;%%-*- 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: PC1/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 JM1, Naive Batrue generated PDs of 20% with PF of 25% ;% ;% -- 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. flight software ;% for earth orbiting satellite. ;% ;% -- 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: 1109 ;% 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. defects : {false,true} % module has/has not one or more ;% % reported defects ;% 8. Missing attributes: none ;% 9. Class Distribution: the class value (defects) is discrete ;% false: 77 = 6.94% ;% true: 1032 = 93.05% ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% (defun PC1 () (data :name 'PC1 :columns '(?$loc ?$v ?$ev ?$iv ?$n ?$v ?$l ?$d ?$i ?$e ?$b ?$t ?$lOCode ?$lOComment ?$lOBlank ?$lOCodeAndComment ?$uniq_Op ?$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 false) (1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 true) (91 9 3 2 318 2089.21 0.04 27.68 75.47 57833.24 0.7 3212.96 80 44 11 31 29 66 192 126 17 true) (109 21 5 18 381 2547.56 0.04 28.37 89.79 72282.68 0.85 4015.7 97 41 12 24 28 75 229 152 38 true) (505 106 41 82 2339 20696.93 0.01 75.93 272.58 1571506.88 6.9 87305.94 457 71 48 49 64 397 1397 942 178 true) (107 25 7 14 619 4282.78 0.02 52.91 80.95 226588.75 1.43 12588.26 103 32 4 39 35 86 359 260 40 true) (74 11 1 8 294 1917.93 0.03 28.77 66.66 55178.46 0.64 3065.47 60 71 14 49 29 63 169 125 21 true) (602 136 123 123 2785 25942.69 0.01 105.26 246.47 2730637.23 8.65 151702.06 600 40 2 225 99 538 1641 1144 236 true) (29 2 1 2 140 718.1 0.1 9.93 72.35 7127.8 0.24 395.99 28 3 1 8 8 27 73 67 3 true) (36 3 1 1 254 1447.91 0.04 23.72 61.05 34338.99 0.48 1907.72 34 2 2 10 15 37 137 117 5 true) (70 11 1 6 434 3047.71 0.04 26.63 114.46 81152.69 1.02 4508.48 57 14 13 18 28 102 240 194 21 true) (109 20 12 4 223 1322.55 0.04 23.91 55.32 31619.49 0.44 1756.64 89 32 20 17 23 38 144 79 39 true) (37 3 1 3 187 1095.44 0.04 24.97 43.87 27356.45 0.37 1519.8 35 15 2 24 21 37 99 88 5 true) (90 29 8 9 488 3387.95 0.05 20.69 163.74 70100.61 1.13 3894.48 66 38 24 17 21 102 287 201 57 true) (19 2 1 2 71 351.75 0.08 12.66 27.79 4451.81 0.12 247.32 18 25 1 9 15 16 44 27 3 true) (152 21 4 5 430 2850.62 0.03 33.3 85.6 94929.66 0.95 5273.87 150 62 2 39 26 73 243 187 41 true) (22 5 5 4 100 539.23 0.07 13.94 38.68 7516.89 0.18 417.61 22 0 0 12 17 25 59 41 9 true) (69 12 1 1 536 3745.93 0.04 23.38 160.24 87570.07 1.25 4865 68 15 1 17 21 106 300 236 23 true) (49 9 1 8 191 1166.73 0.05 21.31 54.76 24859.27 0.39 1381.07 46 1 3 10 25 44 116 75 12 true) (48 3 1 2 248 1470.82 0.03 33.59 43.78 49408.04 0.49 2744.89 37 27 11 23 23 38 137 111 5 true) (128 19 1 3 494 3310.02 0.02 50.76 65.2 168007.02 1.1 9333.72 107 62 21 34 34 70 285 209 37 true) (32 6 1 5 186 1116 0.03 30.72 36.33 34286 0.37 1904.78 30 6 2 11 28 36 107 