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USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 1
Barry Boehm, USC-CSSE
http://csse.usc.edu
Fall 2011
Some Future Software Engineering Opportunities and Challenges
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 2
Outline
• The Future of Information Technology– 8 surprise-free trends; 2 wild-card trends– Changes since 2005 paper– Individual and combined software
engineering opportunities and challenges
• Conclusions: General SW engineering implications– Research, staffing/education
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 3
The Future of Systems and Software: 2005
• Eight surprise-free trends1. Increasing integration of SysE and SwE2. User/Value focus3. Software Criticality and Dependability4. Rapid, Accelerating Change5. Distribution, Mobility, Interoperability, Globalization6. Complex Systems of Systems7. COTS, Open Source, Reuse, Legacy Integration8. Computational Plenty
• Two wild-card trends9. Autonomy Software10.Combinations of Biology and Computing
USC
C S E University of Southern CaliforniaCenter for Software Engineering
2010 Trends Largely Missed in 2005
• Megasensor-intensive smart systems
• Search and mining of ultralarge data aggregations
• Software implications of multicore chips
• Rapid growth of software as a service
• Rapid growth of social networking technologies
04/20/23 4
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 5
The Future of Systems and Software: 2010
• Eight surprise-free trends1. Rapid, Accelerating Change2. Software Criticality and Dependability3. Complexity; Global/Mobile Systems of Systems4. COTS, Open Source, Services, Legacy Integration5. Smart Systems; Mining huge volumes of data6. User Patterns and End Value Focus7. Computational Plenty and Multicore Chips8. Increasing integration of SysE and SwE
• Two wild-card trends9. Autonomy Software10.Combinations of Biology and Computing
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 6
1. Rapid Change Trends
• Global connectivity and competition accelerate change
– More ripple effects of technology, marketplace changes• Increased need for agility, continuous learning
– Need to balance agility and plan-driven dependability– Decline of THWADI (That’s how we’ve always done it)– Avoid technical agility, administrative THWADI
• Hybrid agile/plan-driven processes needed for larger systems
• Need for incremental processes, methods, tools, skills
• Need for pro-active technology, marketplace monitoring
• Education: Need to learn how to learn
USC
C S E University of Southern CaliforniaCenter for Software Engineering
Architected Agile Approach
• Uses Scrum of Scrums approach– Up to 10 Scrum teams of 10 people each– Has worked for distributed international teams– Going to three levels generally infeasible
• General approach shown below– Often tailored to special circumstances
04/20/23 7
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 8
2. Criticality and Dependability Trends
• Software increasingly success-critical to product and services
– Provides competitive differentiation, adaptability to change• Dependability is generally not vendors’ top-priority
– “The IT industry spends the bulk of its resources… on rapidly bringing products to market.” – US PITAC Report
• By 2025, there will be a “9/11” – magnitude software failure
– Major loss of life or collapse of world financial system• This will raise dependability to vendors’ top priority
– Market demand; stronger warranties and accountability– Value-based dependability processes and tools
• Avoid bureaucratic solutions• Reflect all stakeholders’ value dependencies
USC
C S E University of Southern CaliforniaCenter for Software Engineering
9
Achieving Agility and High Assurance -IUsing timeboxed or time-certain development
Precise costing unnecessary; feature set as dependent variable
Rapid Change
HighAssurance
Short, StabilizedDevelopment
Of Increment N
Increment N Transition/O&M
Increment N Baseline
Short DevelopmentIncrements
ForeseeableChange
(Plan)
Stable DevelopmentIncrements
04/20/23
USC
C S E University of Southern CaliforniaCenter for Software Engineering
Evolutionary Concurrent Engineering: Incremental Commitment Spiral Model
Agile Rebaselining for
Future Increments
Short, StabilizedDevelopment
of Increment N
