;%%-*- text -*- ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ;% This is a PROMISE 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: JM1/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 25% with PF of 20% ;% ;% -- 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: ;% ;% -- JM1 is written in "C" and is a real-time predictive ground system: ;% Uses simulations to generate predictions ;% ;% -- 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: 10885 ;% 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: 2106 = 19.35% ;% true: 8779 = 80.65% ;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% (defun JM1 () (data :name 'JM1 :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 false) (1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 true) (72 7 1 6 198 1134.13 0.05 20.31 55.85 23029.1 0.38 1279.39 51 10 8 1 17 36 112 86 13 true) (190 3 1 3 600 4348.76 0.06 17.06 254.87 74202.67 1.45 4122.37 129 29 28 2 17 135 329 271 5 true) (37 4 1 4 126 599.12 0.06 17.19 34.86 10297.3 0.2 572.07 28 1 6 0 11 16 76 50 7 true) (31 2 1 2 111 582.52 0.08 12.25 47.55 7135.87 0.19 396.44 19 0 5 0 14 24 69 42 3 true) (78 9 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 true) (8 1 1 1 16 50.72 0.36 2.8 18.11 142.01 0.02 7.89 5 0 1 0 4 5 9 7 1 true) (24 2 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 true) (143 22 20 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 true) (73 10 4 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 true) (83 11 10 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 true) (12 3 1 1 37 167.37 0.15 6.87 24.34 1150.68 0.06 63.93 8 0 2 0 11 12 22 15 5 true) (48 4 1 4 129 695.61 0.06 17.35 40.1 12067.3 0.23 670.41 29 1 16 0 19 23 87 42 7 true) (68 8 1 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 true) (138 22 10 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 true) (10 1 1 1 9 27 0.5 2 13.5 54 0.01 3 2 0 6 0 4 4 5 4 1 true) (250 49 34 16 1469 9673.31 0.01 97 99.72 938311.06 3.22 52128.39 139 92 17 0 32 64 1081 388 97 true) (77 8 1 1 284 1160.84 0.02 40.95 28.35 47536.38 0.39 2640.91 59 0 16 0 7 10 167 117 15 true) (85 9 1 7 277 1714.58 0.03 32.64 52.53 55961.02 0.57 3108.95 69 0 14 0 26 47 161 118 13 true) (110 17 13 8 322 2069.26 0.03 33.41 61.94 69127.22 0.69 3840.4 81 13 14 0 27 59 176 146 33 true) (49 6 6 3 171 927.89 0.04 25.33 36.63 23506.58 0.31 1305.92 34 0 13 0 19 24 107 64 11 true) (187 35 26 16 526 3296.33 0.02 42.56 77.45 140300.03 1.1 7794.45 164 1 16 0 21 56 299 227 69 true) (27 6 6 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 true) (38 8 1 3 145 673.36 0.05 20.53 32.8 13824.9 0.22 768.05 29 0 7 0 9 16 72 73 15 true) (294 43 33 24 814 5811.59 0.02 40.88 142.15 237606.8 1.94 13200.38 223 41 26 2 28 113 484 330 85 true) (29 3 1 3 88 465.12 0.08 12.04 38.63 5599.99 0.16 311.11 21 0 6 0 14 25 45 43 5 true) (160 5 4 3 698 4862.12 0.03 33.11 146.86 160969.13 1.62 8942.73 123 11 23 1 22 103 388 310 9 true) (94 16 9 5 218 1236.59 0.03 34.52 35.83 42683.63 0.41 2371.31 66 19 6 1 22 29 127 91 31 true) (48 3 1 3 157 927.38 0.08 13.09 70.84 12140.27 0.31 674.46 34 1 9 0 16 44 85 72 5 true) (14 2 1 2 31 129.27 0.19 5.2 24.86 672.19 0.04 37.34 8 1 3 0 8 10 18 13 3 true) (32 6 4 4 116 595 0.06 16.67 35.7 9916.61 0.2 550.92 26 0 4 0 14 21 66 50 11 true) (11 1 1 1 9 27 0.5 2 13.5 54 0.01 3 2 0 6 0 4 4 5 4 1 true) (20 3 1 1 31 118.03 0.11 9 13.11 1062.25 0.04 59.01 13 4 0 0 9 5 21 10 5 true) (39 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 true) (63 21 21 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 41 true) (78 6 1 5 192 