79 11 true) (38 6 6 6 244 1447.1 0.05 21.71 66.64 31422.74 0.48 1745.71 38 6 0 20 19 42 148 96 11 true) (34 6 6 6 176 1026.58 0.06 17 60.39 17452.01 0.34 969.56 34 5 0 19 19 38 108 68 11 true) (16 3 1 3 60 300 0.11 9.33 32.14 2800 0.1 155.56 16 0 0 8 14 18 36 24 5 true) (15 4 3 2 86 457.69 0.07 14.73 31.08 6740.46 0.15 374.47 14 6 1 8 18 22 50 36 7 true) (14 3 1 3 47 215.49 0.16 6.43 33.52 1385.31 0.07 76.96 14 0 0 7 10 14 29 18 5 true) (22 5 5 4 100 535.76 0.07 14.52 36.9 7779.61 0.18 432.2 22 0 0 13 17 24 59 41 9 true) (22 3 1 3 74 370 0.12 8.11 45.57 3004.05 0.12 166.89 22 0 0 15 11 21 43 31 5 true) (12 2 1 2 41 187.98 0.16 6.07 30.96 1141.33 0.06 63.41 12 0 0 8 10 14 24 17 3 true) (25 8 1 4 110 611 0.08 12.13 50.38 7410.9 0.2 411.72 20 9 5 3 16 31 63 47 15 true) (23 4 1 4 101 544.62 0.08 12.22 44.56 6656.52 0.18 369.81 23 0 0 12 15 27 57 44 7 true) (8 1 1 1 49 238.04 0.14 7.03 33.87 1672.9 0.08 92.94 8 4 0 6 11 18 26 23 1 true) (47 7 1 6 149 890.61 0.08 13.2 67.47 11756.11 0.3 653.12 40 4 7 9 18 45 83 66 13 true) (19 3 3 2 77 391.73 0.08 12.25 31.98 4798.75 0.13 266.6 15 14 4 28 14 20 42 35 5 true) (64 8 1 8 198 1174.29 0.04 22.28 52.7 26165.5 0.39 1453.64 48 73 16 32 22 39 119 79 12 true) (110 25 21 9 663 4404.88 0.02 45.1 97.66 198677.1 1.47 11037.62 97 23 13 37 23 77 361 302 49 true) (58 6 1 3 314 1961.85 0.05 20 98.09 39236.98 0.65 2179.83 58 17 0 18 16 60 164 150 11 true) (167 26 1 23 671 4888.5 0.04 22.35 218.7 109272.46 1.63 6070.69 166 49 1 39 20 136 367 304 51 true) (60 12 4 7 183 1080.96 0.04 26.43 40.9 28568.25 0.36 1587.13 58 0 2 13 25 35 109 74 16 true) (14 3 1 1 46 218.72 0.08 12.5 17.5 2734.06 0.07 151.89 13 3 1 2 15 12 26 20 5 true) (15 1 1 1 53 232.79 0.26 3.91 59.59 909.35 0.08 50.52 13 7 2 8 5 16 28 25 1 true) (120 7 3 4 1002 7090.97 0.02 59.93 118.33 424927.99 2.36 23607.11 120 42 0 36 28 107 544 458 13 true) (28 6 1 5 86 477.69 0.07 13.73 34.79 6559.11 0.16 364.4 28 0 0 7 21 26 52 34 8 true) (64 12 8 7 368 2358.66 0.03 36.58 64.48 86270.84 0.79 4792.82 54 18 10 80 26 59 202 166 23 true) (61 7 1 4 304 1893.56 0.05 19.66 96.31 37229.33 0.63 2068.3 49 0 12 11 16 59 159 145 13 true) (3 1 1 1 10 33.22 0.33 3 11.07 99.66 0.01 5.54 3 0 0 1 6 4 6 4 1 true) (141 24 9 18 657 4553.49 0.02 51.91 87.72 236376.96 1.52 13132.05 141 50 0 56 32 90 365 292 36 true) (6 1 1 1 30 139.32 0.15 6.5 21.43 905.55 0.05 50.31 6 1 0 7 13 12 18 12 1 true) (93 17 4 12 409 2680.83 0.03 38.18 70.21 102358.84 0.89 5686.6 93 27 0 31 28 66 229 180 24 true) (6 1 1 1 30 139.32 0.15 6.5 21.43 905.55 0.05 50.31 6 1 0 7 13 12 18 12 1 true) (31 7 4 4 108 603.18 0.07 14.1 42.78 8504.78 0.2 472.49 28 1 3 9 18 30 61 47 13 true) (153 25 6 13 841 5942.58 0.02 61.92 95.98 367939.9 1.98 20441.11 153 55 0 55 33 101 462 379 40 true) (63 17 9 7 429 2843.99 0.03 32 88.87 91007.81 0.95 5055.99 57 6 6 5 24 75 229 200 33 true) (106 29 20 5 539 3676.08 