Spectrum-based sequential diagnosis

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Gonzalez Sanchez, A (Author) Other; Abreu, R (Author) FEUP; Gross, HG (Author) Other; Van Gemund, AJC (Author) Other

Conference proceedings International

Title: Proceedings of the National Conference on Artificial Intelligence

Pages: 189-196

25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11

San Francisco, CA, 7 August 2011 through 11 August 2011

Indexing

ISI Proceedings

ISI Web of Science

Scopus - 10 Citations

Other information

Authenticus ID: P-008-0XB

Abstract (EN): We present a spectrum-based, sequential software debugging approach coined SEQUOIA, that greedily selects tests out of a suite of tests to narrow down the set of diagnostic candidates with a minimum number of tests. SEQUOIA handles multiple faults, that can be intermittent, at polynomial time and space complexity, due to a novel, approximate diagnostic entropy estimation approach, which considers the subset of diagnoses that cover almost all Bayesian posterior probability mass. Synthetic experiments show that SEQUOIA achieves much better diagnostic uncertainty reduction compared to random test sequencing. Real programs, taken from the Software Infrastructure Repository, confirm SEQUOIA'S better performance, with a test reduction up to 80% compared to random test sequences. Copyright

Language: English

Type (Professor's evaluation): Scientific