%%-*- 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: CM1/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 CM1, Naive Bayes generated PDs of 30% 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: % % -- CM1 is a NASA spacecraft instrument written in "C". % % -- 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: 498 % 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: 449 = 90.16% % true: 49 = 9.83% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @relation CM1 @attribute loc numeric @attribute v(g) numeric @attribute ev(g) numeric @attribute iv(g) numeric @attribute n numeric @attribute v numeric @attribute l numeric @attribute d numeric @attribute i numeric @attribute e numeric @attribute b numeric @attribute t numeric @attribute lOCode numeric @attribute lOComment numeric @attribute lOBlank numeric @attribute locCodeAndComment numeric @attribute uniq_Op numeric @attribute uniq_Opnd numeric @attribute total_Op numeric @attribute total_Opnd numeric @attribute branchCount numeric @attribute defects {false,true} @data 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 24,5,1,3,63,309.13,0.11,9.5,32.54,2936.77,0.1,163.15,1,0,6,0,15,15,44,19,9,false 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3,1,1,1,22,85.95,0.2,5,17.19,429.76,0.03,23.88,1,0,3,0,10,5,17,5,1,false 3,1,1,1,21,79.95,0.22,4.5,17.77,359.8,0.03,19.99,1,0,3,0,9,5,16,5,1,false 6,2,1,2,27,112.59,0.16,6.29,17.91,707.7,0.04,39.32,2,6,4,0,11,7,19,8,3,false 7,1,1,1,13,43.19,0.4,2.5,17.27,107.96,0.01,6,2,2,3,0,5,5,8,5,1,false 3,1,1,1,22,85.95,0.2,5,17.19,429.76,0.03,23.88,1,0,3,0,10,5,17,5,1,false 3,1,1,1,21,79.95,0.22,4.5,17.77,359.8,0.03,19.99,1,0,3,0,9,5,16,5,1,false 6,2,1,2,27,112.59,0.16,6.29,17.91,707.7,0.04,39.32,2,6,4,0,11,7,19,8,3,false 2,1,1,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,false 2,1,1,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,false 3,1,1,1,1,0,0,0,0,0,0,0,0,12,8,0,1,0,1,0,1,false 4,1,1,1,5,11.61,0.5,2,5.8,23.22,0,1.29,2,0,3,0,4,1,4,1,1,false 9,2,1,1,15,51.89,0.23,4.38,11.86,227.03,0.02,12.61,0,8,2,0,7,4,10,5,3,false 16,1,1,1,88,408.66,0.07,15.32,26.67,6261.24,0.14,347.85,0,4,7,0,11,14,49,39,1,false 5,1,1,1,5,11.61,0.67,1.5,7.74,17.41,0,0.97,1,1,2,0,3,2,3,2,1,false 4,1,1,1,15,47.55,0.27,3.75,12.68,178.31,0.02,9.91,3,1,1,0,5,4,9,6,1,false 5,1,1,1,27,105.49,0.16,6.29,16.78,663.06,0.04,36.84,4,5,2,0,8,7,16,11,1,false 9,2,1,1,39,171.3,0.1,10,17.13,1713,0.06,95.17,2,5,3,0,12,9,24,15,3,false 18,3,1,1,47,215.49,0.12,8.03,26.81,1732.23,0.07,96.24,4,0,2,0,11,13,28,19,4,false 