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An Empirical Analysis of Software Productivity Over Time

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Talk given at METRICS 2005.

Talk given at METRICS 2005.

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  • 1. Presented at Metrics 2005 - Como, Italy An Empirical Analysis of Software Productivity Over Time Rahul Premraj1 Martin Shepperd2 Barbara Kitchenham3,4 Pekka Forselius5 1 Bournemouth University, UK 2 Brunel University, UK 3 National ICT, Australia 4 Keele University, UK 5 Software Technology Transfer Finland Oy, Finland 11th IEEE Symposium on Software Metrics, 2005 Como, Italy Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 2. Presented at Metrics 2005 - Como, Italy Contents Contents 1 Background to the Data Set 2 Results 1 Scale Economies 2 Productivity Trends 3 Sources of Variance 3 Conclusions Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 3. Presented at Metrics 2005 - Como, Italy Contents Contents 1 Background to the Data Set 2 Results 1 Scale Economies 2 Productivity Trends 3 Sources of Variance 3 Conclusions Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 4. Presented at Metrics 2005 - Como, Italy Contents Contents 1 Background to the Data Set 2 Results 1 Scale Economies 2 Productivity Trends 3 Sources of Variance 3 Conclusions Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 5. Presented at Metrics 2005 - Como, Italy Contents Contents 1 Background to the Data Set 2 Results 1 Scale Economies 2 Productivity Trends 3 Sources of Variance 3 Conclusions Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 6. Presented at Metrics 2005 - Como, Italy Contents Contents 1 Background to the Data Set 2 Results 1 Scale Economies 2 Productivity Trends 3 Sources of Variance 3 Conclusions Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 7. Presented at Metrics 2005 - Como, Italy Contents Contents 1 Background to the Data Set 2 Results 1 Scale Economies 2 Productivity Trends 3 Sources of Variance 3 Conclusions Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 8. Presented at Metrics 2005 - Como, Italy Contents Contents 1 Background to the Data Set 2 Results 1 Scale Economies 2 Productivity Trends 3 Sources of Variance 3 Conclusions Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 9. Presented at Metrics 2005 - Como, Italy The “Finnish Data Set” Also known as the Experience Pro data set. Result of commercial initiatives by Software Technology Transfer Finland (STTF). In total there are 622 projects and 102 features collected including size, effort, factors characterising development environment, target technology, etc. Includes software projects completed in Finland between 1978 and 2003. 93% of the projects are new development projects and the remainder are maintenance projects. Only completed projects submitted. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 10. Presented at Metrics 2005 - Como, Italy The “Finnish Data Set” Also known as the Experience Pro data set. Result of commercial initiatives by Software Technology Transfer Finland (STTF). In total there are 622 projects and 102 features collected including size, effort, factors characterising development environment, target technology, etc. Includes software projects completed in Finland between 1978 and 2003. 93% of the projects are new development projects and the remainder are maintenance projects. Only completed projects submitted. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 11. Presented at Metrics 2005 - Como, Italy The “Finnish Data Set” Also known as the Experience Pro data set. Result of commercial initiatives by Software Technology Transfer Finland (STTF). In total there are 622 projects and 102 features collected including size, effort, factors characterising development environment, target technology, etc. Includes software projects completed in Finland between 1978 and 2003. 93% of the projects are new development projects and the remainder are maintenance projects. Only completed projects submitted. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 12. Presented at Metrics 2005 - Como, Italy The “Finnish Data Set” Also known as the Experience Pro data set. Result of commercial initiatives by Software Technology Transfer Finland (STTF). In total there are 622 projects and 102 features collected including size, effort, factors characterising development environment, target technology, etc. Includes software projects completed in Finland between 1978 and 2003. 93% of the projects are new development projects and the remainder are maintenance projects. Only completed projects submitted. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 13. Presented at Metrics 2005 - Como, Italy The “Finnish Data Set” Also known as the Experience Pro data set. Result of commercial initiatives by Software Technology Transfer Finland (STTF). In total there are 622 projects and 102 features collected including size, effort, factors characterising development environment, target technology, etc. Includes software projects completed in Finland between 1978 and 2003. 