1 code implementation • 3 Oct 2023 • Aidan Z. H. Yang, Ruben Martins, Claire Le Goues, Vincent J. Hellendoorn
Specifically, we propose to overcome the left-to-right nature of LLMs by fine-tuning a small set of bidirectional adapter layers on top of the representations learned by LLMs to produce LLMAO, the first language model based fault localization approach that locates buggy lines of code without any test coverage information.
1 code implementation • 28 Aug 2023 • Daniel Ramos, Hailie Mitchell, Inês Lynce, Vasco Manquinho, Ruben Martins, Claire Le Goues
By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in \comby, a language for structural code search and replace.
1 code implementation • 25 May 2023 • Pedro Orvalho, Vasco Manquinho, Ruben Martins
It has been shown that Maximum Satisfiability (MaxSAT) problem instances can be effectively solved by partitioning the set of soft clauses into several disjoint sets.
no code implementations • 28 Dec 2020 • Margarida Ferreira, Miguel Terra-Neves, Miguel Ventura, Inês Lynce, Ruben Martins
Experimental results show that FOREST successfully returns the desired regular expression in 72% of the instances and outperforms REGEL, a state-of-the-art regular expression synthesizer.
1 code implementation • 31 Aug 2020 • Pedro Orvalho, Miguel Terra-Neves; Miguel Ventura, Ruben Martins, Vasco Manquinho
As a result, many of them can provide examples of data transformations but are unable to produce the desired query.
no code implementations • 10 Oct 2019 • Ruben Martins, Saurabh Joshi, Vasco Manquinho, Ines Lynce
To celebrate the first 25 years of the International Conference on Principles and Practice of Constraint Programming (CP) the editors invited the authors of the most cited paper of each year to write a commentary on their paper.
1 code implementation • 19 Jun 2018 • Saurabh Joshi, Prateek Kumar, Ruben Martins, Sukrut Rao
Incomplete MaxSAT solving aims to quickly find a solution that attempts to minimize the sum of the weights of the unsatisfied soft clauses without providing any optimality guarantees.
no code implementations • 26 Jun 2017 • Edward Zulkoski, Ruben Martins, Christoph Wintersteiger, Robert Robere, Jia Liang, Krzysztof Czarnecki, Vijay Ganesh
Over the years complexity theorists have proposed many structural parameters to explain the surprising efficiency of conflict-driven clause-learning (CDCL) SAT solvers on a wide variety of large industrial Boolean instances.
no code implementations • 21 Jul 2015 • Saurabh Joshi, Ruben Martins, Vasco Manquinho
We show the superiority of GTE with respect to other encodings when large pseudo-Boolean constraints have low number of distinct coefficients.
no code implementations • 10 May 2015 • Miguel Neves, Ruben Martins, Mikoláš Janota, Inês Lynce, Vasco Manquinho
Usually, these MaxSAT algorithms perform better when small unsatisfiable subformulas are found early.
no code implementations • 20 Aug 2014 • Ruben Martins, Saurabh Joshi, Vasco Manquinho, Ines Lynce
In general, MaxSAT algorithms perform a succession of SAT solver calls to reach an optimum solution making extensive use of cardinality constraints.