Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Wasif Afzal",
  title =        "Using Faults-Slip-Through Metric as a Predictor of
  booktitle =    "17th Asia Pacific Software Engineering Conference
                 (APSEC 2010)",
  year =         "2010",
  month =        nov # " 30-" # dec # " 3",
  pages =        "414--422",
  abstract =     "Background: The majority of software faults are
                 present in small number of modules, therefore accurate
                 prediction of fault-prone modules helps improve
                 software quality by focusing testing efforts on a
                 subset of modules. Aims: This paper evaluates the use
                 of the faults-slip-through (FST) metric as a potential
                 predictor of fault-prone modules. Rather than
                 predicting the fault-prone modules for the complete
                 test phase, the prediction is done at the specific test
                 levels of integration and system test. Method: We
                 applied eight classification techniques, to the task of
                 identifying fault prone modules, representing a variety
                 of approaches, including a standard statistical
                 technique for classification (logistic regression),
                 tree-structured classifiers (C4.5 and random forests),
                 a Bayesian technique (Naive Bayes), machine-learning
                 techniques (support vector machines and
                 back-propagation artificial neural networks) and
                 search-based techniques (genetic programming and
                 artificial immune recognition systems) on FST data
                 collected from two large industrial projects from the
                 telecommunication domain. Results: Using area under the
                 receiver operating characteristic (ROC) curve and the
                 location of (PF, PD) pairs in the ROC space, the faults
                 slip-through metric showed impressive results with the
                 majority of the techniques for predicting fault-prone
                 modules at both integration and system test levels.
                 There were, however, no statistically significant
                 differences between the performance of different
                 techniques based on AUC, even though certain techniques
                 were more consistent in the classification performance
                 at the two test levels. Conclusions: We can conclude
                 that the faults-slip-through metric is a potentially
                 strong predictor of fault-proneness at integration and
                 system test levels. The faults-slip-through
                 measurements interact in ways that is conveniently
                 accounted for by majority of the data mining
  keywords =     "genetic algorithms, genetic programming, sbse,
                 Bayesian technique, artificial immune recognition
                 systems, back-propagation artificial neural networks,
                 data mining, fault-proneness predictor,
                 faults-slip-through metric, logistic regression,
                 machine-learning techniques, receiver operating
                 characteristic curve, search-based techniques, software
                 faults, software quality, standard statistical
                 technique, support vector machines, system test levels,
                 tree-structured classifiers, backpropagation, data
                 mining, neural nets, program testing, software quality,
                 statistical analysis, support vector machines",
  DOI =          "doi:10.1109/APSEC.2010.54",
  ISSN =         "1530-1362",
  notes =        "Blekinge Inst. of Technol., Ronneby, Sweden. Also
                 known as \cite{5693218}",

Genetic Programming entries for Wasif Afzal