%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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 . %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @relation desharnais.csv % Domain: software cost estimation % Donor: Martin Shepperd % Corrections by: Dan Port with information from Barbara Kitchenham % % Notes: 4 incomplete projects (projects 38,44,66,75) so often people use the 77 complete cases. % The original source is [1]. % Our paper [2] provides a basic overview of the data set. % References: % [1] J. M. Desharnais, "Analyse statistique de la % productivitie des projets informatique a partie de la % technique des point des fonction," University of Montreal, % Masters Thesis, 1989. % % [2] M. J. Shepperd and C. Schofield, "Estimating software % project effort using analogies," IEEE Transactions on % Software Engineering, vol. 23, pp. 736-743, 1997. % % [3] Dreger, J. Brian. "Function Point Analysis" % Englewood Cliffs, NJ: Prentice Hall, 1989. % % Attributes: % @attribute Project numeric % proj ID @attribute TeamExp numeric % measured in years @attribute ManagerExp numeric % measured in years @attribute YearEnd numeric % Year project ended (catagorical) @attribute Length numeric % Actual project schedule in months (note this is dependent variable like ActualEffort) @attribute Effort numeric % ActualEffort is measured in person-hours @attribute Transactions numeric % Transactions is a count of basic logical transactions in the system (function points) @attribute Entities numeric % Entities is the number of entities in the systems data model (function points) @attribute PointsNonAdjust numeric % This is Transactions + Entities (function points) Note: in the orginal data set this was mislabeled as PointsAjust @attribute Adjustment numeric % Function point complexity adjustment factor (Total Processing Complexity) @attribute PointsAjust numeric % This is the function points adjusted by the Adjustment factor =0.65 + (0.01 * PointsNonAdjust) Note: in the orginal data set this was mislabeled as PointsNonAjust @attribute Language {1,2,3} % programming language (catagorical) % % Note: Many of the above attributes are numerically coded catagorical variables. Values of -1 indicate missing data, % % Sample run (WEKA): % === Run information === % % Scheme: weka.classifiers.functions.LinearRegression -S 0 -R 1.0E-8 % Relation: martin.csv % Instances: 81 % Attributes: 12 % Project % TeamExp % ManagerExp % YearEnd % Length % Effort % Transactions % Entities % PointsNonAdjust % Adjustment % PointsAjust % Langage % % Note from dport: the regression model below is problematic in that it treats catagorical variables as continuous values % and it includes the dependent variable Length as an independent variable which is likely a mistake. % % Test mode: 10-fold cross-validation % % === Classifier model (full training set) === % % Linear Regression Model % % Effort = % -433.25 * TeamExp + % 408.8057 * ManagerExp + % 201.2701 * Length + % 4.2361 * Transactions + % 7.9056 * Entities + % 4.4594 * PointsAdjust + % 92.1389 * Adjustment + % -1777.4579 * Langage + % -278.0786 % % Time taken to build model: 0.02 seconds % % === Cross-validation === % === Summary === % % Correlation