- author = "Shin Yoo",
- title = "Evolving Human Competitive Spectra-Based Fault Localisation Techniques",
- institution = "Department of Computer Science, University College, London",
- year = "2012",
- type = "Research Note",
- number = "RN/12/03",
- address = "UK",
- month = "8 " # may,
- keywords = "genetic algorithms, genetic programming, SBSE, GPU, openCL",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.5356",
-
URL = "
http://www-typo3.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/rn-12-03.pdf",
- size = "13 pages",
- abstract = "Spectra-Based Fault Localisation (SBFL) aims to assist de- bugging by applying risk evaluation formulae (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming approach for evolving risk assessment formulae. The empirical evaluation using 92 faults from four Unix utilities produces promising results. GP-evolved equations can consistently outperform many of the human-designed formulae, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 5.9 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures.",
-
notes = "The program spectra data used in the paper, as well as
the complete empirical results, are available from:
broken Oct 2020
http://www.cs.ucl.ac.uk/staff/s.yoo/evolving-sbfl.html
SIR (flex, grep, gzip, sed), gcov, Linux, pyevolve (python).
Published as \cite{Yoo:2012:SSBSE}
Entered 2012 HUMIES GECCO 2012.
",
