Search Results for author:
Found 5 papers, 1 papers with code
rTisane: Externalizing conceptual models for data analysis increases engagement with domain knowledge and improves statistical model quality
We leverage these findings to develop rTisane, a DSL for expressing conceptual models augmented with an interactive disambiguation process.
Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition
Specifically, it proposes partial test oracles as a method for detecting faults and slice functions as a method for localizing faults to characteristic patterns in the input data or relevant tasks performed by the user.
Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships
Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured.
Practical Mutation Testing at Scale
Mutation analysis assesses a test suite's adequacy by measuring its ability to detect small artificial faults, systematically seeded into the tested program.
Software Engineering 68-04 D.2.5
Tailored Mutants Fit Bugs Better
The results demonstrate that the location selection heuristics produce mutants more closely coupled to real faults for a given budget of mutation operator applications.
Software Engineering
