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. Sampling and . Testing, learning : process for inferring constraints of product lines

, Constraining the configuration space

, Variability model excerpt of the generator

, Learning method on the video generator

, An excerpt of the decision tree built from a sample of 500 configurations/videos

. .. Study, 72 5.1 Configuration of a specialized x264. Given a performance objective, the specialization method infers and fixes some options values (no_mbtree and crfRatio), p.86

. .. , Because of the heterogeneity of systems, we present the percentage of configurations used in the training set compared to the number of available configurations (see last column of Table 5.1 and the absolute number under brackets. The last column present the percentage of interesting configurations according to the distribution of performance and the performance goal (given under brackets), p.108

, thanks to parameters) ; for each morph test suite is executed and performance measurements are gathered ; a dispersion score is finally computed to characterize the quality of a test suite

, An example of a histogram (based on Table 6.1)

, Stability results for the property precision ; X-axis : number of morphs removed ; Y-axis : dispersion score

, The number of bins activated with the original test suite (composed of 84 test cases)

, The number of bins activated with the smaller test suite composed of 5 test cases that maximizes the dispersion score

, On the right is the histogram associated with our smaller test suite. Bars in blue represent activated bins of histograms. Bars in red are bins that are activated with the original test suite but that we fail to activate with our smaller test suite, p.133

, Average precision measures over the 5 videos from RQ2. On X-axis are the CV morphs : first, the 6 first morphs that are supposed to perform moderately badly ; the 6 last morphs are supposed to be good. Y-axis reports the averaged precision measure over the test suite, p.134

. Liste and . Tableaux,

, An example of a confusion matrix for a 2-class problem (i.e., class +1 and -1)

, Confusion matrix of our experiment, Features : number of boolean features / number of numerical features

, #V M : number of valid configurations ; Meas. : number of configurations that have been measured

, An example for showing the inadequacy of variance and illustrating our measure : performance observations gathered for 2 different test suites, each composed of 2 test cases over 6 morphs

, The three case studies

, Sample of observations for precision on the OpenCV case, p.128

. .. , 149 3 Excerpt of competitors' performances differences between the original benchmark (Initial Perf) and our reduced set of 5 concepts (Computed Perf)

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