Verification and Validation (V&V)of Increment N
Deferrals
Artifacts Concerns
Rapid Change
HighAssurance
Future Increment Baselines
Increment N Transition/
Operations and Maintenance
Future V&V
Resources
Increment N Baseline
Current V&V
Resources
Unforeseeable Change (Adapt)
ShortDevelopmentIncrements
ForeseeableChange
(Plan)
Stable DevelopmentIncrements
Continuous V&V
1004/20/23
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 11
3. Complexity and Global Software-IntensiveSystems of Systems (SISOS)
• Lack of integration among stovepiped systems causes– Unacceptable delays in service– Uncoordinated and conflicting plans– Ineffective or dangerous decisions– Inability to cope with fast-moving events
• Increasing SISOS benefits– See first; understand first; act first– Network-centric operations coordination– Transformation of business/mission potential– Interoperability via Integrated Enterprise Architectures
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 12
Complexity of Solution Spaces
• Size: 10-100 MLOC• Number of external interfaces: 30-300• Number of “Coopetitive” suppliers: 20-200
– Even more separate work locations• Depth of supplier hierarchy: 6-12 levels• Number of coordination groups: 20-200
– Reviews, changes, risks, requirements, architecture, standards, procedures, technologies, -ilities, integration, test, deployment, personnel, infrastructure, COTS,…
– Key personnel spend 60 hours/week in meetings• Unprecedentedness• Emergence• Rapid change
USC
C S E University of Southern CaliforniaCenter for Software Engineering
SourceSelection
● ● ●
ValuationExploration Architecting Develop Operation
ValuationExploration Architecting Develop Operation
ValuationExploration Architecting Develop Operation
OperationDevelop Operation Operation Operation
System A
System B
System C
System x
LCO-typeProposal &Feasibility
Info
Candidate Supplier/ Strategic Partner n ●
●●
Candidate Supplier/Strategic Partner 1
SoS-Level ValuationExploration Architecting Develop
FCR1 DCR1
Operation
OCR1
Rebaseline/Adjustment FCR1 OCR2
OCRx1
FCRB DCRB OCRB1
FCRA DCRA
FCRC DCRC OCRC1
OCRx2 OCRx3 OCRx4 OCRx5
OCRC2
OCRB2
OCRA1
Future DoD Challenges: Systems of Systems
1304/20/23
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 14
The Future of Systems and Software: 2010
• Eight surprise-free trends1. Rapid, Accelerating Change2. Software Criticality and Dependability3. Complexity; Global/Mobile Systems of Systems4. COTS, Open Source, Services, Legacy Integration5. Mining huge volumes of data6. User patterns and End Value focus7. Computational Plenty and Multicore Chips8. Increasing integration of SysE and SwE
• Two wild-card trends9. Autonomy Software10.Combinations of Biology and Computing
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 15
4. COTS: The Future Is Here• Escalate COTS priorities for research, staffing,
education– It’s not “all about programming” anymore– New processes required
CBA Growth in USC E-Service Projects
* Standish Group CHAOS 2000
CBA Growth Trend in USC e-Services Projects
0
10
20
30
40
50
60
70
80
1997 1998 1999 2000 2001 2002
Year
Perc
en
tag
e
*
USC
C S E University of Southern CaliforniaCenter for Software Engineering
Purchased Services Growth in USC e-Services Projects
04/20/23 16
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 17
Persistence of Legacy Systems• Before establishing new-system increments
– Determine how to undo legacy system
1939’s Science Fiction World of 2000 Actual World of 2000
USC
C S E University of Southern CaliforniaCenter for Software Engineering
Some Leading Brownfield ApproachesRe-engineering Legacy Software to be Service-Oriented
• IBM Brownfield VITA Approach– Views: Formal descriptions of enabling business systems or
processes
– Inventory: Repository that stores Views information
– Transforms: Define relationships between as-is and to-be states
– Artifacts: Results of Transforms generated from the Inventory
• CMU-SEI Service Migration and Reuse Technique (SMART)– SMART Process from as-is to to-be state
– Service Migration Interview Guide: over 60 questions about migration context, nature, and feasibility
– SMART Tool: Helps gather data, identify risks
– Artifact Templates: For capturing info about stakeholders, components, migration issues, legacy components, creating service components, etc.
