%%-*- 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: cocomonasa/software cost estimation % 2. Sources: % % -- Creators: 60 NASA projects from different centers % for projects from the 1980s and 1990s. Collected by % Jairus Hihn, JPL, NASA, Manager SQIP Measurement & % Benchmarking Element % Phone (818) 354-1248 (Jairus.M.Hihn@jpl.nasa.gov) % % -- Donor: Tim Menzies (tim@barmag.net) % % -- Date: December 2 2004 % 3. Past Usage % 1. "Validation Methods for Calibrating Software Effort % Models", T. Menzies and D. Port and Z. Chen and % J. Hihn and S. Stukes, Proceedings ICSE 2005, % http://menzies.us/pdf/04coconut.pdf % -- Results % -- Given background knowledge on 60 prior projects, % a new cost model can be tuned to local data using % as little as 20 new projects. % -- A very simple calibration method (COCONUT) can % achieve PRED(30)=7% or PRED(20)=50% (after 20 projects). % These are results seen in 30 repeats of an incremental % cross-validation study. % -- Two cost models are compared; one based on just % lines of code and one using over a dozen "effort % multipliers". Just using lines of code loses 10 to 20 % PRED(N) points. % % 3.1 Additional Usage: % 2. "Feature Subset Selection Can Improve Software Cost Estimation Accuracy" % Zhihao Chen, Tim Menzies, Dan Port and Barry Boehm % Proceedings PROMISE Workshop 2005, % http://www.etechstyle.com/chen/papers/05fsscocomo.pdf % P02, P03, P04 are used in this paper. % -- Results % -- To the best of our knowledge, this is the first report % of applying feature subset selection (FSS) % to software effort data. % % -- FSS can dramatically improve cost estimation. % % ---T-tests are applied to the results to demonstrate % that always in our data sets, removing % attributes improves performance without increasing the % variance in model behavior. % % 4. Relevant Information % % The COCOMO software cost model measures effort in calendar months % of 152 hours (and includes development and management hours). % COCOMO assumes that the effort grows more than linearly on % software size; i.e. months=a* KSLOC^b*c. Here, "a" and "b" are % domain-specific parameters; "KSLOC" is estimated directly or % computed from a function point analysis; and "c" is the product % of over a dozen "effort multipliers". I.e. % % months=a*(KSLOC^b)*(EM1* EM2 * EM3 * ...) % % The effort multipliers are as follows: % % increase | acap | analysts capability % these to | pcap | programmers capability % decrease | aexp | application experience % effort | modp | modern programing practices % | tool | use of software tools % | vexp | virtual machine experience % | lexp | language experience % ----------+------+--------------------------- % | sced | schedule constraint % ----------+------+--------------------------- % decrease | stor | main memory constraint % these to | data | data base size % decrease | time | time constraint for cpu % effort | turn | turnaround time % | virt | machine volatility % | cplx | process complexity % | rely | required software reliability % % In COCOMO I, the exponent on KSLOC was a single value ranging from % 1.05 to 1.2. In COCOMO II, the exponent "b" was divided into a % constant, plus the sum of five "scale factors" which modeled % issues such as ``have we built this kind of system before?''. The % COCOMO~II effort multipliers are similar but COCOMO~II dropped one % of the effort multiplier parameters; renamed some others; and % added a few more (for "required level of reuse", "multiple-site % development", and "schedule pressure"). % % The effort multipliers fall into three groups: those that are % positively correlated to more effort; those that are % negatively correlated to more effort; and a third group % containing just schedule information. In COCOMO~I, "sced" has a % U-shaped correlation to effort; i.e. giving programmers either % too much or too little time to develop a system can be % detrimental. % % The numeric values of the effort multipliers are: % % very very extra productivity % low low nominal high high high range % --------------------------------------------------------------------- % acap 1.46 1.19 1.00 0.86 0.71 2.06 % pcap 1.42. 