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%-*- text -*-
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This is a PROMISE Software Engineering Repository data set made publicly
available in order to encourage repeatable, verifiable, refutable, and/or
improvable predictive models of software engineering.
If you publish material based on PROMISE data sets then, please
follow the acknowledgment guidelines posted on the PROMISE repository
web page http://promise.site.uottawa.ca/SERepository .
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
1. Title/Topic: KC2/software defect prediction
2. Sources:
-- Creators: NASA, then the NASA Metrics Data Program,
-- http://mdp.ivv.nasa.gov. Contacts: Mike Chapman,
Galaxy Global Corporation (Robert.Chapman@ivv.nasa.gov)
+1-304-367-8341; Pat Callis, NASA, NASA project manager
for MDP (Patrick.E.Callis@ivv.nasa.gov) +1-304-367-8309
-- Donor: Tim Menzies (tim@barmag.net)
-- Date: December 2 2004
3. Past usage:
1. How Good is Your Blind Spot Sampling Policy?; 2003; Tim Menzies
and Justin S. Di Stefano; 2004 IEEE Conference on High Assurance
Software Engineering (http://menzies.us/pdf/03blind.pdf).
-- Results:
-- Very simple learners (ROCKY) perform as well in this domain
as more sophisticated methods (e.g. J48, model trees, model
trees) for predicting detects
-- Many learners have very low false alarm rates.
-- Probability of detection (PD) rises with effort and rarely
rises above it.
-- High PDs are associated with high PFs (probability of
failure)
-- PD, PF, effort can change significantly while accuracy
remains essentially stable
-- With two notable exceptions, detectors learned from one
data set (e.g. KC2) have nearly they same properties when
applied to another (e.g. PC2, KC2). Exceptions:
-- LinesOfCode measures generate wider inter-data-set variances;
-- Precision's inter-data-set variances vary wildly
2. "Assessing Predictors of Software Defects", T. Menzies and
J. DiStefano and A. Orrego and R. Chapman, 2004,
Proceedings, workshop on Predictive Software Models, Chicago,
Available from http://menzies.us/pdf/04psm.pdf.
-- Results:
-- From KC2, Naive Bayes generated PDs of 50% with PF of 10%
-- Naive Bayes out-performs J48 for defect detection
-- When learning on more and more data, little improvement is
seen after processing 300 examples.
-- PDs are much higher from data collected below the sub-sub-
system level.
-- Accuracy is a surprisingly uninformative measure of success
for a defect detector. Two detectors with the same accuracy
can have widely varying PDs and PFs.
4. Relevant information:
-- Data from C++ functions. Science data processing; another part
of the same project as KC1; different personnel than KC1. Shared
some third-party software libraries with KC1, but no other software
overlap.
-- Data comes from McCabe and Halstead features extractors of
source code. These features were defined in the 70s in an attempt
to objectively characterize code features that are associated with
software quality. The nature of association is under dispute.
Notes on McCabe and Halstead follow.
-- The McCabe and Halstead measures are "module"-based where a
"module" is the smallest unit of functionality. In C or Smalltalk,
"modules" would be called "function" or "method" respectively.
-- Defect detectors can be assessed according to the following measures:
module actually has defects
+-------------+------------+
| no | yes |
+-----+-------------+------------+
classifier predicts no defects | no | a | b |
+-----+-------------+------------+
classifier predicts some defects | yes | c | d |
+-----+-------------+------------+
accuracy = acc = (a+d)/(a+b+c+d
probability of detection = pd = recall = d/(b+d)
probability of false alarm = pf = c/(a+c)
precision = prec = d/(c+d)
effort = amount of code selected by detector
= (c.LOC + d.LOC)/(Total LOC)
Ideally, detectors have high PDs, low PFs, and low
effort. This ideal state rarely happens:
-- PD and effort are linked. The more modules that trigger
the detector, the higher the PD. However, effort also gets
increases
-- High PD or low PF comes at the cost of high PF or low PD
(respectively). This linkage can be seen in a standard
receiver operator curve (ROC). Suppose, for example, LOC> x
is used as the detector (i.e. we assume large modules have
more errors). LOC > x represents a family of detectors. At
x=0, EVERY module is predicted to have errors. This detector
has a high PD but also a high false alarm rate. At x=0, NO
module is predicted to have errors. This detector has a low
false alarm rate but won't detect anything at all. At 0 but does not reach it.
-- The line pf=pd on the above graph represents the "no information"
line. If pf=pd then the detector is pretty useless. The better
the detector, the more it rises above PF=PD towards the "sweet spot".
NOTES ON MCCABE/HALSTEAD
========================
McCabe argued that code with complicated pathways are more
error-prone. His metrics therefore reflect the pathways within a
code module.
@Article{mccabe76,
title = "A Complexity Measure",
author = "T.J. McCabe",
pages = "308--320",
journal = "IEEE Transactions on Software Engineering",
year = "1976",
volume = "2",
month = "December",
number = "4"}
Halstead argued that code that is hard to read is more likely to be
fault prone. Halstead estimates reading complexity by counting the
number of concepts in a module; e.g. number of unique operators.
@Book{halstead77,
Author = "M.H. Halstead",
Title = "Elements of Software Science",
Publisher = "Elsevier ",
Year = 1977}
We study these static code measures since they are useful, easy to
use, and widely used:
-- USEFUL: E.g. this data set can generate highly accurate
predictors for defects
-- EASY TO USE: Static code measures (e.g. lines of code, the
McCabe/Halstead measures) can be automatically and cheaply
collected.
-- WIDELY USED: Many researchers use static measures to guide
