is the approach used by most applications [17,39,40,71]. Next we show that by leveraging the sequential property, incremental core-guided MaxSAT solving [61] can solve the sequential MaxSAT problem more efficiently. The unsat core-guided MaxSAT algorithm, also known as Fu & Malik algorithm [24], forms the basis of many popular MaxSAT algorithms [4,42,44,47].…”

Section: Optimizationsmentioning

confidence: 88%

Maximum Satisfiability in Software Analysis: Applications and Techniques

Zhang

Grigore

et al. 2017

Computer Aided Verification

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Section: Optimizationsmentioning

confidence: 88%

Maximum Satisfiability in Software Analysis: Applications and Techniques

Zhang

Grigore

et al. 2017

Computer Aided Verification

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“…As inherited from jDart, AgxFaults also uses the constraints library jConstraints [26] as an abstraction layer for constraint solvers. Since the jConstraint does not support solving pMaxSMT/MaxSMT and sequential MaxSMT problems, we implemented the Fu & Malik's core-guided max-sat algorithm [27] and the incremental core-guided MaxSMT solving algorithm [20] into the jConstraint library. We use the Z3 (https://github.com/Z3Prover/z3) SMT solver as our back-end constraint solver.…”

Section: Methodsmentioning

confidence: 99%

“…After each iteration of the Angelic Refinement Loop, a new set of constraints are added into the angelic formula. Instead of considering each angelic formula after each iteration as an independent max-sat problem and invoking a Max-SAT solver to find MCS, we consider all generated angelic formula so far as a sequence of a similar max-sat problem, i.e., an instance of a sequential maximum satisfiability problem [20]. The Formula Solver uses a sequential max-sat solver to compute MCS of the angelic formula incrementally.…”

Section: Incremental Formula Solvermentioning

confidence: 99%

“…This encoding results in compact error trace formulas that are easier to solve but that are still sufficient to identify both data and control-related faults. Second, because the angelic formula is incremented dynamically, instead of multiple calls to a MaxSMT solver to solve multiple formulas separately (like OFC approach), we adapt the incremental core-guide MaxSMT algorithm [20] to manipulate the formula and compute the MCS incrementally.…”

Section: Introductionmentioning

confidence: 99%

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Incremental Formula-Based Fix Localization

Phung

Lee

2020

Applied Sciences

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Automatically fixing bugs in software programs can significantly reduce the cost and improve the productivity of the software. Toward this goal, a critical and challenging problem is automatic fix localization, which identifies program locations where a bug fix can be synthesized. In this paper, we present AgxFaults, a technique that automatically identifies minimal subsets of program statements at which a suitable modification can remove the error. AgxFaults works based on dynamically encoding semantic of program parts that are relevant to an observed error into an unsatisfiable logical formula and then manipulating this formula in an increasingly on-demand manner. We perform various experiments on faulty versions of the traffic collision avoidance system (TCAS) program in the Siemens Suite, programs in Bekkouche’s benchmark, and server real bugs in the Defects4J benchmark. The experimental results show that AgxFaults outperforms single-path-formula approaches in terms of effectiveness in finding fix localization and fault localization. AgxFaults is better than program-formula-based approaches in terms of efficiency and scalability, while providing similar effectiveness. Specifically, the solving time of AgxFaults is 28% faster, and the running time is 45% faster, than the program-formula-based approach, while providing similar fault localization results.

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“…To further speed up the solving phase, we observe that the above grounding algorithm is solving a sequence of similar WPMS prob-lems, where each problem is a subset of its immediate successor. By exploiting this insight, we have developed incremental solving which speeds up WMPS solving by reusing past results [39] .…”

Section: Learning and Inferencementioning

confidence: 99%

Combining the logical and the probabilistic in program analysis

Zhang

Naik

2017

Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages

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Conventional program analyses have made great strides by leveraging logical reasoning. However, they cannot handle uncertain knowledge, and they lack the ability to learn and adapt. This in turn hinders the accuracy, scalability, and usability of program analysis tools in practice. We seek to address these limitations by proposing a methodology and framework for incorporating probabilistic reasoning directly into existing program analyses that are based on logical reasoning. We demonstrate that the combined approach can benefit a number of important applications of program analysis and thereby facilitate more widespread adoption of this technology.

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On Incremental Core-Guided MaxSAT Solving

Cited by 5 publications

References 24 publications

Maximum Satisfiability in Software Analysis: Applications and Techniques

Maximum Satisfiability in Software Analysis: Applications and Techniques

Incremental Formula-Based Fix Localization

Combining the logical and the probabilistic in program analysis

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