93% of the projects are new development projects and the remainder are maintenance projects. Only completed projects submitted. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 14. Presented at Metrics 2005 - Como, Italy The “Finnish Data Set” Also known as the Experience Pro data set. Result of commercial initiatives by Software Technology Transfer Finland (STTF). In total there are 622 projects and 102 features collected including size, effort, factors characterising development environment, target technology, etc. Includes software projects completed in Finland between 1978 and 2003. 93% of the projects are new development projects and the remainder are maintenance projects. Only completed projects submitted. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 15. Presented at Metrics 2005 - Como, Italy The “Finnish Data Set” Also known as the Experience Pro data set. Result of commercial initiatives by Software Technology Transfer Finland (STTF). In total there are 622 projects and 102 features collected including size, effort, factors characterising development environment, target technology, etc. Includes software projects completed in Finland between 1978 and 2003. 93% of the projects are new development projects and the remainder are maintenance projects. Only completed projects submitted. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 16. Project Distribution by Business Sector 70 6% Insurance 12% Banking 60 Public Admin. 37% 8% Other Manufacturing Retail 50 15% Project Count 40 22% 30 20 10 0 0 ’78 ’82 ’83 ’85 ’86 ’87 ’88 ’89 ’90 ’91 ’92 ’93 ’94 ’95 ’96 ’97 ’98 ’99 ’00 ’01 ’02 ’03 Years
  • 17. Presented at Metrics 2005 - Como, Italy Data Editing Of 622 projects, the following were removed: 3 projects that were not completed. 5 projects with non-standard size measurement. Projects with implausible delivery rates (i.e. < 1FP hr −1 (6 projects) and > 30FP hr −1 (6 projects)) Thus, in total 20 projects were removed i.e. 3.2% of the data set. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 18. Presented at Metrics 2005 - Como, Italy Productivity and Economies of Scale (1) Raw Data (2) Natural Log-Scale Data (5) Remove Projects with (3) Build Log-Linear Model Cook’s Distance > 4/n ln( Effort ) = a + b ln( Size) (6) Build Log-Linear Model ln( Effort ) = a + b ln( Size) (4) Re-transform Data into Original Scale (7) Re-transform Data Effort = a ( Size)b into Original Scale Effort = a ( Size)b Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 19. Presented at Metrics 2005 - Como, Italy All Projects Production Function 7 Effort vs. Size - All Projects - 6 Power Model Power Model - Outliers Effort = 7.345 (Size)0.961 Outliers 0.909 < b < 1.014 and 5 Effort (Hours - ∗104) R 2 = 0.683. 4 3 2 - Without 31 Outliers - 1 Effort = 6.13 (Size)0.993 0.94 < b < 1.047 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 20. Presented at Metrics 2005 - Como, Italy All Projects Production Function 7 Effort vs. Size - All Projects - 6 Power Model Power Model - Outliers Effort = 7.345 (Size)0.961 Outliers 0.909 < b < 1.014 and 5 Effort (Hours - ∗104) R 2 = 0.683. 4 3 2 - Without 31 Outliers - 1 Effort = 6.13 (Size)0.993 0.94 < b < 1.047 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 21. Presented at Metrics 2005 - Como, Italy All Projects Production Function 7 Effort vs. Size - All Projects - 6 Power Model Power Model - Outliers Effort = 7.345 (Size)0.961 Outliers 0.909 < b < 1.014 and 5 Effort (Hours - ∗104) R 2 = 0.683. 4 3 2 - Without 31 Outliers - 1 Effort = 6.13 (Size)0.993 0.94 < b < 1.047 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 22. Presented at Metrics 2005 - Como, Italy Past and Present Comparison FinnishMF Finnish602 - MF Start dates 1978-94 1997-2003 No. of companies 26 17 No. of projects 206 401 Project sizes (FPs) 33−3375 27−5060 Productivity (FPhr−1 ) 0.177 0.233 Table: Na¨ Productivity Comparison of 1978-94 and 1997-2003 ıve Why Na¨ ıve? Many differences between both samples of data. Non-constant distribution of projects across business sectors. Maintenance projects were added only 1997 onwards. Projects exhibited a tendency to decrease in size with time. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 23. Presented at Metrics 2005 - Como, Italy Past and Present Comparison FinnishMF Finnish602 - MF Start dates 1978-94 1997-2003 No. of companies 26 17 No. of projects 206 401 Project sizes (FPs) 33−3375 27−5060 Productivity (FPhr−1 ) 0.177 0.233 Table: Na¨ Productivity Comparison of 1978-94 and 1997-2003 ıve Why Na¨ ıve? Many differences between both samples of data. Non-constant distribution of projects across business sectors. Maintenance projects were added only 1997 onwards. Projects exhibited a tendency to decrease in size with time. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 24. Presented at Metrics 2005 - Como, Italy Past and Present Comparison FinnishMF Finnish602 - MF Start dates 1978-94 1997-2003 No. of companies 26 17 No. of projects 206 401 Project sizes (FPs) 33−3375 27−5060 Productivity (FPhr−1 ) 0.177 0.233 Table: Na¨ Productivity Comparison of 1978-94 and 1997-2003 ıve Why Na¨ ıve? Many differences between both samples of data. Non-constant distribution of projects across business sectors. Maintenance projects were added only 1997 onwards. Projects exhibited a tendency to decrease in size with time. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 25. Presented at Metrics 2005 - Como, Italy Past and Present Comparison FinnishMF Finnish602 - MF Start dates 1978-94 1997-2003 No. of companies 26 17 No. of projects 206 401 Project sizes (FPs) 33−3375 27−5060 Productivity (FPhr−1 ) 0.177 0.233 Table: Na¨ Productivity Comparison of 1978-94 and 1997-2003 ıve Why Na¨ ıve? Many differences between both samples of data. Non-constant distribution of projects across business sectors. Maintenance projects were added only 1997 onwards. Projects exhibited a tendency to decrease in size with time. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 26. Presented at Metrics 2005 - Como, Italy Past and Present Comparison FinnishMF Finnish602 - MF Start dates 1978-94 1997-2003 No. of companies 26 17 No. of projects 206 401 Project sizes (FPs) 33−3375 27−5060 Productivity (FPhr−1 ) 0.177 0.233 Table: Na¨ Productivity Comparison of 1978-94 and 1997-2003 ıve Why Na¨ ıve? Many differences between both samples of data. Non-constant distribution of projects across business sectors. Maintenance projects were added only 1997 onwards. Projects exhibited a tendency to decrease in size with time. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 27. Presented at Metrics 2005 - Como, Italy Past and Present Comparison FinnishMF Finnish602 - MF Start dates 1978-94 1997-2003 No. of companies 26 17 No. of projects 206 401 Project sizes (FPs) 33−3375 27−5060 Productivity (FPhr−1 ) 0.177 0.233 Table: Na¨ Productivity Comparison of 1978-94 and 1997-2003 ıve Why Na¨ ıve? Many differences between both samples of data. Non-constant distribution of projects across business sectors. Maintenance projects were added only 1997 onwards. Projects exhibited a tendency to decrease in size with time. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 28. Presented at Metrics 2005 - Como, Italy Productivity Model Regression Model of the form: ln(Effort) = βyr ln(Sizeyr ) + BusSectBool + ProjTypeBool 1 Year*Size Interaction: Each year - 1978, ..., 2003 became the dummy variable and ln(Size) the project size in FP for the project. 2 Boolean dummy variables for business sector. 3 Boolean dummy variables for project type (i.e. New Devp. or Maintenance). Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 29. Presented at Metrics 2005 - Como, Italy Productivity Model Regression Model of the form: ln(Effort) = βyr ln(Sizeyr ) + BusSectBool + ProjTypeBool 1 Year*Size Interaction: Each year - 1978, ..., 2003 became the dummy variable and ln(Size) the project size in FP for the project. 2 Boolean dummy variables for business sector. 3 Boolean dummy variables for project type (i.e. New Devp. or Maintenance). Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 30. Presented at Metrics 2005 - Como, Italy Productivity Model Regression Model of the form: ln(Effort) = βyr ln(Sizeyr ) + BusSectBool + ProjTypeBool 1 Year*Size Interaction: Each year - 1978, ..., 2003 became the dummy variable and ln(Size) the project size in FP for the project. 2 Boolean dummy variables for business sector. 3 Boolean dummy variables for project type (i.e. New Devp. or Maintenance). Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 31. Presented at Metrics 2005 - Como, Italy Productivity Model Regression Model of the form: ln(Effort) = βyr ln(Sizeyr ) + BusSectBool + ProjTypeBool 1 Year*Size Interaction: Each year - 1978, ..., 2003 became the dummy variable and ln(Size) the project size in FP for the project. 2 Boolean dummy variables for business sector. 3 Boolean dummy variables for project type (i.e. New Devp. or Maintenance). Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 32. Temporal Productivity Comparison 1.25 Upper and Lower Confidence Interval Bounds (95%) 1.2 Beta Coefficients 1.12 Beta Coefficients (Lowess Smoother) 1.15 1.054 1.1 1.039 Beta Coefficients 1.007 1.05 0.977 0.975 0.969 1 0.944 0.943 0.943 0.934 0.918 0.915 0.913 0.911 0.909 0.95 0.885 0.885 0.881 0.870 0.866 0.862 0.9 0.85 11 18 22 16 39 38 30 15 16 17 34 69 63 60 49 53 45 1 1 1 1 3 0.8 0 ’78 ’82 ’83 ’85 ’86 ’87 ’88 ’89 ’90 ’91 ’92 ’93 ’94 ’95 ’96 ’97 ’98 ’99 ’00 ’01 ’02 ’03 Years
