Metrics for Effort/Cost Estimation of Mobile apps development
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This document discusses metrics for estimating the effort and cost of developing mobile apps. It proposes defining a set of early metrics that can be extracted before full development begins. These early metrics are mapped to guidelines for estimating Cosmic Function Points (CFP). An empirical study evaluates the accuracy of CFP estimations based on the early metrics by comparing them to actual CFP values for 13 mobile apps. The results show the early estimations were reasonably close to the actual values, with mean magnitude relative error of 0.2 and prediction of actual values within 25% for 61% of apps. Future work involves additional validation with companies and gathering more project data.
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Metrics for Effort/Cost Estimation of Mobile apps development
- 1. Università degli studi di Salerno Dipartimento di Scienze Aziendali, Management & Innovation System Corso di Laurea Magistrale inTecnologie Informatiche e Management Metrics for Effort/Cost Estimation of Mobile apps development ANNO ACCADEMICO 2015-2016 Relatore: Prof. ssa Filomena Ferrucci Dott. Pasquale Salza Candidata: Catolino Gemma Matricola 0222500095 Tesi di laurea magistrale in Ingegneria del Software: Metriche, Qualità eValutazione Sperimentale
- 2. The Effort and Cost Estimation
- 3. Not Only for traditional Software
- 4. always hard to estimate in advance
- 7. When more data become available…
- 8. more accurate estimations can be achieved!
- 9. Non-Model-Based Human experts MAN / HOURS
- 10. Model-Based M M M M MAN / HOURS
- 11. the SizeFactor
- 14. F P A Function Point Analysis (functional) transactions and (logical) data the SizeFactor
- 15. c f p Cosmic Function Point movements from/to persistent storage and users the SizeFactor
- 16. D’avanzo et al. approach van Heeringen & van Gorp approach Sellami et al. Set of guidelines for an approximate and quick sizing of mobile apps IFPUG Guidelines
- 17. D’avanzo approach van Heeringen & van Gorp approach Cozzolino et al. approach newset of guidelines
- 18. Cozzolino et al. approach View/Show Data Create/Set/Delete Data Invoking service newset of guidelines
- 19. Cozzolino et al. approach 3 CFP 3 CFP 2 CFP newset of guidelines
- 20. LIMITations
- 21. The software life cycle is already started!
- 24. Defining a set of metrics for mobile early effort estimation
- 25. Defining a set of metrics for mobile early effort estimation Investigating how the early size measure can be mapped into Cozzolino et al. guidelines
- 26. Defining a set of metrics for mobile early effort estimation Investigating if the mapping is useful for estimating CFP Investigating how the early size measure can be mapped into Cozzolino et al. guidelines
- 27. Defining a set of metrics for mobile early effort estimation
- 28. Emilia Mendes Emilia Mendes, Nile Mosley, and Steve Counsell. Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003.
- 29. Analysis of quote form Emilia Mendes, Nile Mosley, and Steve Counsell. Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003.
- 30. Emilia Mendes, Nile Mosley, and Steve Counsell. Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003. 377manually validated links Analysis of quote form
- 31. Analysis of quote form Emilia Mendes, Nile Mosley, and Steve Counsell. Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003. Extraction of initial set of metrics
- 39. Emilia Mendes, Nile Mosley, and Steve Counsell. Investigating early web size measures for web cost estimation. In Proceedings of EASE’2003 Conference,Keele, pages 1–22, 2003. Extraction of initial set of metrics Validation of initial set of metrics Analysis of quote form
- 40. Validation of initial set of metrics 4 2 DEVELOPERS PROJECT MANAGERS
- 41. Validation of initial set of metrics TWO SURVEYS
- 42. Validation of initial set of metrics TWO SURVEYS
- 43. Validation of initial set of metrics TWO SURVEY YY
- 44. Validation of initial set of metrics 48METRICS
- 45. Validation of initial set of metrics 36CONFIRMED
- 46. Validation of initial set of metrics 12DELETED
- 47. Validation of initial set of metrics 12DELETED Project start date App purchasing Type of business owns the app idea Complex back-end
- 48. Validation of initial set of metrics 5ADDED
- 49. Validation of initial set of metrics 5ADDED Support Security Backward compatibility User target
- 50. Features Generalities Projects Design Platfom Accounting User features Social Aspect Remote Connection eCommerce Date & Location Monitoring Additional Functionality Renovation of Categories
- 51. DesignProjects Platfom Features Date & Location Monitoring Additional Functionality Renovation of Categories Accounting User features Social Aspect Generalities Remote Connection eCommerceGoogle Module
- 54. SIZE
- 55. Investigating how the early size measure can be mapped into Cozzolino et al. guidelines
- 56. Requirements Early phase of developments Requirement Elicitation/ Analysis
- 57. SOFTWARE SIZE Early phase of developments Requirement Elicitation/ Analysis
- 58. Early phase of developments Requirement Elicitation/ Analysis cosmic
- 59. View/Show Data Exchange Data via a network Invoking service Create/Set/Delete Data Guidelines Cozzolino et al. Early MetricsSocial sharing Search Messaging Ad hoc authentication Analytics
- 60. Exchange Data via a network Early Metrics Ad hoc authentication Guidelines Cozzolino et al.