38,10,4,4,142,835.34,0.04,26.1,32,21802.25,0.28,1211.24,4,33,27,0,29,30,88,54,13,false 14,6,5,1,59,277.33,0.06,17.6,15.76,4880.94,0.09,271.16,2,2,6,0,16,10,37,22,11,false 47,13,3,2,150,863.23,0.04,27.86,30.99,24047.21,0.29,1335.96,7,14,20,0,26,28,90,60,21,false 11,2,1,1,29,125.34,0.16,6.11,20.51,765.94,0.04,42.55,0,1,4,0,11,9,19,10,3,false 11,2,1,1,28,121.01,0.16,6.11,19.8,739.53,0.04,41.08,0,2,5,0,11,9,18,10,3,false 11,2,1,1,32,142.7,0.1,9.63,14.83,1373.5,0.05,76.31,1,2,3,0,14,8,21,11,3,false 21,4,4,4,96,492.41,0.1,10.5,46.9,5170.32,0.16,287.24,3,7,7,0,15,20,68,28,7,false 23,4,4,4,96,492.41,0.1,10.5,46.9,5170.32,0.16,287.24,1,5,7,0,15,20,68,28,7,false 13,3,1,3,49,240.44,0.14,7.26,33.1,1746.71,0.08,97.04,0,0,0,0,13,17,30,19,5,false 21,3,1,3,72,377.85,0.09,10.93,34.57,4129.37,0.13,229.41,0,0,0,0,17,21,45,27,5,false 21,3,1,3,72,380.55,0.1,10.43,36.48,3969.82,0.13,220.55,0,0,0,0,17,22,45,27,5,false 22,4,4,3,98,531.77,0.08,12.75,41.71,6780.12,0.18,376.67,0,0,0,0,17,26,59,39,7,false 41,8,6,6,187,1113.43,0.07,14.8,75.22,16481.41,0.37,915.63,1,9,2,0,19,43,120,67,15,false 22,4,3,2,64,310.91,0.07,14.77,21.05,4591.91,0.1,255.11,0,0,5,0,16,13,40,24,7,false 9,3,3,1,24,91.38,0.1,10,9.14,913.77,0.03,50.76,0,0,0,0,10,4,16,8,5,false 50,10,10,9,136,733.36,0.09,11.21,65.44,8218.63,0.24,456.59,0,0,12,0,13,29,86,50,19,false 29,10,10,8,118,640.3,0.05,20.43,31.33,13084.38,0.21,726.91,1,0,8,0,20,23,71,47,19,false 50,10,10,9,138,757.88,0.1,10.16,74.62,7697.18,0.25,427.62,0,0,12,0,13,32,88,50,19,false 29,19,1,8,216,1310.28,0.05,18.55,70.63,24306.94,0.44,1350.39,8,0,5,0,18,49,115,101,37,false 29,7,5,2,100,508.75,0.04,25.33,20.08,12888.24,0.17,716.01,0,9,5,0,19,15,60,40,13,false 9,1,1,1,81,366.41,0.05,20.22,18.12,7409.59,0.12,411.64,0,2,3,0,14,9,55,26,1,false 16,4,4,1,135,675,0.04,24.37,27.7,16447.5,0.23,913.75,0,3,4,0,17,15,92,43,7,false 9,1,1,1,25,104.25,0.2,5,20.85,521.24,0.03,28.96,0,0,0,0,9,9,15,10,1,false 6,1,1,1,19,70.31,0.29,3.43,20.51,241.06,0.02,13.39,0,0,0,0,6,7,11,8,1,false 30,7,3,4,171,955.03,0.03,30.04,31.79,28687.59,0.32,1593.75,0,2,7,0,22,26,100,71,13,false 14,2,1,2,20,71.7,0.35,2.86,25.09,204.86,0.02,11.38,0,0,2,0,5,7,12,8,3,false 56,22,1,12,455,2982.34,0.02,53.98,55.25,160995.7,0.99,8944.21,0,1,10,0,35,59,273,182,40,false 21,3,1,3,214,1207.79,0.04,26.67,45.29,32207.61,0.4,1789.31,1,10,7,0,20,30,134,80,5,false 14,1,1,1,80,380.39,0.11,9.5,40.04,3613.71,0.13,200.76,3,12,7,0,9,18,42,38,1,false 7,1,1,1,8,24,0.5,2,12,48,0.01,2.67,0,0,1,0,4,4,4,4,1,false 16,3,3,1,40,169.92,0.17,5.85,29.05,994.02,0.06,55.22,0,0,3,0,9,10,27,13,5,false 