Data
nasa_numeric

nasa_numeric

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
0 likes downloaded by 2 people , 3 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Date unknown Please cite: %-*- 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: COCOMO NASA 2 / Software cost estimation 2. Sources: -- 93 NASA projects from different centers for projects from the following years: n year --- ---- 1 1971 1 1974 2 1975 2 1976 10 1977 4 1978 19 1979 11 1980 13 1982 7 1983 7 1984 6 1985 8 1986 2 1987 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@menzies.us) -- Date: Feb 8 2006 3. Past Usage None with this specific data set. But for older work on similar data, see: 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}"} 5. Number of instances: 93 6. Number of attributes: 24 - 15 standard COCOMO-I discrete attributes in the range Very_Low to Extra_High - 7 others describing the project; - one lines of code measure, - one goal field being the actual effort in person months. 7. Attribute information: Unique id project name cagetory of application flight or ground system? which nasa center? year of development development mode cocomo attributes: described above in section 4 equivalent physical 1000 lines of source code development effort in months (one month =152 hours and includes development and management hours) Section 8. Missing attributes: none Section 9: Distribution of class values # development months == ================== 46 0 - 499 28 500 - 999 7 1000 - 1499 3 1500 - 1999 3 2000 - 2499 3 2500 - 2999 0 3000 - 3999 1 4000 - 4499 1 4500 - 4999 0 5000 - 7999 1 8000

24 features

act_effort (target)numeric74 unique values
0 missing
recordnumbernumeric93 unique values
0 missing
projectnamenominal8 unique values
0 missing
cat2nominal14 unique values
0 missing
forgnominal2 unique values
0 missing
centernominal5 unique values
0 missing
yearnumeric14 unique values
0 missing
modenominal3 unique values
0 missing
relynominal4 unique values
0 missing
datanominal4 unique values
0 missing
cplxnominal5 unique values
0 missing
timenominal4 unique values
0 missing
stornominal4 unique values
0 missing
virtnominal3 unique values
0 missing
turnnominal4 unique values
0 missing
acapnominal3 unique values
0 missing
aexpnominal4 unique values
0 missing
pcapnominal3 unique values
0 missing
vexpnominal4 unique values
0 missing
lexpnominal4 unique values
0 missing
modpnominal5 unique values
0 missing
toolnominal5 unique values
0 missing
scednominal3 unique values
0 missing
equivphysklocnumeric79 unique values
0 missing

108 properties

93
Number of instances (rows) of the dataset.
24
Number of attributes (columns) of the dataset.
-1
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
4
Number of numeric attributes.
20
Number of nominal attributes.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.26
Maximum skewness among attributes of the numeric type.
0.01
Percentage of instances belonging to the least frequent class.
0
Percentage of missing values.
22.46
Third quartile of kurtosis among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1135.93
DataQuality extracted from Fantail Library
-1
Number of instances belonging to the least frequent class.
16.67
Percentage of numeric attributes.
1641.72
Third quartile of means among attributes of the numeric type.
-1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-1
Average entropy of the attributes.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
83.33
Percentage of nominal attributes.
-1
Third quartile of mutual information between the nominal attributes and the target attribute.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
10.55
Mean kurtosis among attributes of the numeric type.
-1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1
First quartile of entropy among attributes.
4.12
DataQuality extracted from Fantail Library
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
686.75
Mean of means among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.88
First quartile of kurtosis among attributes of the numeric type.
885.35
DataQuality extracted from Fantail Library
-1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1
Average mutual information between the nominal attributes and the target attribute.
-1
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
59.32
First quartile of means among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.55
Average number of distinct values among the attributes of the nominal type.
1
Number of binary attributes.
-1
First quartile of mutual information between the nominal attributes and the target attribute.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
2.54
Standard deviation of the number of distinct values among attributes of the nominal type.
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Mean skewness among attributes of the numeric type.
-0.04
First quartile of skewness among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
325.27
Mean standard deviation of attributes of the numeric type.
9.56
DataQuality extracted from Fantail Library
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
-1
Percentage of instances belonging to the most frequent class.
-1
Minimal entropy among attributes.
-1
Second quartile (Median) of entropy among attributes.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
Entropy of the target attribute values.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
-1
Number of instances belonging to the most frequent class.
-1.04
Minimum kurtosis among attributes of the numeric type.
10.07
Second quartile (Median) of kurtosis among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
-1
Maximum entropy among attributes.
47.75
Minimum of means among attributes of the numeric type.
359.22
Second quartile (Median) of means among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
23.1
Maximum kurtosis among attributes of the numeric type.
-1
Minimal mutual information between the nominal attributes and the target attribute.
-1
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
-1
The predictive accuracy obtained by always predicting the majority class.
1980.83
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
1.93
Second quartile (Median) of skewness among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.26
Number of attributes divided by the number of instances.
-1
Maximum mutual information between the nominal attributes and the target attribute.
-0.1
Minimum skewness among attributes of the numeric type.
4.17
Percentage of binary attributes.
80.91
DataQuality extracted from Fantail Library
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
-1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
14
The maximum number of distinct values among attributes of the nominal type.
3.34
DataQuality extracted from Fantail Library
0
Percentage of instances having missing values.
-1
Third quartile of entropy among attributes.
0.13
Average class difference between consecutive instances.
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

3 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: act_effort
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: act_effort
0 runs - estimation_procedure: 50 times Clustering
Define a new task

Discussions

Loading discussions...