Oct 9, 2019 · This article presents a novel learn-to-rank fault localization technique called PRecise machINe-learning-based fault loCalization tEchnique (PRINCE).

First, PRINCE extracts both dynamic features, which depend on the dynamic behaviors of the target programs and static features. For example, PRINCE extracts ...

We have demonstrated that PRINCE can localize a fault precisely (e.g., 2.4% in the expense metric and 0.49 in MAP on average over the target faults and 52.9% in ...

A novel learn-to-rank fault localization technique called PRINCE, which uses genetic programming (GP) to combine multiple sets of localization input ...

Nov 13, 2020 · For dynamic features, PRINCE encompasses both Spectrum Based Fault Localization (SBFL) and Mutation Based Fault Localization (MBFL) techniques.

This article presents a novel learn-to-rank fault localization technique called PRecise machiNe-learning-based fault loCalization tEchnique (PRINCE). PRINCE ...

Dive into the research topics of 'Precise learn-to-rank fault localization using dynamic and static features of target programs'. Together they form a unique ...

This paper puts forward ALBFL, a novel neural ranking model that combines the static and dynamic features, which obtains excellent fault localization accuracy.

This work proposes Savant, a new fault localization approach that employs a learning-to-rank strategy, using likely invariant diffs and suspiciousness ...

The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR).