Software development effort estimation using machine learning techniques

albrecht.arff

@@ -0,0 +1,74 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% 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.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+% To read/write comments on this data, see 
+% http://code.google.com/p/promisedata/wiki/Albrecht .
+
+% 1. Title/Topic: ALBRECHT
+%
+%
+% 2. Sources:
+%		-- Creators: Dr. Albrecht A.J., Dr. Gaffney J. 
+%		-- Reference: A. J. Albrecht, J. Gaffney, 1983 Software function, 
+%			source lines of code, and development effort prediction. 
+%			IEEE Transactions on Software Engineering, 9, 639¨C648.
+%		-- Donor: Yanfu Li ([email protected])
+%		-- Date:  October 14, 2009
+
+% 3. Past usage:
+%
+%		[1] A. J. Albrecht, J. Gaffney, 1983 Software function, 
+%		source lines of code, and development effort prediction. 
+%		IEEE Transactions on Software Engineering, 9, 639¨C648.
+%
+%		[2] M. Shepperd, C. Schofield. 1997 Estimating software 
+%		project effort using analogies. 
+%		IEEE Transactions on Software Engineering, 23, 736-743.
+%
+%		[3] Y.F. Li, M. Xie. T.N. Goh. 2009. A Study of Project Selection 
+%		and Feature Weighting for Analogy Based Software Cost Estimation. 
+%		Journal of Systems and Software. 82(2), 241-252.
+%
+%		[4] Y.F. Li, M. Xie, T.N. Goh. 2009. A Study on the Non-linear 
+%		Adjustment for Analogy Based Software Cost Estimation. 
+%		Empirical Software Engineering. 14(6), 603-643.
+
+@relation albrecht
+
+@attribute Input numeric
+@attribute Output numeric
+@attribute Inquiry numeric
+@attribute File numeric
+@attribute FPAdj numeric
+@attribute RawFPcounts numeric
+@attribute AdjFP numeric
+@attribute Effort numeric
+
+@data
+25,150,75,60,1,1750,1750,102.4
+193,98,70,36,1,1902,1902,105.2
+70,27,0,12,0.8,535,428,11.1
+40,60,20,12,1.15,660,759,21.1
+10,69,1,9,0.9,478.89,431,28.8
+13,19,0,23,0.75,377.33,283,10
+34,14,0,5,0.8,256.25,205,8
+17,17,15,5,1.1,262.73,289,4.9
+45,64,14,16,0.95,715.79,680,12.9
+40,60,20,15,1.15,690.43,794,19
+41,27,29,5,1.1,465.45,512,10.8
+33,17,8,5,0.75,298.67,224,2.9
+28,41,16,11,0.85,490.59,417,7.5
+43,40,20,35,0.85,802.35,682,12
+7,12,13,8,0.95,220,209,4.1
+28,38,24,9,1.05,487.62,512,15.8
+42,57,12,5,1.1,550.91,606,18.3
+27,20,24,6,1.1,363.64,400,8.9
+48,66,13,50,1.15,1073.91,1235,38.1
+69,112,21,39,1.2,1310,1572,61.2
+25,28,4,22,1.05,476.19,500,3.6
+61,68,0,11,1,694,694,11.8
+15,15,6,3,1.05,189.52,199,0.5
+12,15,0,15,0.95,273.68,260,6.1

cocomo811.arff

@@ -0,0 +1,292 @@
+%%-*- 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: cocomo81/software cost estimation
+
+@relation  cocomo81
+
+% 2. Sources:
+%       -- Boehm's 1981 text, transcribed by Srinivasan and Fisher
+%              @Book{boehm81,
+%                Author =	 "B. Boehm",
+%                Title =	 "Software Engineering Economics",
+%                Publisher =	 "Prentice Hall",
+%                Year =	 1981}
+%
+%           then converted by Tim Menzies from
+%           http://www.vuse.vanderbilt.edu/~dfisher/tech-reports/raw-TSE-95
+%           to arff fort.
+%
+%        -- Donor: Tim Menzies [email protected]
+%
+%     -- Date: December 2 2004
+
+% 3. Past Usage
+%     1. K. Srinivasan and D. Fisher, Machine Learning Approaches 
+%        to Estimating Software Development Effort, IEEE Trans. Soft. Eng.,
+%        Feb 1995, pages 126--137
+
+%  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 standard 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
+%    cplx   	0.70 	0.85 	1.00 	1.15 	1.30 	1.65	-1.86
+%    rely   	0.75	 0.88	 1.00 	 1.15 	 1.40		-1.87
+%   
+%    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 * count
+%
+%     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: 63
+
+%  6. Number of attributes: 17 (all numeric: 15 for the effort multipliers,
+%       one for LOC and one for actual development effort.
+
+%  7. Attribute information:
+
+@attribute rely numeric
+@attribute data numeric
+@attribute cplx numeric
+@attribute time numeric
+@attribute stor numeric
+@attribute virt numeric
+@attribute turn numeric
+@attribute acap numeric
+@attribute aexp numeric
+@attribute pcap numeric
+@attribute vexp numeric
+@attribute lexp numeric
+@attribute modp numeric
+@attribute tool numeric
+@attribute sced numeric
+@attribute loc numeric
+@attribute actual numeric
+
+% 8. Missing attributes: none
+
+% 9: Class distribution: the class value (actual) is continuous.
+%    After sorting all the instances on actual, the following
+%    distribution was found:
+%    Instances     Range
+%    ---------  ----------------
+%      1 .. 10	  5.9 ..    15
+%     11 .. 20	  18  ..    47
+%     21 .. 30	  50  ..    87
+%     31 .. 40	  88  ..   218
+%     41 .. 50	 230  ..   539
+%     51 .. 60 	 605  ..  2455
+%     61 .. 63	6400  .. 11400
+
+@data
+
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