1
Hany H. Ammar
LANE Department of Computer Science and Electrical EngineeringWest Virginia University, Morgantown, West Virginia, USA, and
Faculty of Computers and Information, Cairo University, Cairo, Egypt
Introduction to Risk Management And Software Architecture Risk Assessment
الرحيم الرحمن الله بسمالله رسول على والسالم والصالة ، لله الحمد
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OUTLINE
• Risk Management• Software Architecture Risk Assessment
– Maintainability-based risk • Conclusions• Next Steps
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Risk Management
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For NASA Programs
• RISK MANAGEMENT: An organized, systematic decision-making process that efficiently identifies risks, assesses or analyzes risks, and effectively reduces or eliminates risks to achieving program goals.
• RISK: A Program “Risk” is any circumstance or situation that poses a threat to: crew or vehicle safety, Program controlled cost; Program controlled schedule; or major mission objectives, and for which an acceptable resolution is deemed unlikely without a focused management effort
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Risk Management Cycle
Identify: Identify that a risk exits and give it a meaningful name.
Analyze: Determine the severity of the risk according to the risk matrix. If the risk is negligible (low to medium severity, low likelihood of occurrence), stop here. However, if the risk could cause damage to the system or the system's users, continue.
Plan: Decide how to combat the risk based on the risk's severity and likelihood of occurrence.
Mitigate: Follow the plan formulated in the previous phase as closely as possible to combat the risk. If this approach does not work, return to the previous phase and make a new plan. If the plan does work, continue analyzing the risk to determine whether it has been reduced to an acceptable severity level.
Track: Once the risk has been mitigated to an acceptable severity level, the risk should be tracked to ensure the continued control of the risk. If at any time the risk seems to resurface, the risk management cycle should begin again, starting with the analysis phase.
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Risk Definition
• According to NASA Software Safety Technical Standard, risk is defined as: “exposure to the chance of injury or loss. It is a function of the possible frequency of occurrence of the undesired event, of the potential severity of resulting consequences, and of the uncertainties associated with the frequency and severity”.
• For software intensive systems, a risk is a combination of a likelihood of occurrence of an abnormal event or failure and the potential consequences or severity of that event or failure to a system's operators, users, or environment
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Risk Matrix
SeverityLikelihood of Occurrence
Probable Occasional Remote Improbable
Catastrophic High HighHigh-Medium
Medium
Critical HighHigh-Medium
MediumMedium-
Low
MarginalHigh-Mediu
mMedium
Medium-Low
Low
Negligible MediumMedium-
LowLow Low
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NASA IV&V Facility
NPD 2820.1C for Software IV&V Policy states:
"Task the IV&V Facility in Fairmont, West Virginia to manage the performance of all IV&V for software identified per the established
criteria, and for any other safety critical software (as defined in NASA-STD-8719.13)"
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IV&V Function
• Software Independent Verification & Validation (IV&V) is a systems engineering process employing rigorous methodologies for evaluating the correctness and quality of the software product throughout the software life cycle.
