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%%-*- 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) <promise@site.uottawa.ca>
% 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, <zhihaoch@cse.usc.edu>
% 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