  • 33. Presented at Metrics 2005 - Como, Italy New Development Project Models ANOVA highlights significant differences between project size and effort of New Development and Maintenance projects. Project Type Dummy Variable βNewDevp = 0.1198 p = 0.235 and −0.091 < βNewDevp < 0.331 +ve value implies more effort for New Development projects than Maintenance (latter being a point of reference and hence, is zero in the dummy variable). Results in line with Kitchenham et al - No significant differences in productivity between New Development and Maintenance projects. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 34. Presented at Metrics 2005 - Como, Italy New Development Project Models ANOVA highlights significant differences between project size and effort of New Development and Maintenance projects. Project Type Dummy Variable βNewDevp = 0.1198 p = 0.235 and −0.091 < βNewDevp < 0.331 +ve value implies more effort for New Development projects than Maintenance (latter being a point of reference and hence, is zero in the dummy variable). Results in line with Kitchenham et al - No significant differences in productivity between New Development and Maintenance projects. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 35. Presented at Metrics 2005 - Como, Italy New Development Project Models ANOVA highlights significant differences between project size and effort of New Development and Maintenance projects. Project Type Dummy Variable βNewDevp = 0.1198 p = 0.235 and −0.091 < βNewDevp < 0.331 +ve value implies more effort for New Development projects than Maintenance (latter being a point of reference and hence, is zero in the dummy variable). Results in line with Kitchenham et al - No significant differences in productivity between New Development and Maintenance projects. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 36. Presented at Metrics 2005 - Como, Italy New Development Project Models 7 Effort vs. Size Power Model 6 Power Model - Outliers Outliers - All Projects - Effort = 6.55 (Size)0.981 5 Effort (Hours - ∗104) 4 3 2 - Without 30 Outliers - Effort = 5.23 (Size)1.021 1 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 37. Presented at Metrics 2005 - Como, Italy New Development Project Models 7 Effort vs. Size Power Model 6 Power Model - Outliers Outliers - All Projects - Effort = 6.55 (Size)0.981 5 Effort (Hours - ∗104) 4 3 2 - Without 30 Outliers - Effort = 5.23 (Size)1.021 1 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 38. Presented at Metrics 2005 - Como, Italy New Development Project Models 7 Effort vs. Size Power Model 6 Power Model - Outliers Outliers - All Projects - Effort = 6.55 (Size)0.981 5 Effort (Hours - ∗104) 4 3 2 - Without 30 Outliers - Effort = 5.23 (Size)1.021 1 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 39. Presented at Metrics 2005 - Como, Italy Maintenance Project Models 7 Effort vs. Size - All Projects - Effort = 20.6 (Size)0.734 Power Model 6 Power Model - Outliers Outliers 0.613 < b < 0.856 5 Effort (Hours - ∗103) 4 3 2 - Without 4 Outliers - Effort = 23.5 (Size)0.718 1 0.615 < b < 0.821 0 0 100 200 300 400 500 600 700 800 900 1000 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 40. Presented at Metrics 2005 - Como, Italy Maintenance Project Models 7 Effort vs. Size - All Projects - Effort = 20.6 (Size)0.734 Power Model 6 Power Model - Outliers Outliers 0.613 < b < 0.856 5 Effort (Hours - ∗103) 4 3 2 - Without 4 Outliers - Effort = 23.5 (Size)0.718 1 0.615 < b < 0.821 0 0 100 200 300 400 500 600 700 800 900 1000 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 41. Presented at Metrics 2005 - Como, Italy Maintenance Project Models 7 Effort vs. Size - All Projects - Effort = 20.6 (Size)0.734 Power Model 6 Power Model - Outliers Outliers 0.613 < b < 0.856 5 Effort (Hours - ∗103) 4 3 2 - Without 4 Outliers - Effort = 23.5 (Size)0.718 1 0.615 < b < 0.821 0 0 100 200 300 400 500 600 700 800 900 1000 Project Size (EP20 Function Points) Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 42. Presented at Metrics 2005 - Como, Italy Sources of Variance Table: ANOVA of Productivity Factors Variable % of variance “explained” Company 26.2 Process model 12.6 Business sector 11.7 Year 8.4 Hardware 5.6 ANOVA performed on Factors against productivity. Variables significant at p = 0.01. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 43. Presented at Metrics 2005 - Como, Italy Productivity across Companies Finnish602 comprises 32 companies. Removing infrequent companies (5 or less projects) reduces variance explained to 21.1%. Results in line with analysis by Maxwell and Forselius. Is Company acting as a proxy for Business Sector? Cross-tabulating both factors shows companies almost exclusively develop projects within a single business sector. Choice of many factors (technical and non-technical) are determined by business sectors e.g. staff skills, process models, security requirements, etc. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 44. Presented at Metrics 2005 - Como, Italy Productivity across Companies Finnish602 comprises 32 companies. Removing infrequent companies (5 or less projects) reduces variance explained to 21.1%. Results in line with analysis by Maxwell and Forselius. Is Company acting as a proxy for Business Sector? Cross-tabulating both factors shows companies almost exclusively develop projects within a single business sector. Choice of many factors (technical and non-technical) are determined by business sectors e.g. staff skills, process models, security requirements, etc. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 45. Presented at Metrics 2005 - Como, Italy Productivity across Companies Finnish602 comprises 32 companies. Removing infrequent companies (5 or less projects) reduces variance explained to 21.1%. Results in line with analysis by Maxwell and Forselius. Is Company acting as a proxy for Business Sector? Cross-tabulating both factors shows companies almost exclusively develop projects within a single business sector. Choice of many factors (technical and non-technical) are determined by business sectors e.g. staff skills, process models, security requirements, etc. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 46. Presented at Metrics 2005 - Como, Italy Productivity across Companies Finnish602 comprises 32 companies. Removing infrequent companies (5 or less projects) reduces variance explained to 21.1%. Results in line with analysis by Maxwell and Forselius. Is Company acting as a proxy for Business Sector? Cross-tabulating both factors shows companies almost exclusively develop projects within a single business sector. Choice of many factors (technical and non-technical) are determined by business sectors e.g. staff skills, process models, security requirements, etc. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 47. Presented at Metrics 2005 - Como, Italy Productivity across Companies Finnish602 comprises 32 companies. Removing infrequent companies (5 or less projects) reduces variance explained to 21.1%. Results in line with analysis by Maxwell and Forselius. Is Company acting as a proxy for Business Sector? Cross-tabulating both factors shows companies almost exclusively develop projects within a single business sector. Choice of many factors (technical and non-technical) are determined by business sectors e.g. staff skills, process models, security requirements, etc. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 48. Presented at Metrics 2005 - Como, Italy Productivity across Companies Finnish602 comprises 32 companies. Removing infrequent companies (5 or less projects) reduces variance explained to 21.1%. Results in line with analysis by Maxwell and Forselius. Is Company acting as a proxy for Business Sector? Cross-tabulating both factors shows companies almost exclusively develop projects within a single business sector. Choice of many factors (technical and non-technical) are determined by business sectors e.g. staff skills, process models, security requirements, etc. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 49. Presented at Metrics 2005 - Como, Italy Productivity across Companies Finnish602 comprises 32 companies. Removing infrequent companies (5 or less projects) reduces variance explained to 21.1%. Results in line with analysis by Maxwell and Forselius. Is Company acting as a proxy for Business Sector? Cross-tabulating both factors shows companies almost exclusively develop projects within a single business sector. Choice of many factors (technical and non-technical) are determined by business sectors e.g. staff skills, process models, security requirements, etc. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 50. Business Sector Productivity Comparison 0.336 Manufacturing 0.337 0.279 Retail 0.253 0.270 Public Admin. 0.232 0.237 Banking 0.116 Pre - 1995 Projects Post - 1996 Projects 0.191 Insurance 0.116 0.240 Other 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
  • 51. Presented at Metrics 2005 - Como, Italy Business Sector Productivity Comparison Regression Model: ln(Effort) = βyr ln(Sizeyr ) + BusSectBool + ProjTypeBool Table: β Coefficients Comparing Business Sector Productivity 0.4 0.2 Sector βBusSect Lower Upper 0 Bound Bound −0.2 Insurance 0.2434 0.0494 0.4374 Banking 0.1980 -0.0085 0.4046 −0.4 Public Admin -0.1766 -0.3934 0.0401 −0.6 Manufacturing -0.5572 -0.7846 -0.3298 −0.8 Insurance Banking Public Admin. Manuf. Retail Retail -0.3986 -0.6665 -0.1306 Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 52. Presented at Metrics 2005 - Como, Italy Conclusions Analysis Overall increase of 33% in productivity. Strongest increase in productivity during 1980s and early 1990s. No evidence of diseconomies of scale, but pronounced evidence of economies of scale for Maintenance projects. Little difference between productivity of New Development and Maintenance projects. Most significant factors - Company, Business Sector, Year and Hardware. Problem of generalisation. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 53. Presented at Metrics 2005 - Como, Italy Conclusions Analysis Overall increase of 33% in productivity. Strongest increase in productivity during 1980s and early 1990s. No evidence of diseconomies of scale, but pronounced evidence of economies of scale for Maintenance projects. Little difference between productivity of New Development and Maintenance projects. Most significant factors - Company, Business Sector, Year and Hardware. Problem of generalisation. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 54. Presented at Metrics 2005 - Como, Italy Conclusions Analysis Overall increase of 33% in productivity. Strongest increase in productivity during 1980s and early 1990s. No evidence of diseconomies of scale, but pronounced evidence of economies of scale for Maintenance projects. Little difference between productivity of New Development and Maintenance projects. Most significant factors - Company, Business Sector, Year and Hardware. Problem of generalisation. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 55. Presented at Metrics 2005 - Como, Italy Conclusions Analysis Overall increase of 33% in productivity. Strongest increase in productivity during 1980s and early 1990s. No evidence of diseconomies of scale, but pronounced evidence of economies of scale for Maintenance projects. Little difference between productivity of New Development and Maintenance projects. Most significant factors - Company, Business Sector, Year and Hardware. Problem of generalisation. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 56. Presented at Metrics 2005 - Como, Italy Conclusions Analysis Overall increase of 33% in productivity. Strongest increase in productivity during 1980s and early 1990s. No evidence of diseconomies of scale, but pronounced evidence of economies of scale for Maintenance projects. Little difference between productivity of New Development and Maintenance projects. Most significant factors - Company, Business Sector, Year and Hardware. Problem of generalisation. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 57. Presented at Metrics 2005 - Como, Italy Conclusions Analysis Overall increase of 33% in productivity. Strongest increase in productivity during 1980s and early 1990s. No evidence of diseconomies of scale, but pronounced evidence of economies of scale for Maintenance projects. Little difference between productivity of New Development and Maintenance projects. Most significant factors - Company, Business Sector, Year and Hardware. Problem of generalisation. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 58. Presented at Metrics 2005 - Como, Italy Conclusions Analysis Overall increase of 33% in productivity. Strongest increase in productivity during 1980s and early 1990s. No evidence of diseconomies of scale, but pronounced evidence of economies of scale for Maintenance projects. Little difference between productivity of New Development and Maintenance projects. Most significant factors - Company, Business Sector, Year and Hardware. Problem of generalisation. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 59. Presented at Metrics 2005 - Como, Italy Conclusions Process Large data sets are hard to analyse and it is easy to misunderstand the data. Encourage contact with the data collecting entity. This is an initial analysis that has scratched the surface of a large data set. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 60. Presented at Metrics 2005 - Como, Italy Conclusions Process Large data sets are hard to analyse and it is easy to misunderstand the data. Encourage contact with the data collecting entity. This is an initial analysis that has scratched the surface of a large data set. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 61. Presented at Metrics 2005 - Como, Italy Conclusions Process Large data sets are hard to analyse and it is easy to misunderstand the data. Encourage contact with the data collecting entity. This is an initial analysis that has scratched the surface of a large data set. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 62. Presented at Metrics 2005 - Como, Italy Conclusions Process Large data sets are hard to analyse and it is easy to misunderstand the data. Encourage contact with the data collecting entity. This is an initial analysis that has scratched the surface of a large data set. Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity
  • 63. Presented at Metrics 2005 - Como, Italy End of Presentation Authors – 1 Rahul Premraj – rpremraj@bmth.ac.uk 2 Martin Shepperd – martin.shepperd@brunel.ac.uk 3 Barbara Kitchenham – barbara.kitchenham@nicta.com.au 4 Pekka Forselius – pekka.forselius@kolumbus.fi Thank you for your attention. Questions please! Premraj, Shepperd, Kitchenham, Forselius Empirical Analysis of Software Productivity