04/20/2318
USC
C S E University of Southern CaliforniaCenter for Software Engineering
The Incremental Commitment Spiral Model
04/20/23 19
USC
C S E University of Southern CaliforniaCenter for Software Engineering
5. Megasensor- Empowered Smart SystemsSmart power grids, buildings, companies, cities
• Ubiquitously-instrumented artifacts and processes
• Complementary growth in data storage and analysis
• EU Digital Agenda “Internet of Things”
• Commitments: Singapore, Abu Dhabi, S. Korea, Portugal– Industry: IBM, HP, Cisco, Siemens, GE
• Generally Greenfield; incrementally for Brownfield
04/20/23 20
USC
C S E University of Southern CaliforniaCenter for Software Engineering
Mining huge volumes of data
• Google example: billions (B) of search hits– All in about 0.2 seconds (9/25/10; fewer, faster 11/17/10)– Video, 16.1B; TV, 9.6B; Star, 6.1B; Time, 5.4B; Movie, 4.4B;
News, 2.8B; Music, 2.7B; Life, 2.3B; Play, 2.1B; Book, 1.7B– What to show first?– How to narrow search to what you want?
• Recommender systems– Based on preference data or past activity– Amazon.com; Pandora; Netflix
• Service-provider data warehousing– Better services, but service provider has your data
• General concerns with privacy, controls
04/20/23 21
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 22
6. User/Value Focus Trends
• Computerworld panel: More focus on user/ownership costs and benefits; less focus on features and license costs
– Technology should adapt to people, not vice versa– Tension between usability and feature creep
• User-orientation has many challenges– Emergent needs and priorities: IKIWISI, Maslow– Diversity of people and cultures: no OSFA solutions– Group vs. individual performance– Engineer focus on engineer-usability
Golden Rule: Do unto others as you would have others do unto you
Platinum Rule: Do unto others as they would be done unto
IKIWISI: I’ll know it when I see it OSFA: one size fits all
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 23
Motivation for Value-Based SE
• Current SE methods are basically value-neutral– Every requirement, use case, object, test case, and
defect is equally important– Object oriented development is a logic exercise– “Earned Value” Systems don’t track business value– Separation of concerns: SE’s job is to turn requirements
into verified code– Ethical concerns separated from daily practices
• Value–neutral SE methods are increasingly risky– Software decisions increasingly drive system value– Corporate adaptability to change achieved via software
decisions– System value-domain problems are the chief sources of
software project failures
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 24
Value-Based Testing: Empirical Data and ROI— LiGuo Huang, ISESE 2005
-1.5
-1
-0.5
0
0.5
1
1.5
2
0 10 20 30 40 50 60 70 80 90 100
% Tests Run
Re
turn
On
In
ve
stm
en
t (R
OI)
Value-Neutral ATG Testing Value-Based Pareto Testing
% of Valuefor
CorrectCustomer
Billing
Customer Type
100
80
60
40
20
5 10 15
Automated test generation (ATG) tool
- all tests have equal value
Bullock data– Pareto distribution% of
Valuefor
CorrectCustomer
Billing
Customer Type
100
80
60
40
20
5 10 15
Automated test generation (ATG) tool
- all tests have equal value
% of Valuefor
CorrectCustomer
Billing
Customer Type
100
80
60
40
20
5 10 15
Automated test generation (ATG) tool
- all tests have equal value
Bullock data– Pareto distribution
(a)
(b)
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 25
The Future of Systems and Software: 2010
• Eight surprise-free trends1. Rapid, Accelerating Change2. Software Criticality and Dependability3. Complexity; Global/Mobile Systems of Systems4. COTS, Open Source, Services, Legacy Integration5. Mining huge volumes of data6. User Patterns and End Value Focus7. Computational Plenty and Multicore Chips8. Increasing integration of SysE and SwE
• Two wild-card trends9. Autonomy Software10.Combinations of Biology and Computing
USC
C S E University of Southern CaliforniaCenter for Software Engineering
7. Computational Plenty and Multicore Chips
• Moore’s Law stymied by heat dissipation problems– 2x circuit speed, density every 18 months
• Keep growth by developing multi-CPU chips– Lower circuit speed, but lower power consumption– Growth in #CPUs keeps up processing power growth– But only if programs can be parallelized– Otherwise, legacy software will run more slowly– Amdahl’s Law: Speed limited by speed of slowest part on
critical computation path
• But can also use CPUs for other purposes– Assertion checking, intrusion detection, trend analysis,
option analysis, performance monitoring, fault tolerance
04/20/23 26
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 27
8. Increasing SysE/SwE Integration
• Can’t do good SwE by neglecting SysE– Weak SysE the root cause of most SW project failures
• Can’t do good SysE by neglecting critical success factors– Software an increasing system critical success factor
• Provides most of competitive differentiation• Provides most of adaptability to change• Enables later binding of commitments
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 28
Why Software Projects Fail
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 29
9, 10. Wild Cards: Autonomy and Bio-Computing
• Great potential for good– Robot labor; human shortfall compensation
5 Senses, healing, life span, self-actualization
