Zobrazeno 1 - 10
of 178
pro vyhledávání: '"Martins,Ruben"'
Logic programs are a powerful approach for solving NP-Hard problems. However, due to their declarative nature, debugging logic programs poses significant challenges. Unlike procedural paradigms, which allow for step-by-step inspection of program stat
Externí odkaz:
http://arxiv.org/abs/2410.20962
Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
Software security vulnerabilities allow attackers to perform malicious activities to disrupt software operations. Recent Transformer-based language models have significantly advanced vulnerability detection, surpassing the capabilities of static anal
Externí odkaz:
http://arxiv.org/abs/2406.05892
Language models have improved by orders of magnitude with the recent emergence of Transformer-based Large Language Models (LLMs). LLMs have demonstrated their ability to generate natural code that is highly similar to code written by professional dev
Externí odkaz:
http://arxiv.org/abs/2404.15236
We present a comprehensive demonstration of how automated reasoning can assist mathematical research, both in the discovery of conjectures and in their verification. Our focus is a discrete geometry problem: What is $\mu_{5}(n)$, the minimum number o
Externí odkaz:
http://arxiv.org/abs/2311.03645
Autor:
Yang, Aidan Z. H., Brancas, Ricardo, Esteves, Pedro, Aparicio, Sofia, Nadkarni, Joao Pedro, Terra-Neves, Miguel, Manquinho, Vasco, Martins, Ruben
Data analysts use SQL queries to access and manipulate data on their databases. However, these queries are often challenging to write, and small mistakes can lead to unexpected data output. Recent work has explored several ways to automatically synth
Externí odkaz:
http://arxiv.org/abs/2310.03866
Fault Localization (FL) aims to automatically localize buggy lines of code, a key first step in many manual and automatic debugging tasks. Previous FL techniques assume the provision of input tests, and often require extensive program analysis, progr
Externí odkaz:
http://arxiv.org/abs/2310.01726
Autor:
Ramos, Daniel, Mitchell, Hailie, Lynce, Inês, Manquinho, Vasco, Martins, Ruben, Goues, Claire Le
Publikováno v:
38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023, Luxembourg, September 11-15, 2023
Software developers often struggle to update APIs, leading to manual, time-consuming, and error-prone processes. We introduce MELT, a new approach that generates lightweight API migration rules directly from pull requests in popular library repositor
Externí odkaz:
http://arxiv.org/abs/2308.14687
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. The partitioning methods can be based on clause weights (e.g., stratification) or b
Externí odkaz:
http://arxiv.org/abs/2305.16191
In recent years, more people have seen their work depend on data manipulation tasks. However, many of these users do not have the background in programming required to write complex programs, particularly SQL queries. One way of helping these users i
Externí odkaz:
http://arxiv.org/abs/2203.04995
Autor:
Ni, Ansong, Ramos, Daniel, Yang, Aidan, Lynce, Inês, Manquinho, Vasco, Martins, Ruben, Goues, Claire Le
With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source organizations of
Externí odkaz:
http://arxiv.org/abs/2102.06726