1.17 1.00 0.86 0.70 1.67 % aexp 1.29 1.13 1.00 0.91 0.82 1.57 % modp 1.24. 1.10 1.00 0.91 0.82 1.34 % tool 1.24 1.10 1.00 0.91 0.83 1.49 % vexp 1.21 1.10 1.00 0.90 1.34 % lexp 1.14 1.07 1.00 0.95 1.20 % sced 1.23 1.08 1.00 1.04 1.10 e % stor 1.00 1.06 1.21 1.56 -1.21 % data 0.94 1.00 1.08 1.16 -1.23 % time 1.00 1.11 1.30 1.66 -1.30 % turn 0.87 1.00 1.07 1.15 -1.32 % virt 0.87 1.00 1.15 1.30 -1.49 % rely 0.75 0.88 1.00 1.15 1.40 -1.87 % cplx 0.70 0.85 1.00 1.15 1.30 1.65 -2.36 % % These were learnt by Barry Boehm after a regression analysis of the % projects in the COCOMO I data set. % @Book{boehm81, % Author = "B. Boehm", % Title = "Software Engineering Economics", % Publisher = "Prentice Hall", % Year = 1981} % % The last column of the above table shows max(E)/min(EM) and shows % the overall effect of a single effort multiplier. For example, % increasing "acap" (analyst experience) from very low to very % high will most decrease effort while increasing "rely" % (required reliability) from very low to very high will most % increase effort. % % There is much more to COCOMO that the above description. The % COCOMO~II text is over 500 pages long and offers % all the details needed to implement data capture and analysis of % COCOMO in an industrial context. % @Book{boehm00b, % Author = "Barry Boehm and Ellis Horowitz and Ray Madachy and % Donald Reifer and Bradford K. Clark and Bert Steece % and A. Winsor Brown and Sunita Chulani and Chris Abts", % Title = "Software Cost Estimation with Cocomo II", % Publisher = "Prentice Hall", % Year = 2000, % ibsn = "0130266922"} % % Included in that book is not just an effort model but other % models for schedule, risk, use of COTS, etc. However, most % (?all) of the validation work on COCOMO has focused on the effort % model. % @article{chulani99, % author = "S. Chulani and B. Boehm and B. Steece", % title = "Bayesian Analysis of Empirical Software Engineering % Cost Models", % journal = "IEEE Transaction on Software Engineering", % volume = 25, % number = 4, % month = "July/August", % year = "1999"} % % The value of an effort predictor can be reported many ways % including MMRE and PRED(N).MMRE and PRED are computed from the % relative error, or RE, which is the relative size of the % difference between the actual and estimated value: % % RE.i = (estimate.i - actual.i) / (actual.i) % % Given a data set of of size "D", a "Train"ing set of size % "(X=|Train|) <= D", and a "test" set of size "T=D-|Train|", then % the mean magnitude of the relative error, or MMRE, is the % percentage of the absolute values of the relative errors, % averaged over the "T" items in the "Test" set; i.e. % % MRE.i = abs(RE.i) % MMRE.i = 100/T*( MRE.1 + MRE.2 + ... + MRE.T) % % PRED(N) reports the average percentage of estimates that were % within N% of the actual values: % % count=0 % for(i=1;i<=T;i++) do if (MRE.i <= N/100) then count++ fi done % PRED(N) = 100/T * sum % % For example, e.g. PRED(30)=50% means that half the estimates are % within 30% of the actual. Shepperd and Schofield comment that % "MMRE is fairly conservative with a bias against overestimates % while Pred(25) will identify those prediction systems that are % generally accurate but occasionally wildly inaccurate". % @article{shepperd97, % author="M. Shepperd and C. Schofield", % title="Estimating Software Project Effort Using Analogies", % journal="IEEE Transactions on Software Engineering", % volume=23, % number=12, % month="November", % year=1997, % note="Available from % \url{http://www.utdallas.edu/~rbanker/SE_XII.pdf}"} % % 4.1 Further classification of the projects % % 4.1.1 Classify the projects into different project categories - P02, P03, P04. % (The criteria is