– Adaptive control of the environment
– Redesigning the world for higher quality of lifePhysically, biologically, informationally
• Great potential for harm– Loss of human primacy: computers propose, humans decide
– Overempowerment of humans Accidents, terrorism, 1984 revisited
– New failure modes: adaptive control instability, self-modifying software, commonsense reasoning, bio-computer mismatches
– V&V difficulties: cooperating autonomous agents, biocomputing
• Forms and timing of new capabilities still unclear
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 30
Outline
• The Future of Information Technology– 8 surprise-free trends; 2 wild-card trends– Changes since 2005 paper– Individual and combined software
engineering opportunities and challenges
• Conclusions: General SW engineering implications– Research, staffing/education
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 31
Software Process Research Implications – I
• Empirically-evolved process technology– Languages, methods, metrics, models, and tools– Incremental and ambiguity-tolerant– Accommodating incomplete, informal, and partial specification– Bridging formality, life-cycle, and culture gaps– Empirical testbed-based maturity/transition acceleration
• Virtual process collaboration support– Distributed, multi-stakeholder, multi-cultural
• Game technology for process education and training– Acquire and develop the way you train– Train the way you acquire and develop
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 32
Software Process Research Implications – II
• Lean, value-based processes for balancing dependability and agility– Plus scalability, incrementality for systems of systems– General techniques for multi-attribute tradeoff analysis– Associated progress metrics, review criteria, early warning
indicators
• Integrated technical and acquisition processes– Supporting balance of dependability and agility– Applicable to globally-distributed, multi-cultural collaboration
• Process capitalization on computational plenty– Self-monitoring software, higher levels of abstraction,
knowledge-based tools
• Integration and risk assessment of wild-card technologies– Autonomy, bio-computing
USC
C S E University of Southern CaliforniaCenter for Software Engineering
Software Engineering Education Implications
• Current software engineering students will be practicing into the 2050s. Their education should consider the following:
– Anticipating future trends and preparing students to deal with them; – Capitalizing on information technology to enable the delivery of just-in-time and
web-based education; – Monitoring current principles and practices and separating timeless principles
from outdated practices; – Participating in leading-edge software engineering research and practice and
incorporating the results into the curriculum; – Packaging smaller-scale educational experiences in ways that apply to large-
scale projects; – Helping students learn how to learn, through state-of-the-art analyses, future-
oriented educational games and exercises, and participation in research; and – Offering lifelong learning opportunities for systems engineers who must update
their skills to keep pace with the evolution of best practices
• ETH Zurich program an excellent education and research example
04/20/23 33
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 34
Backup Charts
• Limitations to software process perfectability
• Value-based systems and software engineering
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 35
Limitations: Brooks’ Four Essentials Plus Two
• Complexity: larger components, systems of systems, attribute tradeoffs
• Conformity: evolving standards, external system/COTS constraints
• Changeability: solution half-life, unpredictable certainties
• Conceptuality (invisibility): COTS opacity, multi-view consistency
• Community: stakeholder proliferation, distribution, diversity
• Centrality: software failure risks, rice-bowl implications
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 36
Limitations: Lampson’s Continuing SW Crisis
• Moore’s Law enables the feasibility of new applications– Requiring new and often more complex software
• Easier to handle complexity in software than elsewhere– Good engineering practice to address via software
• Few physical limits on software applications– Easy to overreach with proposed software solutions
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 37
Limitations: Converse of Conway’s Law
• Convay’s Law (extended to user organizations)– The structure of a computer program– Reflects the structure of– The organizations that build and use it
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 38
Limitations: Converse of Conway’s Law
• Conway’s Law (extended to user organizations)– The structure of a computer program– Reflects the structure of– The organizations that build and use it
• Converse of Conway’s Law– We will learn how to build perfectly functioning
software– As soon as– We learn how to build perfectly functioning
organizations
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 39
Initial VBSE Theory: 4+1 - with Apurva Jain
• Engine: Theory W (stakeholder win-win): What values are important?– Enterprise Success Theorem– Theory of Justice– Win-Win Equilibrium and Negotiation
• Four Supporting Theories– Utility Theory: How important are the values?
