;%%-*- 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 Batrue generated PDs of 50% with PF of 10% ;% ;% -- Naive Batrue 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 | true | ;% +-----+-------------+------------+ ;% classifier predicts no defects | no | a | b | ;% +-----+-------------+------------+ ;% classifier predicts some defects | true | 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,true}% module has/has not one or more ;% % reported defects ;% 8. Missing attributes: none ;% 9. Class Distribution: the class value (problems) is discrete ;% true: 105 = 20.5% ;% no: 415 = 79.5% ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% (defun KC2 () (data :name 'KC2 :columns '($loc $v $ev $iv $n $v $l $d $i $e $b $t $lOCode $lOComment $lOBlank $locCodeAndComment $uniq_Op $uniq_Opnd $total_Op $total_Opnd $branchCount defects) :egs '( (1.1 1.4 1.4 1.4 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 2 2 2 2 1.2 1.2 1.2 1.2 1.4 no) (1 1.0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 true) (415 59.0 50 51 1159 8411.31 0.01 103.53 81.24 870848.58 2.8 48380.48 359 35 9 10 47 106 692 467 106 true) (230 33.0 10 16 575 3732.82 0.03 39.82 93.74 148644.06 1.24 8258 174 15 34 5 23 67 343 232 65 true) (175 26.0 12 13 500 3123.96 0.03 29.48 105.96 92103.07 1.04 5116.84 142 7 19 4 18 58 310 190 51 true) (163 16.0 13 11 440 2714.77 0.03 32.25 84.14 87589.65 0.9 4866.09 139 2 20 0 19 53 260 180 31 true) (152 11.0 6 11 432 2629.78 0.03 31.68 83.01 83311.56 0.88 4628.42 114 18 17 0 18 50 256 176 21 true) (3 1.0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 no) (14 2.0 1 2 22 88 0.17 5.79 15.21 509.14 0.03 28.29 8 0 1 0 9 7 13 9 3 no) (10 2.0 1 2 18 64.53 0.14 7 9.22 451.71 0.02 25.09 8 0 0 0 8 4 11 7 3 no) (8 1.0 1 1 10 31.7 0.5 2 15.85 63.4 0.01 3.52 3 0 0 1 4 5 5 5 1 no) (6 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 2 0 3 2 3 2 1 no) (14 2.0 1 1 11 36.54 0.27 3.75 9.74 137.03 0.01 7.61 3 9 0 0 6 4 6 5 3 no) (4 1.0 1 1 9 25.27 0.67 1.5 16.84 37.9 0.01 2.11 2 0 0 0 3 4 5 4 1 no) (2 1.0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 3 2 3 2 1 no) (2 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 0 0 0 0 1 0 1 0 1 no) (39 4.0 1 2 83 429.1 0.05 19 22.58 8152.97 0.14 452.94 29 1 7 0 19 17 49 34 7 no) (5 1.0 1 1 7 19.65 0.4 2.5 7.86 49.13 0.01 2.73 2 1 0 0 5 2 5 2 1 no) (9 2.0 1 1 21 87.57 0.25 4 21.89 350.27 0.03 19.46 8 0 0 0 8 10 11 10 3 no) (9 2.0 1 1 21 87.57 0.25 4 21.89 350.27 0.03 19.46 8 0 0 0 8 10 11 10 3 no) (6 1.0 1 1 16 57.36 0.35 2.86 20.08 163.88 0.02 9.1 5 0 0 0 5 7 8 8 1 no) (6 1.0 1 1 16 57.36 0.35 2.86 20.08 163.88 0.02 9.1 5 0 0 0 5 7 8 8 1 no) (11 1.0 1 1 25 108.05 0.27 3.77 28.67 407.26 0.04 22.63 8 0 1 0 7 13 11 14 1 no) (14 2.0 1 2 33 147.16 0.13 8 18.4 1177.29 0.05 65.4 11 1 0 0 11 11 17 16 3 no) (63 10.0 4 5 157 829.81 0.05 18.2 45.59 15102.51 0.28 839.03 55 2 4 0 14 25 92 65 19 no) (3 1.0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 no) (74 12.0 4 5 193 1135.35 0.05 19.92 57 22614.95 0.38 1256.39 56 5 8 0 22 37 126 67 19 no) (10 1.0 1 1 17 53.89 0.5 2 26.94 107.78 0.02 5.99 5 0 0 0 3 8 9 8 1 no) (7 1.0 1 1 17 58.81 0.67 1.5 39.21 88.22 0.02 4.9 5 0 0 0 3 6 9 8 1 no) (4 1.0 1 1 6 15.51 0.4 2.5 6.2 38.77 0.01 2.15 2 0 0 0 5 1 5 1 1 no) (5 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 0 0 3 2 3 2 1 no) (38 9.0 5 5 98 490 0.07 13.68 35.81 6705.26 0.16 372.51 34 1 1 0 13 19 58 40 17 no) (15 3.0 1 2 32 138.3 0.15 6.5 21.28 898.96 0.05 49.94 12 0 1 0 10 10 19 13 5 no) (45 6.0 1 3 104 549.68 0.07 14.61 37.63 8030.13 0.18 446.12 35 1 5 1 16 23 62 42 11 no) (39 4.0 1 2 105 520.19 0.07 13.89 37.44 7227.91 0.17 401.55 29 1 4 2 12 19 61 44 7 no) (7 2.0 1 2 10 30 0.22 4.5 6.67 135 0.01 7.5 5 0 0 0 6 2 7 3 3 no) (82 10.0 1 10 220 1178.66 0.07 15.03 78.4 17719.21 0.39 984.4 73 0 7 0 11 30 138 82 19 no) (13 2.0 1 2 18 66.61 0.18 5.6 11.89 373 0.02 20.72 8 0 1 0 8 5 11 7 3 no) (12 3.0 1 2 29 120.93 0.17 6 20.15 725.57 0.04 40.31 9 0 1 0 9 9 17 12 5 no) (64 4.0 1 3 92 464.08 0.14 7.13 65.12 3306.6 0.15 183.7 28 28 5 0 9 24 54 38 7 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (9 1.0 1 1 8 22.46 0.5 2 11.23 44.92 0.01 2.5 4 0 2 0 4 3 5 3 1 no) (64 6.0 1 6 185 977.8 0.05 19.32 50.61 18891.08 0.33 1049.5 49 7 5 0 14 25 116 69 11 no) (4 2.0 1 1 11 36.54 0.4 2.5 14.62 91.35 0.01 5.08 2 0 0 0 5 5 6 5 3 no) (12 1.0 1 1 23 87.57 0.16 6.25 14.01 547.31 0.03 30.41 6 0 2 0 10 4 18 5 1 no) (23 3.0 1 2 40 183.4 0.15 6.77 27.09 1241.47 0.06 68.97 11 6 4 0 11 13 24 16 5 no) (20 4.0 1 3 45 200.67 0.15 6.67 30.1 1337.83 0.07 74.32 14 3 1 0 10 12 29 16 7 no) (13 1.0 1 1 14 46.51 0.67 1.5 31 69.76 0.02 3.88 4 0 2 2 3 7 7 7 1 no) (15 1.0 1 1 32 135.93 0.13 7.56 17.97 1028 0.05 57.11 9 0 3 0 11 8 21 11 1 no) (13 1.0 1 1 32 130.8 0.13 7.86 16.65 1027.7 0.04 57.09 6 3 2 0 10 7 21 11 1 no) (26 4.0 1 3 53 249.12 0.11 9 27.68 2242.11 0.08 124.56 19 0 3 0 13 13 35 18 7 no) (6 1.0 1 1 11 34.87 0.29 3.5 9.96 122.04 0.01 6.78 4 0 0 0 7 2 9 2 1 no) (15 2.0 1 1 38 169.46 0.13 7.8 21.73 1321.78 0.06 73.43 10 0 1 0 12 10 25 13 3 no) (6 1.0 1 1 11 34.87 0.29 3.5 9.96 122.04 0.01 6.78 4 0 0 0 7 2 9 2 1 no) (4 1.0 1 1 9 25.27 0.33 3 8.42 75.8 0.01 4.21 2 0 0 0 6 1 8 1 1 no) (6 2.0 1 1 15 55.51 0.33 3 18.5 166.52 0.02 9.25 4 0 0 0 6 7 8 7 3 no) (3 1.0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 no) (17 3.0 1 2 64 310.91 0.09 10.73 28.97 3337.11 0.1 185.39 13 0 2 0 14 15 41 23 5 no) (22 2.0 1 2 34 141.78 0.17 6 23.63 850.66 0.05 47.26 8 1 4 0 10 14 34 24 1 no) (16 1.0 1 1 58 265.93 0.12 8.57 31.02 2279.38 0.09 126.63 13 2 5 0 9 9 22 12 3 no) (23 2.0 1 2 84 408.07 0.1 10.39 39.28 4239.4 0.14 235.52 18 0 2 0 11 18 50 34 3 no) (28 2.0 1 2 130 677.23 0.1 10.15 66.7 6876.48 0.23 382.03 21 1 3 0 11 26 82 48 3 no) (9 1.0 1 1 4 6.34 1.33 0.75 8.45 4.75 0 0.26 2 0 0 0 3 2 18 11 1 no) (7 1.0 1 1 29 67.34 0.12 8.25 8.16 555.52 0.02 30.86 5 0 0 0 2 10 8 11 1 no) (4 1.0 1 1 19 68.11 0.91 1.1 61.92 74.93 0.02 4.16 4 0 0 0 1 2 1 3 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 6 15.51 0.5 2 7.75 31.02 0.01 1.72 2 0 0 0 4 2 4 2 1 no) (5 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 1 0 3 1 3 1 1 no) (15 1.0 1 1 55 224.81 0.17 5.73 39.25 1287.55 0.07 71.53 12 0 1 0 6 11 34 21 1 no) (4 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 0 0 3 2 3 2 1 no) (56 3.0 1 2 285 1722.65 0.06 18.08 95.28 31145.55 0.57 1730.31 78 3 13 0 11 34 105 57 17 no) (4 1.0 1 1 7 19.65 0.5 2 9.83 39.3 0.01 2.18 2 0 0 0 4 3 4 3 1 no) (7 1.0 1 1 5 11.61 0 0 0 0 0 0 2 2 1 0 5 0 5 0 1 no) (78 6.0 5 6 302 1704.44 0.04 26.12 65.26 44516.08 0.57 2473.12 67 5 4 0 16 34 191 111 11 no) (3 1.0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 no) (16 2.0 1 2 23 87.57 0.14 7.2 12.16 630.5 0.03 35.03 12 0 2 0 9 5 15 8 3 no) (39 4.0 3 3 75 371.56 0.08 13.13 28.31 4876.79 0.12 270.93 30 2 5 0 15 16 47 28 7 no) (30 6.0 5 4 72 346.13 0.07 14 24.72 4845.81 0.12 269.21 20 2 5 0 14 14 44 28 11 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (102 25.0 4 4 248 1488 0.05 19.17 77.62 28525.28 0.5 1584.74 92 3 5 0 17 47 142 106 49 no) (10 2.0 1 2 22 83.76 0.17 6 13.96 502.57 0.03 27.92 8 0 0 0 8 6 13 9 3 no) (40 4.0 1 4 102 495.51 0.09 11.61 42.68 5753.47 0.17 319.64 29 1 7 1 11 18 64 38 7 no) (46 4.0 1 4 114 564.78 0.06 15.53 36.37 8769.75 0.19 487.21 35 1 7 1 13 18 71 43 7 no) (29 6.0 4 4 67 311.14 0.08 12.46 24.97 3877.26 0.1 215.4 25 0 2 0 12 13 40 27 11 no) (3 1.0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 no) (68 5.0 1 3 76 348.46 0.08 12.27 28.4 4275.3 0.12 237.52 25 0 4 0 12 24 94 73 9 no) (31 5.0 1 3 167 863.38 0.05 18.25 47.31 15756.64 0.29 875.37 51 0 14 0 11 13 47 29 9 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (16 1.0 1 1 10 30 0.33 3 10 90 0.01 5 5 0 0 0 4 5 4 7 1 no) (8 1.0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 4 4 4 6 1 no) (7 1.0 1 1 11 34.87 0.36 2.8 12.45 97.63 0.01 5.42 5 0 0 0 2 12 10 13 1 no) (4 1.0 1 1 23 87.57 0.92 1.08 80.83 94.87 0.03 5.27 4 0 0 0 1 0 1 0 1 no) (45 5.0 1 4 117 626.83 0.07 13.76 45.55 8625.23 0.21 479.18 37 1 5 0 16 25 74 43 9 no) (4 1.0 1 1 6 15.51 0.4 2.5 6.2 38.77 0.01 2.15 2 0 0 0 5 1 5 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (17 6.0 1 5 50 216.1 0.14 7.33 29.47 1584.71 0.07 88.04 13 0 2 0 8 12 28 22 11 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (6 