unknown and they are disjoint.) % % Category sequence Original sequence_of_NASA % P01 1 NASA 26 % P01 2 NASA 27 % P01 3 NASA 28 % P01 4 NASA 29 % P01 5 NASA 30 % P01 6 NASA 31 % P01 7 NASA 32 % P02 1 NASA 4 % P02 2 NASA 5 % P02 3 NASA 6 % P02 4 NASA 7 % P02 5 NASA 8 % P02 6 NASA 9 % P02 7 NASA 10 % P02 8 NASA 11 % P02 9 NASA 12 % P02 10 NASA 13 % P02 11 NASA 14 % P02 12 NASA 15 % P02 13 NASA 16 % P02 14 NASA 17 % P02 15 NASA 18 % P02 16 NASA 19 % P02 17 NASA 20 % P02 18 NASA 21 % P02 19 NASA 22 % P02 20 NASA 23 % P02 21 NASA 24 % P02 22 NASA 25 % P03 1 NASA 34 % P03 2 NASA 35 % P03 3 NASA 36 % P03 4 NASA 37 % P03 5 NASA 38 % P03 6 NASA 39 % P03 7 NASA 40 % P03 8 NASA 41 % P03 9 NASA 42 % P03 10 NASA 43 % P03 11 NASA 44 % P03 12 NASA 45 % P04 1 NASA 47 % P04 2 NASA 48 % P04 3 NASA 49 % P04 4 NASA 50 % P04 5 NASA 51 % P04 6 NASA 52 % P04 7 NASA 53 % P04 8 NASA 54 % P04 9 NASA 55 % P04 10 NASA 56 % P04 11 NASA 57 % P04 12 NASA 58 % P04 13 NASA 59 % P04 14 NASA 60 % % 4.1.2 Classify the projects into different task categories - T01, T02, T03. % (The criteria is unknown and they are disjoint.) % T01:sequencing T02:avionics T03:missionPlanning % % Category sequence Original sequence_of_NASA % T01 1 NASA 43 % T01 2 NASA 41 % T01 3 NASA 37 % T01 4 NASA 34 % T01 5 NASA 40 % T01 6 NASA 38 % T01 7 NASA 39 % T01 8 NASA 36 % T02 1 NASA 4 % T02 2 NASA 6 % T02 3 NASA 26 % T02 4 NASA 27 % T02 5 NASA 33 % T02 6 NASA 32 % T02 7 NASA 29 % T02 8 NASA 30 % T02 9 NASA 28 % T02 10 NASA 7 % T02 11 NASA 9 % T02 12 NASA 10 % T02 13 NASA 55 % T02 14 NASA 31 % T03 1 NASA 51 % T03 2 NASA 52 % T03 3 NASA 16 % T03 4 NASA 17 % T03 5 NASA 8 % T03 6 NASA 50 % T03 7 NASA 53 % T03 8 NASA 45 % T03 9 NASA 48 % T03 10 NASA 47 % % 4.1.3 Classify the projects into different Centers - C01, C02, C03. % (The criteria is unknown and they are disjoint.) % Category sequence Original sequence_of_NASA % % C01 1 NASA 1 % C01 2 NASA 2 % C01 3 NASA 51 % C01 4 NASA 52 % C01 5 NASA 50 % C01 6 NASA 53 % C01 7 NASA 48 % C01 8 NASA 47 % C01 9 NASA 58 % C01 10 NASA 59 % C01 11 NASA 60 % C01 12 NASA 49 % C01 13 NASA 54 % C02 1 NASA 45 % C02 2 NASA 43 % C02 3 NASA 41 % C02 4 NASA 35 % C02 5 NASA 34 % C02 6 NASA 40 % C02 7 NASA 38 % C02 8 NASA 39 % C02 9 NASA 36 % C02 10 NASA 37 % C02 11 NASA 42 % C02 12 NASA 44 % C03 1 NASA 4 % C03 2 NASA 6 % C03 3 NASA 26 % C03 4 NASA 27 % C03 5 NASA 33 % C03 6 NASA 32 % C03 7 NASA 29 % C03 8 NASA 30 % C03 9 NASA 28 % C03 10 NASA 7 % C03 11 NASA 9 % C03 12 NASA 10 % C03 13 NASA 31 % C03 14 NASA 21 % C03 15 NASA 14 % C03 16 NASA 22 % C03 17 NASA 3 % C03 18 NASA 19 % C03 19 NASA 16 % C03 20 NASA 17 % C03 21 NASA 8 % C03 22 NASA 23 % C03 23 NASA 20 % C03 24 NASA 24 % C03 25 NASA 12 % C03 26 NASA 5 % C03 27 NASA 13 % C03 28 NASA 25 % C03 29 NASA 15 % C03 30 NASA 18 % C03 31 NASA 11 % 5. Number of instances: 60 % 6. Number of attributes: 17 (15 discrete in the range Very_Low to % Extra_High; one lines of code measure, and one goal field % being the actual effort in person months). % 7. Attribute information: @relation cocomonasa.csv @attribute RELY {Nominal,Very_High,High,Low} %1 @attribute DATA {High,Low,Nominal,Very_High} %2 @attribute CPLX {Very_High,High,Nominal,Extra_High,Low} %3 @attribute TIME {Nominal,Very_High,High,Extra_High} %4 @attribute STOR {Nominal,Very_High,High,Extra_High} %5 @attribute VIRT {Low,Nominal,High} %6 @attribute TURN {Nominal,High,Low} %7 @attribute ACAP {High,Very_High,Nominal} %8 @attribute AEXP {Nominal,Very_High,High} %9 @attribute PCAP {Very_High,High,Nominal} %10 @attribute VEXP {Low,Nominal,High} %11 @attribute LEXP {Nominal,High,Very_Low,Low} %12 @attribute MODP {High,Nominal,Very_High,Low} %13 @attribute TOOL {Nominal,High,Very_High,Very_Low,Low} %14 @attribute SCED {Low,Nominal,High} %15 @attribute LOC numeric %16 @attribute ACT_EFFORT numeric %17 % 8. Missing attributes: none % 9: Class distribution: the class value (ACT_EFFORT) is continuous. % After sorting all the instances on ACT_EFFORT, the following % distribution was found: % Instances Range % --------- -------------- % 1..10 8.4 .. 42 % 11..20 48 .. 68 % 21..30 70 .. 117.6 % 31..40 120 .. 300 % 41..50 324 .. 571 % 51..60 750 .. 3240 % Change log: % ----------- % % 2005/04/04 Jelber Sayyad Shirabad (PROMISE Librarian) % 1) Minor editorial changes, as well as moving the information provided by % Zhihao Chen to the new sections 3.1 and 4.1 % % 2005/03/28 Zhihao Chen, CSE, USC, USA, % 1) Fix a mistake in line corresponding to cplx entry in the table of "The numeric values of the effort multipliers" % "cplx 0.70 0.85 1.00 1.15 1.30 1.65 -1.86" should be % "cplx 0.70 0.85 1.00 1.15 1.30 1.65 -2.36" % % 2) Additional information about various classifications of the projects are provided. % % 3) Additional usage information is provided % @data Nominal,High,Very_High,Nominal,Nominal,Low,Nominal,High,Nominal,Very_High,Low,Nominal,High,Nominal,Low,70,278 % instance number: 1 Very_High,High,High,Very_High,Very_High,Nominal,Nominal,Very_High,Very_High,Very_High,Nominal,High,High,High,Low,227,1181 % instance number: 2 Nominal,High,High,Very_High,High,Low,High,High,Nominal,High,Low,High,High,Nominal,Low,177.9,1248 % instance number: 3 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,115.8,480 % instance number: 4 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,29.5,120 % instance number: 5 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,19.7,60 % instance number: 6 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,66.6,300 % instance number: 7 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,5.5,18 % instance number: 8 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,10.4,50 % instance number: 9 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,14,60 % instance number: 10 Nominal,Nominal,High,High,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,16,114 % instance number: 11 High,Nominal,High,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,6.5,42 % instance number: 12 Nominal,Nominal,High,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,13,60 % instance number: 13 Nominal,Nominal,High,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,8,42 % instance number: 14 Nominal,Nominal,High,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,High,Nominal,High,High,High,Nominal,90,450 % instance number: 15 High,Nominal,Nominal,High,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Nominal,Nominal,Nominal,Nominal,15,90 % instance number: 16 High,Nominal,High,Nominal,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Nominal,Nominal,Nominal,Nominal,38,210 % instance number: 17 Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Nominal,Nominal,Nominal,Nominal,10,48 % instance number: 18 Nominal,Very_High,High,Very_High,Very_High,Low,High,Very_High,High,Nominal,Low,High,Very_High,Very_High,Low,161.1,815 % instance number: 19 Nominal,Very_High,High,Very_High,Very_High,Low,High,Very_High,High,Nominal,Low,High,Very_High,Very_High,Low,48.5,239 % instance number: 20 Nominal,Very_High,High,Very_High,Very_High,Low,High,Very_High,High,Nominal,Low,High,Very_High,Very_High,Low,32.6,170 % instance number: 21 Nominal,Very_High,High,Very_High,Very_High,Low,High,Very_High,High,Nominal,Low,High,Very_High,Very_High,Low,12.8,62 % instance number: 22 Nominal,Very_High,High,Very_High,Very_High,Low,High,Very_High,High,Nominal,Low,High,Very_High,Very_High,Low,15.4,70 % instance number: 23 Nominal,Very_High,High,Very_High,Very_High,Low,High,Very_High,High,Nominal,Low,High,Very_High,Very_High,Low,16.3,82 % instance number: 24 Nominal,Very_High,High,Very_High,Very_High,Low,High,Very_High,High,Nominal,Low,High,Very_High,Very_High,Low,35.5,192 % instance number: 25 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,25.9,117.6 % instance number: 26 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,24.6,117.6 % instance number: 27 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,7.7,31.2 % instance number: 28 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,9.7,25.2 % instance number: 29 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,2.2,8.4 % instance number: 30 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,3.5,10.8 % instance number: 31 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,8.2,36 % instance number: 32 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,Nominal,Nominal,High,High,Nominal,Low,66.6,352.8 % instance number: 33 Nominal,Low,High,Nominal,Extra_High,Low,Low,High,Very_High,Very_High,Nominal,High,Nominal,Nominal,Nominal,150,324 % instance number: 34 Nominal,Low,High,Nominal,Nominal,Low,Low,High,Nominal,Nominal,Nominal,Very_Low,Nominal,Nominal,Nominal,100,360 % instance number: 35 Nominal,Low,High,Nominal,Nominal,High,Low,High,High,High,Low,Very_Low,Nominal,Nominal,Nominal,100,215 % instance number: 36 Nominal,Low,High,Nominal,Nominal,Low,Low,High,Very_High,Very_High,Nominal,High,Nominal,Nominal,Nominal,100,360 % instance number: 37 Nominal,Low,High,Nominal,Nominal,Low,Low,High,Very_High,High,Nominal,High,Nominal,Nominal,Nominal,15,48 % instance number: 38 Nominal,Low,High,Nominal,Extra_High,Low,Low,High,High,Nominal,Nominal,High,Nominal,Nominal,Nominal,32.5,60 % instance number: 39 Nominal,Low,High,Nominal,Nominal,Low,Low,High,High,High,Nominal,High,Nominal,Nominal,Nominal,31.5,60 % instance number: 40 Nominal,Low,High,Nominal,Nominal,Low,Low,High,Very_High,High,Nominal,High,Nominal,Nominal,Nominal,6,24 % instance number: 41 Nominal,Low,High,Nominal,Nominal,Low,Low,High,Very_High,Nominal,Nominal,Low,Nominal,Nominal,Nominal,11.3,36 % instance number: 42 Nominal,Low,High,Nominal,Nominal,Low,Low,High,Very_High,Very_High,Nominal,High,Nominal,Nominal,Nominal,20,72 % instance number: 43 Nominal,Low,High,Nominal,Nominal,Low,Low,High,Very_High,High,Nominal,High,Nominal,Nominal,Nominal,20,48 % instance number: 44 High,Low,High,Extra_High,Extra_High,Low,High,High,High,High,Nominal,High,High,High,Nominal,7.5,72 % instance number: 45 High,Low,High,Nominal,Nominal,Low,Low,Nominal,Nominal,High,Nominal,Nominal,High,Very_Low,Nominal,302,2400 % instance number: 46 High,Nominal,High,High,High,Low,High,Nominal,High,Nominal,Nominal,Nominal,Low,Very_High,Nominal,370,3240 % instance number: 47 High,Nominal,High,High,High,Low,High,Nominal,High,Nominal,Nominal,Nominal,Low,Very_High,Nominal,219,2120 % instance number: 48 High,Nominal,High,High,High,Low,High,Nominal,High,Nominal,Nominal,Nominal,Low,Very_High,Nominal,50,370 % instance number: 49 High,Nominal,Very_High,High,High,Low,High,High,Nominal,Nominal,High,High,Low,Very_High,High,101,750 % instance number: 50 Nominal,Nominal,Nominal,Nominal,Nominal,Low,Nominal,High,Very_High,Very_High,Low,High,High,Nominal,Nominal,190,420 % instance number: 51 Nominal,Nominal,High,Nominal,High,Nominal,Nominal,High,High,Nominal,Nominal,High,High,Nominal,High,47.5,252 % instance number: 52 Very_High,Nominal,Extra_High,High,High,Low,Low,Nominal,High,Nominal,Nominal,Nominal,Low,High,Nominal,21,107 % instance number: 53 Low,Nominal,Nominal,Nominal,Nominal,Low,Low,High,High,Very_High,Nominal,High,Low,Low,High,423,2300 % instance number: 54 High,High,Nominal,Nominal,Nominal,Low,Low,Nominal,High,High,Nominal,High,Nominal,Nominal,Nominal,79,400 % instance number: 55 High,High,Low,Nominal,Nominal,Nominal,High,High,High,Nominal,Nominal,Nominal,High,Nominal,Nominal,284.7,973 % instance number: 56 Nominal,High,Low,Nominal,Nominal,High,Nominal,High,High,Nominal,Nominal,Nominal,High,High,Nominal,282.1,1368 % instance number: 57 Nominal,High,High,Very_High,Nominal,Nominal,High,High,High,High,Nominal,High,Low,Low,High,78,571.4 % instance number: 58 Nominal,High,High,Very_High,Nominal,Nominal,High,High,High,High,Nominal,High,Low,Low,High,11.4,98.8 % instance number: 59 Nominal,High,High,Very_High,Nominal,Nominal,High,High,High,High,Nominal,High,Low,Low,High,19.3,155 % instance number: 60