software quality predictions (see the reference list in the above
"blind spot" paper. Verification and validation (V\&V) textbooks
advise using static code complexity measures to decide which
modules are worthy of manual inspections. Further, we know of
several large government software contractors that won't review
software modules _unless_ tools like McCabe predict that they are
fault prone. Hence, defect detectors have a major economic impact
when they may force programmers to rewrite code.
Nevertheless, the merits of these metrics has been widely
criticized. Static code measures are hardly a complete
characterization of the internals of a function. Fenton offers an
insightful example where the same functionality is achieved using
different programming language constructs resulting in different
static measurements for that module. Fenton uses this example to
argue the uselessness of static code measures.
@book{fenton97,
author = "N.E. Fenton and S.L. Pfleeger",
title = {Software metrics: a Rigorous \& Practical Approach},
publisher = {International Thompson Press},
year = {1997}}
An alternative interpretation of Fenton's example is that static
measures can never be a definite and certain indicator of the
presence of a fault. Rather, defect detectors based on static
measures are best viewed as probabilistic statements that the
frequency of faults tends to increase in code modules that trigger
the detector. By definition, such probabilistic statements will
are not categorical claims with some a non-zero false alarm
rate. The research challenge for data miners is to ensure that
these false alarms do not cripple their learned theories.
The McCabe metrics are a collection of four software metrics:
essential complexity, cyclomatic complexity, design complexity and
LOC, Lines of Code.
-- Cyclomatic Complexity, or "v(G)", measures the number of
"linearly independent paths". A set of paths is said to be
linearly independent if no path in the set is a linear combination
of any other paths in the set through a program's "flowgraph". A
flowgraph is a directed graph where each node corresponds to a
program statement, and each arc indicates the flow of control from
one statement to another. "v(G)" is calculated by "v(G) = e - n + 2"
where "G" is a program's flowgraph, "e" is the number of arcs in
the flowgraph, and "n" is the number of nodes in the
flowgraph. The standard McCabes rules ("v(G)">10), are used to
identify fault-prone module.
-- Essential Complexity, or "ev(G)$" is the extent to which a
flowgraph can be "reduced" by decomposing all the subflowgraphs
of $G$ that are "D-structured primes". Such "D-structured
primes" are also sometimes referred to as "proper one-entry
one-exit subflowgraphs" (for a more thorough discussion of
D-primes, see the Fenton text referenced above). "ev(G)" is
calculated using "ev(G)= v(G) - m" where $m$ is the number of
subflowgraphs of "G" that are D-structured primes.
-- Design Complexity, or "iv(G)", is the cyclomatic complexity of a
module's reduced flowgraph. The flowgraph, "G", of a module is
reduced to eliminate any complexity which does not influence the
interrelationship between design modules. According to McCabe,
this complexity measurement reflects the modules calling patterns
to its immediate subordinate modules.
-- Lines of code is measured according to McCabe's line counting
conventions.
The Halstead falls into three groups: the base measures, the
derived measures, and lines of code measures.
-- Base measures:
-- mu1 = number of unique operators
-- mu2 = number of unique operands
-- N1 = total occurrences of operators
-- N2 = total occurrences of operands
-- length = N = N1 + N2
-- vocabulary = mu = mu1 + mu2
-- Constants set for each function:
-- mu1' = 2 = potential operator count (just the function
name and the "return" operator)
-- mu2' = potential operand count. (the number
of arguments to the module)
For example, the expression "return max(w+x,x+y)" has "N1=4"
operators "return, max, +,+)", "N2=4" operands (w,x,x,y),
"mu1=3" unique operators (return, max,+), and "mu2=3" unique
operands (w,x,y).
-- Derived measures:
-- P = volume = V = N * log2(mu) (the number of mental
comparisons needed to write
a program of length N)
-- V* = volume on minimal implementation
= (2 + mu2')*log2(2 + mu2')
-- L = program length = V*/N
-- D = difficulty = 1/L
-- L' = 1/D
-- I = intelligence = L'*V'
-- E = effort to write program = V/L
-- T = time to write program = E/18 seconds
5. Number of instances: 522
6. Number of attributes: 22 (5 different lines of code measure,
3 McCabe metrics, 4 base Halstead measures, 8 derived
Halstead measures, a branch-count, and 1 goal field)
7. Attribute Information:
1. loc : numeric % McCabe's line count of code
2. v(g) : numeric % McCabe "cyclomatic complexity"
3. ev(g) : numeric % McCabe "essential complexity"
4. iv(g) : numeric % McCabe "design complexity"
5. n : numeric % Halstead total operators + operands
6. v : numeric % Halstead "volume"
7. l : numeric % Halstead "program length"
8. d : numeric % Halstead "difficulty"
9. i : numeric % Halstead "intelligence"
10. e : numeric % Halstead "effort"
11. b : numeric % Halstead
12. t : numeric % Halstead's time estimator
13. lOCode : numeric % Halstead's line count
14. lOComment : numeric % Halstead's count of lines of comments
15. lOBlank : numeric % Halstead's count of blank lines
16. lOCodeAndComment: numeric
17. uniq_Op : numeric % unique operators
18. uniq_Opnd : numeric % unique operands
19. total_Op : numeric % total operators
20. total_Opnd : numeric % total operands
21: branchCount : numeric % of the flow graph
22. problems : {no,yes}% module has/has not one or more
% reported defects
8. Missing attributes: none
9. Class Distribution: the class value (problems) is discrete
yes: 105 = 20.5%
no: 415 = 79.5%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