- 61. Exchange Data via a network Lines guide Cozzolino et al.Early Metrics Ad hoc authentication MIN MAX 10 CFP5 CFP Login + RegisterLogin Ad hoc authentication
- 62. Exchange Data via a network Lines guide Cozzolino et al.Early Metrics Ad hoc authentication MINMAX 10 CFP 5 CFP Login + Register Login 41 METRICS
- 63. Exchange Data via a network Lines guide Cozzolino et al.Early Metrics Ad hoc authentication MINMAX 10 CFP 5 CFP Login + Register Login 26 METRICS MIN MAXOPERATIONS
- 64. Empirical study
- 65. Evaluate the accuracy of the estimations in terms of COSMIC Function Points of the early metrics
- 66. RQ:To what extent the CFPs extractable using the early metrics are close to the actual CFPs of a Mobile app? Evaluate the accuracy of the estimations in terms of COSMIC Function Points of the early metrics
- 67. Evaluate the accuracy of the estimations in terms of COSMIC Function Points of the early metrics 13MOBILE APPLICATIONS
- 71. Results
- 72. Application Early CFP_min Early CFP_max Early CFP_avg Oracle Wikipedia 37 47 42 46 Munch 41 51 46 42 Loopboard 16 21 18,5 14 Man man 34 44 38,5 38 Easy Sound Recorder 20 25 22,5 18 K-9 Mail 38 53 45,5 32 Transportr 47 67 57 38 Hashr 23 23 23 19 arXiv Mobile 37 42 39,5 39 NPR News 37 42 39,5 38 Loop Habit Tracker 26 31 28,5 28 Radio Droid 33 38 35,5 50 RoomMates Expense 26 31 28,5 44
- 73. Application Early CFP_min Early CFP_max Early CFP_avg Oracle Wikipedia 37 47 42 46 Munch 41 51 46 42 Loopboard 16 21 18,5 14 Man man 34 44 38,5 38 Easy Sound Recorder 20 25 22,5 18 K-9 Mail 38 53 45,5 32 Transportr 47 67 57 38 Hashr 23 23 23 19 arXiv Mobile 37 42 39,5 39 NPR News 37 42 39,5 38 Loop Habit Tracker 26 31 28,5 28 Radio Droid 33 38 35,5 50 RoomMates Expense 26 31 28,5 44
- 74. Application Early CFP_min MRE_min PRED(25) Wikipedia 37 0,19 1 Munch 41 0,02 1 Loopboard 16 0,14 1 Man man 34 0,01 1 Easy Sound Recorder 20 0,11 1 K-9 Mail 38 0,19 1 Transportr 47 0,24 1 Hashr 23 0,21 1 arXiv Mobile 37 0,05 1 NPR News 37 0,03 1 Loop Habit Tracker 26 0,07 1 Radio Droid 33 0,34 0 RoomMates Expense 26 0,41 0 MIN
- 76. Application Early max MRE_max PRED(25) Wikipedia 47 0,02 1 Munch 51 0,21 1 Loopboard 21 0,5 0 Man man 44 0,16 1 Easy Sound Recorder 25 0,39 0 K-9 Mail 53 0,66 0 Transportr 67 0,76 0 Hashr 23 0,21 1 arXiv Mobile 42 0,08 1 NPR News 42 0,1 1 Loop Habit Tracker 31 0,11 1 Radio Droid 38 0,24 1 RoomMates Expense 31 0,29 1 MAX
- 77. MIN MMRE 0,29 0 MDMRE 0,21 PRED(25) 61%
- 78. Application Early avg MRE_avg PRED(25) Wikipedia 42 0,09 1 Munch 46 0,09 1 Loopboard 18,5 0,32 0 Man man 38,5 0,01 1 Easy Sound Recorder 22,5 0,25 1 K-9 Mail 45,5 0,42 0 Transportr 57 0,5 0 Hashr 23 0,21 1 arXiv Mobile 39,5 0,01 1 NPR News 39,5 0,04 1 Loop Habit Tracker 28,5 0,02 1 Radio Droid 35,5 0,29 0 RoomMates Expense 28,5 0,35 0 AVG
- 79. MMRE 0.2 MDMRE 0.21 PRED(25) 61% AVG
- 80. Application Early CFP_min Early CFP_max Early CFP_avg Oracle Wikipedia 37 47 42 46 Munch 41 51 46 42 Loopboard 16 21 18,5 14 Man man 34 44 38,5 38 Easy Sound Recorder 20 25 22,5 18 K-9 Mail 38 53 45,5 32 Transportr 47 67 57 38 Hashr 23 23 23 19 arXiv Mobile 37 42 39,5 39 NPR News 37 42 39,5 38 Loop Habit Tracker 26 31 28,5 28 Radio Droid 33 38 35,5 50 RoomMates Expense 26 31 28,5 44
- 81. Application Early CFP_min Early CFP_max Early CFP_avg Oracle Wikipedia 37 47 42 46 Munch 41 51 46 42 Loopboard 16 21 18,5 14 Man man 34 44 38,5 38 Easy Sound Recorder 20 25 22,5 18 K-9 Mail 38 53 45,5 32 Transportr 47 67 57 38 Hashr 23 23 23 19 arXiv Mobile 37 42 39,5 39 NPR News 37 42 39,5 38 Loop Habit Tracker 26 31 28,5 28 Radio Droid 33 38 35,5 50 RoomMates Expense 26 31 28,5 44
- 82. RQ:To what extent the CFPs extractable using the early metrics are close to the actual CFPs of a Mobile app? The estimations provided by our metrics resulted quite close to the actual values
- 84. EARLY METRIC Additional validation with companies Gather data FUTURE WORK
- 86. Summary
- 87. Summary
- 88. Summary
- 89. Summary
- 90. Summary
- 91. Summary
- 92. Thank you!