coefficient 0.7303 % Mean absolute error 2082.1182 % Root mean squared error 3007.0504 % Relative absolute error 64.9965 % % Root relative squared error 67.3788 % % Total Number of Instances 81 @data 1,1,4,85,12,5152,253,52,305,34,302,1 2,0,0,86,4,5635,197,124,321,33,315,1 3,4,4,85,1,805,40,60,100,18,83,1 4,0,0,86,5,3829,200,119,319,30,303,1 5,0,0,86,4,2149,140,94,234,24,208,1 6,0,0,86,4,2821,97,89,186,38,192,1 7,2,1,85,9,2569,119,42,161,25,145,2 8,1,2,83,13,3913,186,52,238,25,214,1 9,3,1,85,12,7854,172,88,260,30,247,1 10,3,4,83,4,2422,78,38,116,24,103,1 11,4,1,84,21,4067,167,99,266,24,237,1 12,2,1,84,17,9051,146,112,258,40,271,1 13,1,1,84,3,2282,33,72,105,19,88,1 14,3,4,85,8,4172,162,61,223,32,216,1 15,4,4,85,9,4977,223,121,344,28,320,1 16,3,2,85,8,1617,119,48,167,26,152,2 17,4,3,85,8,3192,57,43,100,43,108,1 18,4,4,86,14,3437,68,316,384,20,326,2 19,3,4,87,14,4494,9,386,395,21,340,2 20,4,2,86,5,840,58,34,92,29,86,1 21,4,4,86,12,14973,318,269,587,34,581,2 22,2,4,85,18,5180,88,170,258,34,255,1 23,2,4,86,5,5775,306,132,438,37,447,1 24,4,1,87,20,10577,304,78,382,39,397,1 25,1,4,86,8,3983,89,200,289,33,283,1 26,4,1,85,14,3164,86,230,316,33,310,1 27,2,0,86,6,3542,71,235,306,37,312,1 28,3,1,85,14,4277,148,324,472,39,491,1 29,4,4,85,16,7252,116,170,286,27,263,1 30,4,1,85,14,3948,175,277,452,37,461,1 31,4,3,86,6,3927,79,128,207,27,190,1 32,1,1,86,9,710,145,38,183,27,168,3 33,4,4,87,9,2429,174,78,252,41,267,3 34,1,1,85,5,6405,194,91,285,35,285,1 35,2,2,88,3,651,126,49,175,38,180,3 36,1,3,86,17,9135,317,119,436,34,432,2 37,2,4,87,11,1435,289,88,377,28,351,3 38,-1,-1,87,8,5922,260,144,404,24,360,1 39,1,4,88,4,847,158,59,217,18,180,3 40,3,3,88,16,8050,302,145,447,52,523,2 41,1,1,87,9,4620,451,48,499,28,464,1 42,2,4,87,34,2352,661,132,793,23,698,3 43,1,1,88,10,2174,64,54,118,25,106,1 44,-1,4,86,39,19894,284,230,514,50,591,1 45,2,1,86,18,6699,182,126,308,35,308,1 46,2,3,87,27,14987,173,332,505,19,424,1 47,2,2,88,9,4004,252,7,259,28,241,1 48,4,3,85,11,12824,131,180,311,51,361,1 49,2,3,85,8,2331,106,39,145,6,103,1 50,3,3,85,9,5817,96,108,204,29,192,1 51,2,3,85,7,2989,116,72,188,18,156,1 52,3,3,85,6,3136,86,49,135,32,131,1 53,2,3,85,17,14434,221,121,342,35,342,1 54,1,1,87,12,2583,61,96,157,18,130,1 55,1,3,86,12,3647,132,89,221,5,155,2 56,3,7,86,13,8232,45,387,432,16,350,2 57,1,1,86,12,3276,55,112,167,12,129,2 58,1,4,87,8,2723,124,52,176,14,139,2 59,3,3,87,5,3472,120,126,246,15,197,2 60,1,2,87,6,1575,47,32,79,14,62,2 61,1,1,86,12,2926,126,107,233,23,205,2 62,3,2,86,6,1876,101,45,146,15,117,2 63,1,1,86,5,2520,78,99,177,14,140,1 64,4,7,86,13,1603,69,74,143,14,113,1 65,1,3,86,8,3626,194,97,291,35,291,2 66,2,-1,87,10,6783,224,110,334,28,309,2 67,2,4,87,15,11361,323,184,507,35,507,2 68,1,3,86,6,1267,42,31,73,27,67,2 69,1,2,87,5,2548,74,43,117,25,105,2 70,3,4,87,10,1155,101,57,158,9,117,2 71,0,4,86,6,546,97,42,139,6,99,3 72,2,3,84,13,2275,134,77,211,13,165,2 73,4,5,86,26,9100,482,227,709,26,645,2 74,0,2,84,6,595,213,73,286,6,203,3 75,0,-1,84,22,3941,139,143,282,22,245,2 76,2,3,86,24,13860,473,182,655,40,688,2 77,4,4,85,12,1400,229,169,398,39,414,3 78,4,3,83,12,2800,227,73,300,34,297,1 79,4,4,82,24,9520,395,193,588,40,617,1 80,4,3,86,12,5880,469,176,645,43,697,3 81,4,4,85,36,23940,886,241,1127,34,1116,1