0.03 39.86 92.23 146524.08 1.23 8140.23 106 39 0 32 28 85 297 242 57 true) (23 5 4 3 113 650.3 0.05 20.26 32.1 13174.23 0.22 731.9 21 8 2 9 25 29 66 47 9 true) (29 5 5 5 164 920.81 0.05 20.61 44.67 18980.62 0.31 1054.48 29 9 0 7 18 31 93 71 9 true) (84 9 1 3 876 6498.04 0.02 46.39 140.08 301439.38 2.17 16746.63 84 20 0 27 31 140 457 419 17 true) (17 1 1 1 102 550.02 0.07 13.54 40.63 7446.38 0.18 413.69 16 9 1 12 16 26 58 44 1 true) (6 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 6 0 0 0 4 5 6 5 1 true) (15 4 3 2 86 457.69 0.07 14.73 31.08 6740.46 0.15 374.47 14 5 1 7 18 22 50 36 7 true) (29 5 5 5 146 815.4 0.05 18.6 43.84 15166.52 0.27 842.58 29 7 0 5 18 30 84 62 9 true) (5 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 5 0 0 0 4 5 6 5 1 true) (15 3 1 3 53 249.12 0.13 7.7 32.35 1918.25 0.08 106.57 15 0 0 9 11 15 32 21 5 true) (5 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 5 0 0 0 4 5 6 5 1 true) (6 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 6 0 0 0 4 5 6 5 1 true) (23 4 1 4 101 548.05 0.08 11.79 46.5 6459.19 0.18 358.84 23 0 0 12 15 28 57 44 7 true) (5 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 5 0 0 0 4 5 6 5 1 true) (5 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 5 0 0 0 4 5 6 5 1 true) (43 9 7 7 186 1084.92 0.07 13.35 81.27 14482.39 0.36 804.58 43 1 0 16 14 43 104 82 17 true) (14 4 1 2 69 353.92 0.09 10.88 32.54 3848.89 0.12 213.83 12 7 2 4 15 20 40 29 7 true) (16 5 1 2 90 482.18 0.08 12.16 39.65 5863.3 0.16 325.74 13 4 3 3 16 25 52 38 9 true) (3 1 1 1 18 68.53 0.22 4.5 15.23 308.4 0.02 17.13 3 0 0 2 9 5 13 5 1 true) (24 4 1 1 116 585.15 0.06 16 36.57 9362.4 0.2 520.13 19 6 5 7 12 21 60 56 7 true) (17 4 1 1 79 391.38 0.08 12.28 31.88 4805.3 0.13 266.96 15 6 2 8 13 18 45 34 7 true) (6 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 6 0 0 0 4 5 6 5 1 true) (27 7 1 4 94 506.88 0.05 21.45 23.63 10872.53 0.17 604.03 27 4 0 20 22 20 55 39 12 true) (6 1 1 1 11 34.87 0.5 2 17.43 69.74 0.01 3.87 6 0 0 0 4 5 6 5 1 true) (10 1 1 1 25 97.67 0.27 3.67 26.64 358.13 0.03 19.9 8 2 2 6 6 9 14 11 1 false) (29 6 1 3 79 398.51 0.08 12.92 30.85 5147.38 0.13 285.97 29 2 0 21 15 18 48 31 8 false) (8 2 1 1 11 33 0.22 4.5 7.33 148.5 0.01 8.25 6 3 2 2 6 2 8 3 3 false) (108 22 5 17 464 3215.86 0.03 33.04 97.32 106264.81 1.07 5903.6 107 35 1 43 31 91 270 194 39 false) (14 7 1 2 82 430.33 0.07 14.55 29.59 6259.35 0.14 347.74 14 0 0 1 16 22 42 40 13 false) (0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 false) (13 4 1 3 42 181.52 0.08 12.75 14.24 2314.39 0.06 128.58 13 0 0 2 12 8 25 17 7 false) (0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 false) (56 1 1 1 327 2078.92 0.02 44.27 46.96 92035.5 0.69 5113.08 49 1 7 2 34 48 202 125 29 false) (0 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 2 0 2 0 1 false) (0 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