22,3,1,3,229,1335.73,0.04,25.08,53.25,33504.61,0.45,1861.37,1,4,5,0,21,36,143,86,5,false 27,4,1,3,75,360.55,0.09,11.7,30.82,4218.45,0.12,234.36,0,3,9,0,13,15,48,27,7,false 3,1,1,1,12,39.86,0.29,3.5,11.39,139.52,0.01,7.75,0,0,0,0,7,3,9,3,1,false 7,2,1,1,27,105.49,0.08,12,8.79,1265.83,0.04,70.32,0,0,1,0,10,5,15,12,3,false 3,1,1,1,14,48.43,0.23,4.38,11.07,211.89,0.02,11.77,0,0,0,0,7,4,9,5,1,false 7,1,1,1,29,110.41,0.21,4.88,22.65,538.26,0.04,29.9,0,0,0,0,6,8,16,13,1,false 9,3,1,1,41,170.97,0.07,14.14,12.09,2417.96,0.06,134.33,0,0,1,0,11,7,23,18,5,false 11,3,1,1,49,215.22,0.07,14.67,14.67,3156.61,0.07,175.37,0,0,1,0,12,9,27,22,5,false 4,1,1,1,13,39,0.33,3,13,117,0.01,6.5,0,0,0,0,4,4,7,6,1,false 3,1,1,1,7,19.65,0.4,2.5,7.86,49.13,0.01,2.73,0,0,0,0,5,2,5,2,1,false 12,3,1,1,51,227.43,0.1,10.42,21.83,2369.07,0.08,131.62,0,0,1,0,10,12,26,25,5,false 31,4,1,2,141,829.45,0.05,21.52,38.55,17846.19,0.28,991.46,1,19,15,0,27,32,90,51,7,true 29,5,1,3,111,641.73,0.08,12.33,52.03,7914.68,0.21,439.7,4,22,27,0,22,33,74,37,9,true 71,10,8,9,211,1251.39,0.04,27.11,46.15,33930.43,0.42,1885.02,6,45,30,0,26,35,138,73,19,true 15,2,1,2,74,385.5,0.07,14.25,27.05,5493.37,0.13,305.19,0,7,9,0,19,18,47,27,3,true 33,6,1,5,172,989.84,0.05,18.46,53.63,18269.63,0.33,1014.98,7,30,31,0,19,35,104,68,11,true 92,4,1,4,822,6234.84,0.04,26.64,234.02,166113.93,2.08,9228.55,7,46,62,0,24,168,449,373,7,true 44,7,1,7,269,1746.31,0.05,22.12,78.96,38620.28,0.58,2145.57,6,14,36,0,25,65,154,115,13,true 143,13,1,12,695,4996.93,0.04,28.52,175.18,142535.33,1.67,7918.63,11,57,93,0,24,122,405,290,25,true 196,37,21,26,1177,9135.35,0.01,71.54,127.7,653537.1,3.05,36307.62,14,170,155,0,52,165,723,454,71,true 12,1,1,1,37,159.91,0.11,9,17.77,1439.2,0.05,79.96,0,1,7,0,12,8,25,12,1,true 12,1,1,1,37,159.91,0.11,9,17.77,1439.2,0.05,79.96,0,1,7,0,12,8,25,12,1,true 133,30,1,26,605,4185.91,0.01,77.46,54.04,324260.81,1.4,18014.49,3,91,55,0,50,71,385,220,34,true 28,3,1,2,104,557.19,0.07,15.24,36.57,8490.44,0.19,471.69,0,11,9,0,20,21,72,32,5,true 91,20,9,4,568,3740.26,0.02,51.47,72.67,192513.32,1.25,10695.18,20,96,46,0,28,68,318,250,39,true 41,4,3,3,129,745.8,0.08,13.19,56.52,9840.36,0.25,546.69,0,12,1,0,19,36,79,50,7,true 149,20,1,18,702,5081.36,0.03,29.58,171.78,150305.78,1.69,8350.32,0,62,10,0,32,119,482,220,37,true 411,73,30,41,1500,12749.77,0.02,43.41,293.68,553518.63,4.25,30751.03,45,339,42,0,48,314,932,568,141,true 121,15,3,12,537,3817.28,0.04,23.26,164.09,88801.93,1.27,4933.44,3,29,16,0,24,114,316,221,29,true 130,20,9,10,607,4282.56,0.02,51.6,83,220979.91,1.43,12276.66,23,65,12,0,38,95,349,258,37,true 