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20172023
most citedExplainable Automated Debugging via Large Language Model-driven Scientific Debugging

14 citations · 26 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.SE202314 cited

Explainable Automated Debugging via Large Language Model-driven Scientific Debugging

Sungmin Kang, Bei Chen, Shin Yoo +1

Automated debugging techniques have the potential to reduce developer effort in debugging, and have matured enough to be adopted by industry. However, one critical issue with exist…

cs.SE2022

GLAD: Neural Predicate Synthesis to Repair Omission Faults

Sungmin Kang, Shin Yoo

Existing template and learning-based APR tools have successfully found patches for many benchmark faults. However, our analysis of existing results shows that omission faults pose…

cs.SE2021

Searching for Multi-Fault Programs in Defects4J

Gabin An, Juyeon Yoon, Shin Yoo

Defects4J has enabled numerous software testing and debugging research work since its introduction. A large part of its contribution, and the resulting popularity, lies in the clea…

cs.SE20211 cited

Improving Test Distance for Failure Clustering with Hypergraph Modelling

Gabin An, Juyeon Yoon, Joyce Jiyoung Whang +1

Automated debugging techniques, such as Fault Localisation (FL) or Automated Program Repair (APR), are typically designed under the Single Fault Assumption (SFA). However, in pract…

cs.SE20216 cited

Causal Program Dependence Analysis

Seongmin Lee, Dave Binkley, Robert Feldt +2

We introduce Causal Program Dependence Analysis (CPDA), a dynamic dependence analysis that applies causal inference to model the strength of program dependence relations in a conti…

cs.SE20201 cited

Genetic Improvement @ ICSE 2020

William B. Langdon, Westley Weimer, Justyna Petke +13

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international G…