– Multi-attribute utility; Maslow need hierarchy
– Decision Theory: How do values determine decisions?– Investment theory; game theory; statistical decision theory
– Dependency Theory: How do dependencies affect value realization?
– Results chains; value chains; cost/schedule/performance tradeoffs
– Control Theory: How to monitor and control value realization– Feedback control; adaptive control; spiral risk control
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 40
Theory W: Enterprise Success Theorem– And informal proof
Theorem: Your enterprise will succeed if and only if
it makes winners of your success-critical stakeholders
• Proof of “if”: Everyone that counts is a winner. Nobody significant is left to complain.
• Proof of “only if”:Nobody wants to lose.Prospective losers will refuse to participate, or will counterattack.The usual result is lose-lose.
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 41
Initial VBSE Theory: 4+1 Process– With a great deal of concurrency and backtracking
Utility Theory
Theory W:SCS Win-Win
Decision Theory
Dependency Theory
Control Theory
6a, 7c. State measurement, prediction, correction; Milestone synchronization
5a. Investment analysis, Risk analysis
1. Protagonist goals3a. Solution exploration7. Risk, opportunity, change management
5a, 7b. Prototyping
2a. Results Chains3b, 5a, 7b. Cost/schedule/performance tradeoffs
2. Identify SCSs
3b, 7a. Solution Analysis
5a, 7b. Option, solution development & analysis
4. SCS expectations management
3. SCS Value Propositions(Win conditions)
SCS: Success-Critical Stakeholder
6, 7c. Refine, Execute, Monitor & Control Plans
5. SCS Win-Win Negotiation
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 42
By Number P-value % Gr A higher
By Impact P-value % Gr A higher
Average of Concerns 0.202 34 Average Impact of Concerns
0.049 65
Average of Problems 0.056 51 Average Impact of Problems
0.012 89
Average of Concerns per hour
0.026 55 Average Cost Effectiveness of Concerns
0.004 105
Average of Problems per hour
0.023 61 Average Cost Effectiveness of Problems
0.007 108
• Group A: 15 IV&V personnel using VBR procedures and checklists
• Group B 13 IV&V personnel using previous value-neutral checklists– Significantly higher numbers of trivial typo and grammar faults
ExperimentExperiment
Value-Based Reading (VBR) Experiment— Keun Lee, ISESE 2005
USC
C S E University of Southern CaliforniaCenter for Software Engineering
43
Adaptation Challenges: A Dual Cone of Uncertainty– Need early systems engineering, evolutionary development
Feasibility
Concept of Operation
Rqts. Spec.
Plans and
Rqts.
Product Design
Product Design Spec.
Detail Design Spec.