• Software IV&V is adapted to the characteristics of the project. Different projects require different level of IV&V
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IV&V Lifecycle Activities
• Life-cycle IV&V is designed to mesh with the Project schedule and provide timely inputs to mitigate risk
• Dialog between the IV&V Facility and the Project must begin before SRR
• For most Projects, IV&V ends (and the Final Report is delivered) on or about MRR. Some Projects have extended S/W development post-launch or major upgrades/maintenance (e.g. Shuttle, MER)
System Requirements Review
Preliminary DesignReview
CriticalDesignReview
System Test
S/W FQT
Initial IVVPSigned
Mission Readiness Review
Concept Phase
2.0
Requirements Phase
3.0
Design Phase
4.0
Implementation Phase
5.0
Test Phase
6.0
Operations &Maintenance Phase
7.0
Baseline IVVPSigned
- IV&V provides support and reports for Project milestones - Technical Analysis Reports document major phases- IVVP is updated to match changes in Project
IV&VProvidesCoFR
IV&V Final Report
IV&V Phase Independent Support1.0
SystemRetirement
Launch
Note: numbers correspond to IV&V WBS
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Software Project Resolution
1. Successful Projects– Completed and operational, and:
• On Schedule• On Cost• With all originally specified features and functions
2. Challenged Projects– Completed and operational, but:
• Behind Schedule------- Project Risk• Over Cost--------------- Project Risk• With fewer features and functions than originally specified
------------ Product Risk 3. Failed Projects:
– Cancelled before completion or never implemented
Project Resolution is commonly categorized into three resolution types:
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Software CHAOSThe Standish Group has examined 30,000 Software Projects in the US since 1994. This "CHAOS" research has revealed a decided improvement in IT project management with the implementation of standards and practices such as IV&V. This improvement correlates with the rise in project success depicted in the chart below:
16% 53% 31%
27% 33% 40%
26% 46% 28%
28% 49% 23%
0% 20% 40% 60% 80% 100%
1994
1996
1998
2000
Successful
Challenged
Failed
Project Resolution History (1994-2000)
The Standish Group International, Inc.: Extreme CHAOS (2001) - The 2001 update to the CHAOS report. http://www.standishgroup.com/sample_research/PDFpages/extreme_chaos.pdf
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Error Detection/CorrectionEarly error detection and correction are vital. The cost to correct software errors multiplies during the software development lifecycle. Early error detection and correction reduce costs and save time.
Test
Code
Design
Requirements
0
5
10
15
20
25
30
35
40
45
50
Relative Cost to Fix
Phase Found
De
fec
t
Ty
pe
Relative Cost to Fix Defects per Phase Found
Test Code Design Requirements
Direct Return on Investment of Software Independent Verification and Validation: Methodology and Initial Case Studies, James B. Dabney and Gary Barber, Assurance TechnologySymposium, 5 June 2003.
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IV&V Characteristics• Includes Risk Identification and Mitigation Techniques • Provides Independent Evaluation / Assessment of:
– Are we building the product right? = Verification – Are we building the right product? = Validation
• Requires Technical, Managerial and Financial Independence • Makes a value added contribution, everyone shares the same
mission success objective– For NASA Management - Provides Mission Assurance – For Project Management - Provides Unbiased Source of Help
• Helps deliver – Risk Identification and Mitigation– Increased Quality and Safety – Improved Timeliness and Reliability – Reduced Rework Cost
NPD 8730.4: Requires NASA programs and projects that contain mission or safety critical software to document decisions concerning the use of IV&V.
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OUTLINE
• Risk Management• Software Architecture Risk Assessment
– Reliability-based risk– Performance-based risk– Maintainability-based risk – Component Ranking
• Conclusions• Next Steps
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Software Architecture Risk Assessment
This work is funded in part by grants to West Virginia University Research Corp. from the NSF (ITR) Program, and from the NASA Office of Safety and Mission Assurance (OSMA) Software Assurance Research Program (SARP) managed through the NASA Independent Verification and Validation (IV&V) Facility, Fairmont
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Project Overview
• Risk Assessment of software architecture components, usage scenarios, and requirements
• Risk definition is based on
* Frequency of abnormal events
* Severity or consequences of events
• Reliability-based risk,• Performance-based risk• Requirement–based risk• Severity Analysis• Maintainability-based risk
What keeps satellites working 24/7 ?
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Software Architecture Risk Assessment
• An architecture-based approach for risk assessmentComponents\connectors, requirements, and scenario risk,
• Define several types of risk factors Reliability-based Risk [IEEE Trans. on Rel 2001, on SE, 2002, 2003]
Probability of failure * Severity or Consequences of this
failureMaintainability-based Risk [RAMS 06, ICSM 05, ICSM 04]
Probability of performing maintenance task * Cost of performing this task
• The losses caused by low system maintainability can be:– High cost of maintenance effort – Loss of the system by aging
Performance-based Risk [IEEE Trans. SE, Jan. 2005]
Probability of missing timing or performance requirements
* Severity or Consequences
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Importance / Benefits
• Identify the high risk components of the system in terms of Reliability/Maintainability/Performance
Components
Risk
Factor
Components Risk
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Software Architecture Risk Assessment
Methodology
Requirements Model System Architecture Model
Components Risk Factors
Maintainability-based
Risk Analysis
Performance-based
Risk Analysis
Reliability-based
Risk Analysis
Software Architecture Risk Assessment
Components Ranking
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OUTLINE
• Risk Management• Software Architectures• Software Architecture Risk Assessment
– Maintainability-based risk • Conclusions• Next Steps
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Maintainability-based Risk
[ICSM 05] AbdelMoez, Goseva, Ammar, Mili, Fuhrman, “Architectural level Maintainability Based Risk Assessment”
[RAMS 06] AbdelMoez, Goseva, Ammar,” Methodology for Maintainability Based Risk Assessment”, Jan 2006.