1072.31 0.04 28.21 38.01 30254.54 0.36 1680.81 45 6 19 6 20 28 113 79 11 true) (209 26 17 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 47 true) (6 2 1 1 21 82.04 0.17 6 13.67 492.27 0.03 27.35 4 0 0 0 10 5 15 6 3 true) (44 13 13 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 true) (24 3 1 1 52 224.74 0.06 16.71 13.45 3756.37 0.07 208.69 18 0 4 0 13 7 34 18 5 true) (23 2 1 2 51 233.83 0.17 6 38.97 1403 0.08 77.94 19 0 2 0 9 15 31 20 3 true) (69 6 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 true) (148 19 10 11 516 3223.93 0.03 34.67 93 111762.93 1.07 6209.05 118 7 16 1 19 57 308 208 37 true) (83 20 15 7 226 1300.6 0.03 33.19 39.19 43166.62 0.43 2398.15 62 7 12 0 25 29 149 77 39 true) (53 4 1 4 106 539.27 0.07 14.86 36.3 8012.03 0.18 445.11 36 7 8 0 13 21 58 48 7 true) (97 15 12 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 29 true) (25 2 1 2 71 355 0.15 6.46 54.98 2292.07 0.12 127.34 20 3 0 0 9 23 38 33 3 true) (466 110 95 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 219 true) (110 27 14 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53 true) (113 19 18 11 491 3179.59 0.02 45.4 70.04 144343.07 1.06 8019.06 92 9 12 0 26 63 271 220 37 true) (119 9 7 7 300 1862.84 0.04 24.96 74.63 46500.6 0.62 2583.37 79 21 15 2 21 53 174 126 17 true) (15 3 1 2 47 215.49 0.11 9 23.94 1939.44 0.07 107.75 10 0 2 1 12 12 29 18 5 true) (44 10 8 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 true) (124 55 55 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 109 true) (8 1 1 1 15 51.89 0.34 2.92 17.79 151.35 0.02 8.41 4 0 2 0 5 6 8 7 1 true) (8 1 1 1 15 51.89 0.34 2.92 17.79 151.35 0.02 8.41 4 0 2 0 5 6 8 7 1 true) (162 27 23 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53 true) (113 27 20 16 445 2829.11 0.02 41.45 68.26 117259.19 0.94 6514.4 97 0 9 5 25 57 256 189 53 true) (8 1 1 1 15 51.89 0.34 2.92 17.79 151.35 0.02 8.41 4 0 2 0 5 6 8 7 1 true) (8 1 1 1 14 46.51 0.33 3 15.5 139.52 0.02 7.75 4 0 2 0 5 5 8 6 1 true) (9 2 1 1 17 62.91 0.24 4.08 15.41 256.87 0.02 14.27 6 0 1 0 7 6 10 7 3 true) (179 36 27 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 71 true) (171 16 1 16 644 4230.99 0.03 34.25 123.53 144911.31 1.41 8050.63 115 8 24 22 19 76 370 274 31 true) (77 15 11 15 273 1656.04 0.06 18.04 91.8 29873.71 0.55 1659.65 62 4 9 0 16 51 158 115 29 true) (8 1 1 1 15 51.89 0.34 2.92 17.79 151.35 0.02 8.41 4 0 2 0 5 6 8 7 1 true) (12 4 1 1 40 183.4 0.13 8 22.92 1467.19 0.06 81.51 8 0 2 0 12 12 24 16 7 true) (42 9 1 4 135 708.47 0.07 14.88 47.63 10538.49 0.24 585.47 28 6 6 0 17 30 116 74 27 true) (14 1 1 1 79 346.99 0.22 4.59 75.63 1592.09 0.12 88.45 12 0 0 0 4 17 40 39 1 true) (19 5 1 1 62 267.96 0.05 21 12.76 5627.15 0.09 312.62 15 0 2 0 12 8 34 28 9 true) (88 8 1 7 250 1372.96 0.03 33.25 41.29 45651.03 0.46 2536.17 52 19 12 3 21 24 174 76 15 true) (13 2 1 2 50 235.02 0.24 4.24 55.47 995.75 0.08 55.32 10 0 1 0 7 19 27 23 3 true) (46 4 1 4 80 396.34 0.08 11.92 33.26 4723 0.13 262.39 28 1 14 0 13 18 47 33 7 true) (129 30 18 25 402 2583.36 0.02 46.42 55.65 119927.45 0.86 6662.64 111 1 14 1 34 52 260 142 58 true) (20 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 true) (22 1 1 1 107 508.77 0.08 12.06 42.19 6135.2 0.17 340.84 16 0 4 0 10 17 66 41 1 true) (32 2 1 2 157 851.92 0.08 12.2 69.81 10396.13 0.28 577.56 23 3 4 0 11 32 86 71 3 true) (13 1 1 1 45 171.33 0.07 14.4 11.9 2467.17 0.06 137.06 9 0 2 0 9 5 29 16 1 true) (12 3 1 2 22 88 0.12 8.33 10.56 733.33 0.03 40.74 9 0 1 0 10 6 12 10 5 true) (6 1 1 1 11 34.87 0.32 3.12 11.16 108.97 0.01 6.05 3 0 1 0 5 4 6 5 1 true) (17 