Bridging the Gap between Different Programming Paradigms in Coverage-based Fault Localization

Fault localization is to identify faulty program elements. Among the large number of fault localization approaches in the literature, coverage-based fault localization, especially spectrum-based fault localization has been intensively studied due to its effectiveness and lightweightness. Despite the rich literature, almost all existing fault localization approaches and studies are conducted on imperative programming languages such as Java and C, leaving a gap in other programming paradigms. In this paper, we aim to study fault localization approaches for the functional programming paradigm, using Haskell language as a representation. We build up the first dataset on real Haskell projects including both real and seeded faults, which enables the research of fault localization for functional languages. With this dataset, we explore fault localization techniques for Haskell. In particular, as typically for SBFL approaches, we study methods for coverage collection as well as formulae for suspiciousness scores computation, and carefully adapt these two components to Haskell considering the language features and characteristics, resulting in a series of adaption approaches and a learning-based approach, which are evaluated on the dataset to demonstrate the promises of the direction.