Maintenance EffortCorrective
20%
Adaptive25%
Perfective55%
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Importance / BenefitsMaintainability-based
Risk • According to Pigoski, 60%-80% of the system budget
is spent on maintenance
Maintenance EffortCorrective
20%
Adaptive25%
Perfective55%
Enhancements (perfective/ adaptive maintenance) account for 78%-83% of the maintainer effort
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• Unisys holds the NASA contract to maintain and support 14 million lines of ground software for the space shuttle
• There were 3,800 requirement changes made to the software after the loss of Challenger. These changes resulted in 900 software releases, of which 30 applied to the mission-control center with 3 of these being major upgrades • Reference:
IEEE Software, Vol.6, No.1, pp. 116-119
Importance / BenefitsMaintainability-based Risk
http://hebb.cis.uoguelph.ca/~dave/343/Lectures/introduction.html
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Importance / BenefitsTrade off Analysis for Perfective
Maintenance
apptgui banner borg btgui calgui calmodel errmsg helpscrn model propgui srchgui taskgui taskmodel tdgui-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25Change in Risk
Components
Cha
nge
in R
isk
Risk
Factor
Components
Patterns
Components Risk
Maintainability risk for perfective maintenance (open source case study Borg)
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Software Architecture Risk Assessment
Methodology: Maintainability-based Risk
Requirments maturity Index/ change / error reports
SW Architecture
(2) Estimate Change
Propagation (CP) probabilities
(3) Estimate Size of Change
(SC)
(4) Estimate component risk factor
ICP=[icpi] CP=[cpi/j] SC=[sci/j]
(1) Estimate componentsInitial Change Probability
(ICP)
ijijij
jjijiji sccpsccpicpmrMR //// . . . .
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Maintenance change propagation
Incoming maintenance Outgoing maintenance
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Estimating Change Propagation
C1C2V11
V1
V12
V13
= 1
= 0
Change in ProvidedService
• Change Propagation Probabilities matrix CP=[cpij ]
cpij is the probability that a change in Ci due to corrective/ perfective maintenance requires a change in Cj while maintaining the overall function of a system S
cpij = P([Cj] [Cj'] | [Ci] [Ci'] ^ [S] = [S'] ) cpij is estimated by
.
.V13
RequiredServices
cpij = iV
ijv
iV
||
1
ijv
ijv
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scij =
||
1||
1 iVijv
iM
Estimating Size of Change
Size of change SC=[scij ] scij is defined as the ratio between the number of affected
methods of the receiving component caused by the changes in the interface elements of the providing components and the total number of methods in the receiving component
scij is estimated by
C1C2V11
V1
V12
V13Change in ProvidedService
M1
M2
M3
…
M7
ReceivingComponent
methods
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CM1 Maintainability-Based Risk in Adaptive Maintenance Context
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Case Study: NASA CM1 UML Model Structure Diagram
• The UML-RT Model of CM1 wasDeveloped by WVU students (Nathan, Tom and Rajesh, Summer 2004) based on the CM1 software design specification
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Change Propagation Probabilities for CM1
• The Change Propagation probabilities CP is estimated using the CM1 UML model
• The Change Propagation probabilities CP can be automatically estimated from UML-RT models, or java source
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Size of Change for CM1
• The Size of Change metrics SC is estimated using the CM1 UML model
• The Size of Change metrics SC Probabilities CP can be automatically estimated from UML-RT models, or Java source
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Software Architecture Risk Assessment
Methodology: Maintainability-based Risk
Requirments maturity Index/ change / error reports
SW Architecture
(2) Estimate Change
Propagation (CP) probabilities
(3) Estimate Size of Change
(SC)
(4) Estimate component risk factor
ICP=[icpi] CP=[cpi/j] SC=[sci/j]
(1) Estimate componentsInitial Change Probability
(ICP)
ijijij
jjijiji sccpsccpicpmrMR //// . . . .
