1. An Empirical Study of Requirements-to-Code Elaboration Factors
Ali Afzal Malik, Supannika Koolmanojwong, Barry W. Boehm
{alimalik, koolmano,
24th International Forum on COCOMO and Systems/Software Cost Modeling
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2. Outline Introduction Motivation Related Work Empirical Setting Method
Results
Case Study
Conclusions & Future Work
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3. Introduction Requirements elaboration Elaboration factor 9/22/2018
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4. Requirements Elaboration
Requirements Level
Cockburn Metaphor
Objectives
Cloud
Shall Statements
Kite
Use Cases
Sea Level
Use Case Steps
Fish
SLOC
Clam
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5. Elaboration Factor Set of elaboration factors for each project
{EF(1, 2), EF(2, 3), …, EF(n-1, n)}
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6. Motivation Importance of size in cost estimation
Cost estimation models
COCOMO II: SLOC, FP
Problem: Not available during inception
COSYSMO: # of Requirements
Problem: Inconsistency in level of detail
Elaboration factors solve these problems
Enable early cost estimation
Achieve consistency in level of detail
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7. Related Work (Cockburn, 2001) (Antón, 1996)
Hierarchical refinement of use cases
(Antón, 1996)
GBRAM
(Letier and van Lamsweerde, 2002)
Agent-based tactics
(Malik and Boehm, 2008)
Quantitative analysis of requirements elaboration
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8. Empirical Setting 25 e-services projects done at USC Similarities
Real-client
SAIV: 24-weeks
Architected-agile process
Custom-development
Differences
Domains
Types
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9. Method Data collection process Define mapping with Cockburn metaphor
Identify relevant artifacts
Retrieve requirements data from artifacts
Normalize requirements data
Calculate elaboration factors
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10. Step 1: Mapping Cockburn Metaphor Requirements Level Cloud
Capability goals
Kite
Capability requirements
Sea Level
Use cases
Fish
Use case steps
Clam
SLOC
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11. Step 2: Relevant Artifacts
Requirements Level
Artifact
Capability goals
Operational Concept Description
Capability requirements
System and Software Requirements Definition
Use cases
System and Software Architecture Description
Use case steps
SLOC
Source Code
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12. Step 3: Data Retrieval SLOC Rest Code counting tools Inspection
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13. Step 4: Normalization Improves Examples Consistency in level of detail
Accuracy of analysis
Examples
CG: merge low-level goals
CR: split aggregate requirements
SLOC: filter auto-generated code
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14. Elaboration Factor Formula
Step 5: Calculate EFs
Elaboration Stage
Elaboration Factor Formula
Cockburn Metaphor
E-Services Projects
Cloud-to-Kite
Capability Goals-to-Capability Requirements
Kite-to-Sea Level
Capability Requirements-to-Use Cases
Sea Level-to-Fish
Use Cases-to-Use Case Steps
Fish-to-Clam
Use Case Steps-to-SLOC
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15. Results Distribution of EFs EF statistics Utility of EFs 9/22/2018
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16. Distribution of EFs 1 3 2 4
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17. EF Statistics Statistic Elaboration Factors Average 2.46 0.89 7.06
66.91
Median
2.75
6.57
40.72
Standard Deviation
0.94
0.55
2.97
64.46
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18. Utility Early SLOC estimation of similar new projects
Most likely SLOC and 1-sd confidence interval
Example: New e-services project; CG = 5
Most Likely SLOC = 5 * 2.46 * 0.89 * 7.06 * ≈ 5171
Minimum SLOC = 5 * (2.46 – 0.94) * (0.89 – 0.55) * (7.06 – 2.97) * (66.91 – 64.46) ≈ 26
Maximum SLOC = 5 * ( ) * ( ) * ( ) * ( ) ≈ 32, 256
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19. Number of Requirements
Case Study
Large IBM projects
Ball park figures from (Hopkins & Jenkins, 2008)
Requirements Level
Number of Requirements
Business events 50
Use cases
200
Main steps
1, 500
Detailed operations
20, 000
SLOC
1, 500, 000
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20. Case Study cont… Elaboration Stage Elaboration Factor
Cockburn Metaphor
Large IBM Projects
Cloud-to-Kite
Business Events-to-Use Cases
4.00
Kite-to-Sea Level
Use Cases-to-Main Steps
7.50
Sea Level-to-Fish
Main Steps-to-Detailed Operations
13.33
Fish-to-Clam
Detailed Operations-to-SLOC
75.00
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21. Case Study cont…
EFs helpful in early sizing and cost estimation of similar large projects
Example: New project with 60 business events
Rough size = 60 * 4 * 7.5 * * 75 ≈ 1, 800 KSLOC
Caveat: code reuse
Best approach is to obtain confidence intervals for SLOC estimates using EF distributions
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22. Conclusions & Future Work
Entire process of requirements elaboration can be quantified using a set of EFs
Sets of EFs helpful in early sizing and cost estimation of similar projects
Same approach can be used to study elaboration of different types of project
Future work
Framework for predicting variance in EFs
Other mappings
More elaboration data of real projects!
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23. Further Information
Details and a complete list of references available in accompanying paper
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