Detail Design
Devel. and Test
Accepted Software
Phases and Milestones
RelativeCost Range x
4x
2x
1.25x
1.5x
0.25x
0.5x
0.67x
0.8x
Uncertainties in competition, technology, organizations,
mission priorities
Uncertainties in scope, COTS, reuse, services
04/20/23
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 44
Agile and Plan-Driven Home Grounds: Five Critical Decision Factors
• Size, Criticality, Dynamism, Personnel, Culture
Personnel
Dynamism (% Requirements- change/month)
Culture (% thriving on chaos vs. order)
Size (# of personnel)
Criticality (Loss due to impact of defects)
3010
3.01.0
0.3
90
70
50
30
10
3
10
30
100
300
35
30
25
20
15
Essential Funds Discretionary
Funds Comfort
Single Life
Many Lives
(% Level 1B) (% Level 2&3)
0
10
20
30
40
Agile
Plan- driven
Personnel
Dynamism (% Requirements- change/month)
Culture (% thriving on chaos vs. order)
Size (# of personnel)
Criticality (Loss due to impact of defects)
90
70
50
30
10
3
10
30
100
300
35
30
25
20
15
Essential Funds Discretionary
Funds Comfort
Single Life
Many Lives
(% Level 1B) (% Level 2&3)
0
10
20
30
40
Agile
Plan- driven
USC
C S E University of Southern CaliforniaCenter for Software Engineering
45
Added Cost of Weak ArchitectingCalibration of COCOMO II Architecture and Risk Resolution
factor to 161 project data points
04/20/23
USC
C S E University of Southern CaliforniaCenter for Software Engineering
Effect of Size on Software Effort Sweet Spots
4604/20/23
USC
C S E University of Southern CaliforniaCenter for Software Engineering
DDR&E Systems 2020 Objectives and Constraints
OBJECTIVES
• DEVELOP FAST: Reduce by 3x the time to acquisition of first article for systems and solutions – DEVELOP FAST
• FLEXIBLE: Reduce by 4x the time to implement planned and foreseen changes in systems – FLEX
• ADAPTABLE: Embed within systems the ability for changes at the tactical edge, as the mission evolves in unplanned and unforeseen ways, e.g., IED threat – ADAPT
CONSTRAINTS
• Achieve the objectives while maintaining or enhancing:– Trust and Assurance – Able to withstand exploitation before or after
fielding, enabling the leveraging of global supply chains
– Reliability – Across a range of changing operational conditions
– Interoperability – Working with other systems to meet user needs04/20/23 47
USC
C S E University of Southern CaliforniaCenter for Software Engineering
DDR&E Systems 2020 Research Areas
Capability
on Demand
Capability
on Demand
Model Based
Engineering
Model Based
Engineering
Platform Based
Engineering
Platform Based
Engineering
Modeling and simulation tools for concurrent design, development &
manufacture
Architectural and automated design tools to rapidly insert new capabilities
Architectural and automated design tools to rapidly insert new capabilities
Systems embedded with organic adaption capabilities
Systems embedded with organic adaption capabilities
Faster delivery of complex, adaptive systemsFaster delivery of complex, adaptive systems
Trusted Systems Design
Trusted Systems Design
Design methods and tools for system assurance that detect malice or enable self
awareness
Design methods and tools for system assurance that detect malice or enable self
awareness04/20/23 48
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 49
Software Process Management Implications• Increasing uncertainty requires risk/value-based
processes– Concurrent engineering of system goals, solutions, plans
Integration of systems engineering and software engineering
– Thoroughly validated for consistency and feasibilityVia prototypes, benchmarks, models, role-playingAddressing both quantitative and qualitative value factorsValidation progress becomes a key management metricValidation shortfalls become risks to be managed
• Criticality and rapid change require balance of agile, plan-driven processes– Plan-driven for foreseeable change, high criticality
Parnas encapsulation of sources of change
– Agile for unforeseeable change– Continuous learning and adaptation
Especially in wild-card areas
USC
C S E University of Southern CaliforniaCenter for Software Engineering
04/20/23 50
Computational Plenty: Other Implications
• New platforms: smart dust, human prosthetics (physical, mental)– New applications: sensor networks, nanotechnology
• Enable higher levels of abstraction– Pattern programming, programming by example with
dialogue– Simpler brute-force solutions: exhaustive case analysis
• Enable more powerful software tools– Based on domain, programming, management knowledge– Show-and-tell documentation– Game-oriented software engineering education
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