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 3 0 1 0 3 2 3 2 1 no) (15 6.0 1 5 44 179.85 0.23 4.36 41.22 784.79 0.06 43.6 13 0 0 0 6 11 28 16 11 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 no) (4 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 0 0 3 2 3 2 1 no) (4 1.0 1 1 6 15.51 0.4 2.5 6.2 38.77 0.01 2.15 2 0 0 0 5 1 5 1 1 no) (188 8.0 4 5 509 3451.71 0.05 19.1 180.68 65942.86 1.15 3663.49 136 9 40 0 19 91 326 183 15 no) (4 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 0 0 3 2 3 2 1 no) (8 1.0 1 1 10 30 0.3 3.33 9 100 0.01 5.56 4 0 1 0 5 3 6 4 1 no) (4 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 0 0 3 2 3 2 1 no) (4 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 0 0 3 2 3 2 1 no) (8 1.0 1 1 10 30 0.3 3.33 9 100 0.01 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1101.53 0.06 17.14 64.26 18877.98 0.37 1048.78 58 8 11 0 14 29 132 71 15 true) (76 10.0 4 7 184 1016.34 0.03 29.4 34.57 29880.26 0.34 1660.01 60 3 9 0 21 25 114 70 19 true) (72 12.0 5 6 200 1122.94 0.04 23.23 48.35 26081.23 0.37 1448.96 63 0 6 0 18 31 120 80 23 true) (36 5.0 1 2 103 548.16 0.09 11.07 49.5 6070.35 0.18 337.24 26 0 5 1 13 27 57 46 9 true) (143 14.0 6 10 364 2260.24 0.04 25.05 90.25 56608.76 0.75 3144.93 105 14 21 1 19 55 219 145 27 true) (70 11.0 1 9 175 961.07 0.05 20.34 47.25 19547.56 0.32 1085.98 57 4 6 0 17 28 108 67 21 true) (35 5.0 1 4 77 388.42 0.1 10.08 38.55 3913.31 0.13 217.41 30 0 3 0 13 20 46 31 9 true) (104 17.0 5 11 356 2159.53 0.04 22.27 96.95 48102.42 0.72 2672.36 86 5 11 0 16 51 214 142 33 true) (34 6.0 6 6 80 403.55 0.06 16.29 24.78 6572.12 0.13 365.12 27 1 4 0 19 14 56 24 11 true) (102 10.0 8 9 244 1452.82 0.04 24.75 58.7 35957.39 0.48 1997.63 72 12 16 0 22 40 154 90 19 true) (33 6.0 1 3 82 413.64 0.08 12.89 32.08 5333.78 0.14 296.32 28 0 3 0 14 19 47 35 11 true) (63 5.0 1 4 160 873.51 0.06 16.57 52.73 14471.13 0.29 803.95 50 1 10 0 14 30 89 71 9 true) (61 7.0 1 4 166 916.91 0.06 16.19 56.64 14842.5 0.31 824.58 49 1 9 0 14 32 92 74 13 true) (58 4.0 1 3 140 754.92 0.06 16.39 46.06 12372.37 0.25 687.35 44 1 10 0 15 27 81 59 7 true) (113 14.0 8 12 265 1558.9 0.05 20.44 76.27 31864.67 0.52 1770.26 85 9 15 0 17 42 164 101 27 true) (139 32.0 16 10 426 2677.58 0.04 24.66 108.56 66039.69 0.89 3668.87 118 1 16 1 17 61 249 177 63 true) (55 4.0 1 3 131 715.19 0.06 17 42.07 12158.15 0.24 675.45 42 0 11 0 17 27 77 54 7 true) (210 5.0 1 3 687 4305.28 0.02 41.37 104.08 178095.18 1.44 9894.18 164 3 41 0 17 60 395 292 9 true) (72 4.0 1 4 162 879.05 0.05 19.85 44.28 17450.87 0.29 969.49 53 1 16 0 16 27 95 67 7 true) (24 1.0 1 1 40 175.69 0.19 5.23 33.59 919.01 0.06 51.06 13 2 7 0 8 13 23 17 1 true) (118 3.0 1 2 353 2050 0.03 31.2 65.71 63959.88 0.68 3553.33 87 1 23 0 16 40 197 156 5 true) (227 27.0 7 16 596 3869.14 