3 1 1 28 109.39 0.13 7.43 14.73 812.63 0.04 45.15 13 1 1 0 8 7 15 13 5 true) (5 1 1 1 15 53.77 0.24 4.2 12.8 225.85 0.02 12.55 3 0 0 0 7 5 9 6 1 true) (227 55 22 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 109 true) (68 5 1 4 309 1725.75 0.03 32.04 53.85 55302.55 0.58 3072.36 53 1 8 0 15 32 167 140 9 true) (37 4 1 4 73 371.38 0.1 10.15 36.59 3769.56 0.12 209.42 23 6 6 0 14 20 44 29 7 true) (106 12 5 9 288 1759.26 0.03 34.94 50.35 61473.97 0.59 3415.22 76 17 10 1 25 44 165 123 22 true) (18 1 1 1 18 72 0.3 3.3 21.82 237.6 0.02 13.2 14 0 2 0 6 10 7 11 1 true) (100 11 1 7 277 1636.21 0.04 28.25 57.92 46222.9 0.55 2567.94 64 17 17 0 20 40 164 113 21 true) (99 14 5 11 277 1802.66 0.04 26.16 68.92 47150.8 0.6 2619.49 85 4 10 0 27 64 153 124 24 true) (53 6 1 3 176 942.93 0.03 30.23 31.19 28502.18 0.31 1583.45 37 2 10 2 19 22 106 70 11 true) (4 1 1 1 10 30 0.5 2 15 60 0.01 3.33 2 0 0 0 4 4 6 4 1 true) (4 1 1 1 10 30 0.5 2 15 60 0.01 3.33 2 0 0 0 4 4 6 4 1 true) (4 1 1 1 10 30 0.5 2 15 60 0.01 3.33 2 0 0 0 4 4 6 4 1 true) (4 1 1 1 10 30 0.5 2 15 60 0.01 3.33 2 0 0 0 4 4 6 4 1 true) (4 1 1 1 10 30 0.5 2 15 60 0.01 3.33 2 0 0 0 4 4 6 4 1 true) (50 5 5 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 true) (14 3 1 1 21 82.04 0.13 8 10.26 656.36 0.03 36.46 7 2 3 0 10 5 13 8 5 true) (11 2 1 1 30 120 0.11 9.17 13.09 1100 0.04 61.11 8 0 1 0 10 6 19 11 3 true) (174 15 11 7 448 3020.18 0.04 24.66 122.46 74486 1.01 4138.1 107 28 37 0 21 86 246 202 29 true) (33 4 1 3 98 485.51 0.09 10.71 45.31 5201.91 0.16 288.99 29 0 2 0 10 21 53 45 7 true) (4 1 1 1 8 24 0.4 2.5 9.6 60 0.01 3.33 2 0 0 0 5 3 5 3 1 true) (13 2 1 2 59 236 0.13 7.8 30.26 1840.8 0.08 102.27 9 0 2 0 6 10 33 26 3 true) (135 11 1 9 352 2317.91 0.05 18.58 124.77 43059.58 0.77 2392.2 92 14 24 3 18 78 191 161 21 true) (49 6 3 5 137 760.98 0.04 23.76 32.03 18080.85 0.25 1004.49 38 0 9 0 22 25 83 54 11 true) (45 6 1 3 226 1268.92 0.04 27.36 46.38 34716.98 0.42 1928.72 40 1 2 0 17 32 123 103 11 true) (65 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 true) (40 5 3 5 131 723.59 0.04 23.38 30.96 16913.84 0.24 939.66 32 0 5 0 22 24 80 51 9 true) (59 7 5 7 195 1089.07 0.05 20.64 52.77 22476.1 0.36 1248.67 34 13 8 2 19 29 132 63 13 true) (31 4 3 4 107 576.98 0.05 20.5 28.15 11828.05 0.19 657.11 23 0 6 0 21 21 66 41 7 true) (45 9 9 5 195 1094.87 0.03 28.97 37.8 31713.43 0.36 1761.86 36 4 5 0 20 29 111 84 17 true) (31 6 1 3 69 338.58 0.07 14.86 22.79 5030.26 0.11 279.46 21 3 5 0 16 14 43 26 11 true) (28 2 1 2 75 339.27 0.07 15.27 22.21 5181.53 0.11 287.86 17 8 1 0 12 11 47 28 3 true) (49 3 1 3 121 639.53 0.05 20.86 30.65 13343 0.21 741.28 33 2 4 8 17 22 67 54 5 true) (30 5 3 4 100 545.94 0.05 21 26 11464.81 0.18 636.93 21 1 5 1 22 22 58 42 9 true) (146 15 9 9 423 2658.73 0.02 43.15 61.62 114716.17 0.89 6373.12 93 23 13 12 27 51 260 163 29 true) (127 16 10 9 462 3042.25 0.02 42 72.43 127774.61 1.01 7098.59 105 3 11 8 28 68 258 204 31 true) (85 18 11 1 350 2083.96 0.02 43.89 47.48 91475.26 0.69 5081.96 68 3 11 0 24 38 211 139 35 true) (28 5 3 4 98 528.45 0.05 20 26.42 10568.94 0.18 587.16 21 0 5 0 21 21 58 40 9 true) (21 4 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 true) (127 23 21 8 323 2081.07 0.03 36.58 56.89 76123.38 0.69 4229.08 88 31 8 0 30 57 184 139 45 true) (45 12 1 6 160 813.99 0.04 28.44 28.62 23153.61 0.27 1286.31 34 1 8 0 16 18 96 64 23 true) (31 3 1 2 87 438.86 0.09 10.86 40.42 4764.79 0.15 264.71 24 0 5 0 12 21 49 38 5 true) (11 3 1 2 39 165.67 0.09 11.69 14.17 1936.26 0.06 107.57 9 0 0 0 11 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