248,640 research outputs found
Test Set Diameter: Quantifying the Diversity of Sets of Test Cases
A common and natural intuition among software testers is that test cases need
to differ if a software system is to be tested properly and its quality
ensured. Consequently, much research has gone into formulating distance
measures for how test cases, their inputs and/or their outputs differ. However,
common to these proposals is that they are data type specific and/or calculate
the diversity only between pairs of test inputs, traces or outputs.
We propose a new metric to measure the diversity of sets of tests: the test
set diameter (TSDm). It extends our earlier, pairwise test diversity metrics
based on recent advances in information theory regarding the calculation of the
normalized compression distance (NCD) for multisets. An advantage is that TSDm
can be applied regardless of data type and on any test-related information, not
only the test inputs. A downside is the increased computational time compared
to competing approaches.
Our experiments on four different systems show that the test set diameter can
help select test sets with higher structural and fault coverage than random
selection even when only applied to test inputs. This can enable early test
design and selection, prior to even having a software system to test, and
complement other types of test automation and analysis. We argue that this
quantification of test set diversity creates a number of opportunities to
better understand software quality and provides practical ways to increase it.Comment: In submissio
How is a data-driven approach better than random choice in label space division for multi-label classification?
We propose using five data-driven community detection approaches from social
networks to partition the label space for the task of multi-label
classification as an alternative to random partitioning into equal subsets as
performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector,
infomap, walktrap and label propagation algorithms. We construct a label
co-occurence graph (both weighted an unweighted versions) based on training
data and perform community detection to partition the label set. We include
Binary Relevance and Label Powerset classification methods for comparison. We
use gini-index based Decision Trees as the base classifier. We compare educated
approaches to label space divisions against random baselines on 12 benchmark
data sets over five evaluation measures. We show that in almost all cases seven
educated guess approaches are more likely to outperform RAkELd than otherwise
in all measures, but Hamming Loss. We show that fastgreedy and walktrap
community detection methods on weighted label co-occurence graphs are 85-92%
more likely to yield better F1 scores than random partitioning. Infomap on the
unweighted label co-occurence graphs is on average 90% of the times better than
random paritioning in terms of Subset Accuracy and 89% when it comes to Jaccard
similarity. Weighted fastgreedy is better on average than RAkELd when it comes
to Hamming Loss
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
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