0.03 30.14 128.36 116627.07 1.29 6479.28 168 21 30 4 20 70 385 211 53 true) (45 9.0 1 4 197 1158.88 0.04 23.33 49.67 27040.55 0.39 1502.25 36 2 4 1 20 39 106 91 17 true) (112 19.0 11 15 348 2206.27 0.04 24.75 89.13 54614.17 0.74 3034.12 93 2 10 5 20 61 197 151 37 true) (44 2.0 1 2 66 326.98 0.09 11.12 29.41 3635.21 0.11 201.96 22 12 7 0 14 17 39 27 3 true) (43 3.0 1 3 83 380.55 0.12 8.25 46.13 3139.55 0.13 174.42 24 4 8 0 8 16 50 33 5 true) (43 6.0 5 5 106 564.12 0.04 22.5 25.07 12692.8 0.19 705.16 32 0 9 0 20 20 61 45 11 true) (39 7.0 1 4 89 418.34 0.08 13.29 31.49 5557.93 0.14 308.77 34 0 3 0 12 14 58 31 13 true) (58 10.0 5 6 176 949.05 0.05 21.54 44.06 20441.03 0.32 1135.61 50 4 2 0 16 26 106 70 19 true) (37 8.0 1 4 99 475.93 0.09 11.65 40.86 5543.16 0.16 307.95 24 4 7 0 11 17 63 36 15 true) (18 2.0 1 2 33 132 0.1 10 13.2 1320 0.04 73.33 12 1 3 0 10 6 21 12 3 true) (49 4.0 1 4 126 618.27 0.06 16.67 37.1 10304.47 0.21 572.47 39 2 5 0 12 18 76 50 7 true) (65 6.0 1 6 139 712.97 0.06 16.25 43.88 11585.77 0.24 643.65 52 3 7 1 13 22 84 55 11 true) (32 6.0 1 6 87 446.25 0.06 16.94 26.34 7559.96 0.15 420 27 1 2 0 18 17 55 32 11 true) (15 2.0 1 1 19 74.23 0.33 3 24.74 222.69 0.02 12.37 4 0 0 0 8 18 24 22 3 true) (88 10.0 6 6 198 1123.14 0.06 17.83 63 20023.99 0.37 1112.44 66 7 11 0 16 35 120 78 19 true) (29 1.0 1 1 73 354.63 0.11 9.17 38.69 3250.8 0.12 180.6 20 0 7 0 11 18 43 30 1 true) (14 3.0 1 2 33 142.62 0.17 5.73 24.9 816.84 0.05 45.38 12 0 0 0 9 11 19 14 5 true) (14 2.0 1 2 61 307.71 0.11 9.42 32.65 2900.15 0.1 161.12 12 0 0 0 13 20 32 29 3 true) (14 3.0 1 3 42 189.99 0.15 6.54 29.06 1242.24 0.06 69.01 10 0 2 0 10 13 25 17 5 true) (81 7.0 1 7 207 1136.81 0.04 22.77 49.93 25882.81 0.38 1437.93 62 4 11 0 17 28 132 75 13 true) (13 2.0 1 1 31 131.69 0.21 4.73 27.86 622.51 0.04 34.58 10 0 1 0 8 11 18 13 3 true) (37 2.0 1 2 75 343.87 0.1 10.5 32.75 3610.66 0.11 200.59 26 5 5 0 7 17 24 51 3 true) (12 2.0 1 1 27 110.36 0.24 4.2 26.28 463.52 0.04 25.75 8 0 1 0 7 10 15 12 3 true) (11 3.0 1 2 34 151.62 0.14 7.2 21.06 1091.67 0.05 60.65 9 0 0 0 12 10 22 12 5 true) (10 2.0 1 2 18 64.53 0.14 7 9.22 451.71 0.02 25.09 8 0 0 0 8 4 11 7 3 true) (8 1.0 1 1 10 30 0.3 3.33 9 100 0.01 5.56 4 0 1 0 5 3 6 4 1 true) (8 1.0 1 1 10 30 0.3 3.33 9 100 0.01 5.56 4 0 1 0 5 3 6 4 1 true) (14 3.0 1 1 32 128 0.22 4.5 28.44 576 0.04 32 9 0 3 0 6 10 17 15 5 true) (6 2.0 1 1 15 53.77 0.4 2.5 21.51 134.44 0.02 7.47 2 0 2 0 5 7 8 7 3 true) (4 1.0 1 1 5 11.61 0.5 2 5.8 23.22 0 1.29 2 0 0 0 4 1 4 1 1 true) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 true) (4 1.0 1 1 4 8 0.67 1.5 5.33 12 0 0.67 2 0 0 0 3 1 3 1 1 true) (4 1.0 1 1 5 11.61 0.67 1.5 7.74 17.41 0 0.97 2 0 0 0 3 2 3 2 1 true) (3 1.0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 true) )))