problems (target) | nominal | 2 unique values 0 missing | |

t | numeric | 330 unique values 0 missing | |

branchCount | numeric | 37 unique values 0 missing | |

total_Opnd | numeric | 131 unique values 0 missing | |

total_Op | numeric | 152 unique values 0 missing | |

uniq_Opnd | numeric | 66 unique values 0 missing | |

uniq_Op | numeric | 30 unique values 0 missing | |

lOCodeAndComment | numeric | 8 unique values 0 missing | |

lOBlank | numeric | 35 unique values 0 missing | |

lOComment | numeric | 28 unique values 0 missing | |

lOCode | numeric | 112 unique values 0 missing | |

loc | numeric | 123 unique values 0 missing | |

b | numeric | 86 unique values 0 missing | |

e | numeric | 329 unique values 0 missing | |

i | numeric | 319 unique values 0 missing | |

d | numeric | 254 unique values 0 missing | |

l | numeric | 44 unique values 0 missing | |

v | numeric | 297 unique values 0 missing | |

n | numeric | 196 unique values 0 missing | |

iv(g) | numeric | 26 unique values 0 missing | |

ev(g) | numeric | 21 unique values 0 missing | |

v(g) | numeric | 37 unique values 0 missing |

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.18

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.34

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.18

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.34

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.18

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.34

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

0.4

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.33

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.33

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0.73

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

Maximum mutual information between the nominal attributes and the target attribute.

2

The maximum number of distinct values among attributes of the nominal type.

Average mutual information between the nominal attributes and the target attribute.

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

2

Average number of distinct values among the attributes of the nominal type.

Minimal mutual information between the nominal attributes and the target attribute.

2

The minimal number of distinct values among attributes of the nominal type.

0.81

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

43.98

First quartile of kurtosis among attributes of the numeric type.

First quartile of mutual information between the nominal attributes and the target attribute.

5.48

First quartile of skewness among attributes of the numeric type.

6.51

First quartile of standard deviation of attributes of the numeric type.

139.55

Second quartile (Median) of kurtosis among attributes of the numeric type.

9.74

Second quartile (Median) of means among attributes of the numeric type.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

10.08

Second quartile (Median) of skewness among attributes of the numeric type.

21.94

Second quartile (Median) of standard deviation of attributes of the numeric type.

199.21

Third quartile of kurtosis among attributes of the numeric type.

Third quartile of mutual information between the nominal attributes and the target attribute.

12.28

Third quartile of skewness among attributes of the numeric type.

116.69

Third quartile of standard deviation of attributes of the numeric type.

0.78

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.41

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.78

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.41

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.78

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.41

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.64

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.19

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.38

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.64

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.19

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.38

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.64

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.19

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.38

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0

Standard deviation of the number of distinct values among attributes of the nominal type.