Zoltar: A Toolset for Automatic Fault Localization. Locating software components which are responsible for observed failures is the most expensive, error-prone phase in the software development life cycle. Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important process for the development of dependable software. In this paper we present a toolset for automatic fault localization, dubbed Zoltar, which hosts a range of spectrum-based fault localization techniques featuring BARINEL, our latest algorithm. The toolset provides the infrastructure to automatically instrument the source code of software programs to produce runtime data, which is subsequently analyzed to return a ranked list of diagnosis candidates. Aimed at total automation (e.g., for runtime fault diagnosis), Zoltar has the capability of instrumenting the program under analysis with fault screeners as a run-time replacement for design-time test oracles

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  1. Qusay Idrees Sarhan, Attila Szatmari, Rajmond Toth, Arpad Beszedes: CharmFL: A Fault Localization Tool for Python (2021) arXiv
  2. Pedro Pinto, Rui Abreu, João M. P. Cardoso: Fault Detection in C Programs using Monitoring of Range Values: Preliminary Results (2015) arXiv
  3. Dandan, Gong; Tiantian, Wang; Xiaohong, Su; Peijun, Ma: A test-suite reduction approach to improving fault-localization effectiveness (2013) ioport
  4. Abreu, Rui; van Gemund, Arjan J. C.: Diagnosing multiple intermittent failures using maximum likelihood estimation (2010) ioport