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Maintainability-based Risk For corrective maintenance
(case study CM1)
ICP is estimated
using error reports data
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Prioritizing Corrective Maintenance Tasks for CM1
CriticalMajorCatCriticalCat.CriticalMajorMajorMinorCat.Cat.MinorSeverity
Level
TMALITISSSISCUI1553ICUIEDACDPADCXDCICCMBIT
Components
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Maintainability-based Risk Maintainability risk for Adaptive maintenance (case study CM1)
ICP is estimated using change reports data
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Tool Support
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Technology Readiness Level The Software Architecture Risk Assessment
Tool Support
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Conclusions
• Risk Management is vital to the success of projects and products
• A risk Assessment process is needed• Software Architecture is a major determinant of
software quality • Software Architecture can be used to manage
project and product risks • Development of a methodology and a process for
software architecture risk assessment • Continued development of a software architecture
risk assessment tool to support the methodology
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Papers Published1. Vittorio Cortellessa, Katerina Goseva-Popstojanova, Kalaivani Appukkutty, Ajith R. Guedem, Ahmed
Hassan, Rania Elnaggar, Walid Abdelmoez, Hany H. Ammar, “Model-Based Performance Risk Analysis, IEEE Transactions on Software Engineering, January 2005, (Vol. 31, No. 1), pp.3-20.
2. Katerina Goseva-Popstojanova, Ahmed Hassan, Ajith Guedem, Walid Abdelmoez, Diaa Eldin M. Nassar, Hany Ammar, Ali Mili, "Architectural-Level Risk Analysis Using UML", IEEE Transactions on Software Engineering, October 2003 (Vol. 29, No. 10), pp.946-960.
3. S. Yacoub, H. Ammar, “A Methodology for Architectural-Level Reliability Risk Analysis,” IEEE Transactions on Software Engineering, Vol. 28, No. 6, June 2002.
4. W. AbdelMoez, K. Goseva-Popstojanova, H.H. Ammar,” Methodology for Maintainability-Based Risk Assessment”, Proc. of the 52nd Annual Reliability & Maintainability Symposium (RAMS 2006), Newport Beach, Ca., January 23-26, 2006.
5. Israr P. Shaik , W. Abdelmoez, R. Gunnalan, M. Shereshevsky, A. Zeid, H.H. Ammar, A. Mili, C. Fuhrman, “Change Propagation for Assessing Design Quality of Software Architectures”, Proc. of 5th IEEE/IFIP
Working Conference on Software Architecture (WICSA), Pittsburgh, Pa., USA, November 6-9, 2005.
6. AbdelMoez, W., I. Shaik, R. Gunnalan, M. Shereshevsky, K. Goseva-Popstojanova, H.H. Ammar, A. Mili, C. Fuhrman, “Architectural level Maintainability Based Risk Assessment”, Proc. of poster papers in IEEE International Conference on Software Maintenance (ICSM 2005), Budapest, Hungary, September 25-30,2005.
7. W. Abdelmoez, D. M. Nassar, M. Shereshevsky, N. Gradetsky, R. Gunnalanm and H. H. Ammar, Bo Yu, and Ali Mili "Error Propagation in Software Architectures". In Proceedings of the 10th International Symposium on Software Metrics (METRICS'04), September 11 - 17, 2004 , IEEE Comp. Soc., pp 384-393
8. Abdelmoez, W., M. Shereshevsky, R. Gunnalan, H. H. Ammar, Bo Yu, S. Bogazzi, M. Korkmaz, A. Mili, "Software Architectures Change Propagation Tool (SACPT),” 20th IEEE International Conference on Software Maintenance (ICSM'04) September 11 - 14, 2004 , Chicago, Illinois, IEEE Comp. Soc., pp 517
9. A. Hassan, K. Goseva-Popstojanova, and H. Ammar, “UML Based Severity Analysis Methodology”, Proceedings of the 2005 Annual Reliability and Maintainability Symposium (RAMS 2005), Alexandria, VA, January 2005.