79,12,4,6,201,1227.81,0.07,13.73,89.42,16858.82,0.41,936.6,14,78,2,0,17,52,117,84,23,true 57,9,1,7,213,1318.43,0.03,29.27,45.05,38588.27,0.44,2143.79,6,7,6,0,32,41,138,75,15,true 49,7,1,5,229,1341.48,0.07,13.6,98.6,18250.34,0.45,1013.91,6,4,3,0,15,43,151,78,13,true 58,10,5,1,187,1038.71,0.03,33.52,30.99,34816.7,0.35,1934.26,6,6,9,0,21,26,104,83,19,true 28,3,1,3,148,835.29,0.05,18.67,44.75,15592.09,0.28,866.23,2,11,40,0,20,30,92,56,5,true 13,3,1,3,38,164.23,0.14,7.33,22.4,1204.38,0.05,66.91,2,0,0,0,11,9,26,12,5,true 71,8,1,7,307,1993,0.05,21.94,90.85,43721.41,0.66,2428.97,6,39,25,0,26,64,199,108,15,true 22,2,1,2,114,575.06,0.06,15.43,37.27,8872.37,0.19,492.91,1,19,27,0,12,21,60,54,3,true 22,5,5,3,84,440.83,0.08,12.55,35.13,5531.32,0.15,307.3,0,17,9,0,17,21,53,31,9,true 59,7,1,7,349,2318.71,0.05,22.14,104.75,51324.87,0.77,2851.38,16,23,32,0,26,74,223,126,13,true 19,1,1,1,118,561.08,0.08,12,46.76,6732.92,0.19,374.05,1,3,10,0,8,19,61,57,1,true 89,24,9,19,402,2603.24,0.02,47.6,54.69,123914.45,0.87,6884.14,4,62,30,0,34,55,248,154,32,true 32,4,1,4,121,648.26,0.05,18.57,34.91,12037.08,0.22,668.73,3,24,13,0,19,22,78,43,7,true 74,11,11,9,354,2274.9,0.03,36.74,61.92,83581.43,0.76,4643.41,7,28,31,0,32,54,230,124,21,true 32,4,1,4,117,574.11,0.04,22.75,25.24,13060.92,0.19,725.61,0,4,17,0,14,16,65,52,5,true 107,18,9,10,516,3366.16,0.02,64.92,51.85,218524.86,1.12,12140.27,9,73,51,0,37,55,323,193,35,true 7,1,1,1,30,122.62,0.18,5.63,21.8,689.76,0.04,38.32,0,0,0,0,9,8,20,10,1,true 19,3,1,1,85,439.44,0.1,9.64,45.57,4237.49,0.15,235.42,2,22,12,0,15,21,58,27,5,true 18,3,1,3,60,297.25,0.11,9.47,31.39,2815.15,0.1,156.4,3,34,14,0,14,17,37,23,5,true 22,3,1,3,81,439.53,0.09,11.08,39.66,4871.43,0.15,270.63,3,11,10,0,19,24,53,28,5,true 20,3,1,3,64,330.88,0.1,10.5,31.51,3474.19,0.11,193.01,2,19,8,0,18,18,43,21,5,true 8,1,1,1,24,103.73,0.2,5,20.75,518.63,0.03,28.81,0,7,7,0,10,10,14,10,1,true 44,8,4,7,159,971.26,0.08,13.06,74.36,12685.78,0.32,704.77,2,31,10,0,20,49,95,64,15,true 91,18,7,16,591,4176.06,0.05,21.41,195.05,89412.4,1.39,4967.36,4,18,4,0,22,112,373,218,35,true 47,3,1,3,256,1563.78,0.04,28,55.85,43785.9,0.52,2432.55,2,13,2,0,23,46,144,112,5,true 24,4,3,3,107,587.63,0.05,19.13,30.72,11241.58,0.2,624.53,1,7,4,0,22,23,67,40,7,true 82,11,3,10,475,3155.83,0.02,44.71,70.59,141084.24,1.05,7838.01,9,59,35,0,32,68,285,190,21,true 10,2,1,1,32,150.41,0.15,6.5,23.14,977.69,0.05,54.32,1,12,4,0,13,13,19,13,3,true 28,6,5,5,104,564.33,0.06,16.09,35.08,9078.38,0.19,504.35,2,7,0,0,20,23,67,37,11,true