Transcript On software maintenance and evolution

On software maintenance
and evolution
University of Waterloo
Michael W. Godfrey
Software Architecture Group (SWAG)
University of Waterloo
Overview
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Software aging
Software “maintenance”
Software “evolution”
A case study of growth and evolution
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Software Aging
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Parnas on “Software Aging”
• “Bit rot” isn’t the problem!
• Rather:
– Our expectations change, new demands, new
environments, emergent properties, …
• e.g., MS-Word generates HTML, OLE, vmware,
network drives, XML as a RDBMS, …
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Parnas on “Software Aging”
BUT
– Large software systems are complex
• Difficult and expensive to change!
• “Legacy” systems are expensive assets!
– “Ignorant surgery” leads to brittle, hard-tochange systems
• Business pressures lead to “JIT maintenance”
• Accidental complexity accrues over time
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Parnas on “Software Aging”
• Parnas’ advice:
– Design for change!
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Isolate likely-to-change hotspots and virtualize
Keep documentation up-to-date
Perform design reviews
Perform redesigns
• But we know this already, right?
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Software “maintenance”
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A thought experiment …
• Ignore software for a moment …
– What does the term “maintenance” connote?
• Keep it running, replace worn out bits, upgrades
sometimes, …
– Is this what software maintainers do?
• Mostly not!
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Kinds of software maintenance
• What are the common categories of software
maintenance?
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Corrective
Adaptive
Perfective
Preventative
• Alas, there is much confusion about what these
terms (ought to) mean!
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Swanson’s original view
[ICSE 1976]
1. Corrective: [Fix program so that it meets its specs]
– Processing failure
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wrong answer or program aborts
– Performance failure
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doesn’t satisfy non-functional reqs, esp. real time performance
– Impl. failure
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programming standards ignored, impl. inconsistent/incomplete,
reqs not implemented correctly
2. Adaptive: [Fix to adjust to new underlying env./formats]
– Change in data format
– Change in processing environment
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Swanson’s original view
[ICSE 1976]
3. Perfective: [It works fine, but let’s make it even better]
– Processing inefficiency
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Even in performance is within specified params, may still
want to make it faster
– Performance enhancement
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Make the output more readable
(NOT related to response time)
– Maintainability
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Better use of comments, documentation
(NOT refactoring/restructuring/redesigning)
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Swanson’s original view
[ICSE 1976]
1. Corrective: [Fix program so that it meets its specs]
2. Adaptive: [Fix to adjust to new underlying env./formats]
3. Perfective: [It works fine, but let’s make it even better]
BUT
• Where does preventative maintenance fit in?
• Where does “make it faster” fit in?
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Swanson/Leintz
[CACM 1978]
Refined model, most commonly quoted:
1. Corrective:
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fixing bugs
2. Adaptive:
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adapting to new environment
– but no outward semantic change, no new features
3. Perfective:
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any other kind of change intended to make the
system “better”
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e.g, performance tuning, adding new features,
“preventative maintenance” (redesign/refactoring)
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IEEE Std on Software Engineering
Terminology [1990]
• Software maintenance
– “The process of modifying a software system or component after delivery to
correct faults, improve performance, or adapt to a changed environment”
• Corrective maintenance
– “Maintenance performed to correct faults in hardware or software.”
• Adaptive maintenance
– “Software maintenance performed to make a computer program usable in a
changed environment.”
• Perfective maintenance
– “Software maintenance performed to improve the performance, maintainability,
or other attributes of a computer program.”
• Preventative maintenance
– “Maintenance performed for the purpose of preventing problems before they
occur.”
Q: Adding new features?
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IEEE Std on Software Maintenance
Terminology [1998]
• Software maintenance
– “Modification of a software product after delivery to correct faults, improve
performance or other attributes, or adapt to a modified environment”
• Corrective maintenance
– “Reactive modification of a software product performed after delivery to correct
discovered faults.”
• Adaptive maintenance
– “Modification of a software product performed after delivery to keep a computer
program usable in a changed or changing environment.”
• Perfective maintenance
– “Modification of a software product after delivery to improve performance or
maintainability.”
• Preventative maintenance … is MIA
Q: Adding new features?
• Part of the “environment”?
• Not a maintenance activity per se?
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The current view
There’s no standard taxonomy … but here’s a reasonable view:
• Corrective maintenance
– Fix bugs
• Adaptive maintenance
– Add support for new deployment environments
– May include “adding new features”
• Perfective maintenance
– Improve non-functional requirements (performance, security) apart from
portability
– May include “adding new features”
– May include preventative maintenance
• Preventative maintenance
– Improve internal design w/o changing external semantics
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Software maintenance vs. evolution
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Software maintenance vs. evolution
• So far, we’ve treated initial delivery as the major
milestone in a product’s lifespan.
– What about changes during “initial” development?
– Is it reasonable to make such a sharp distinction between
pre- and post-initial delivery?
– Are there other important phases in a software systems?
– What are the forces at play as a system evolves?
– Is “evolution” a better term than “maintenance”?
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Waterfall-like process model
Requirements
engineering
Design and
implementation
Testing
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Staged model of maintenance and evolution
[Bennet/Rajlich 2000]
• The staged model recognizes that there are fundamentally
different life phases of a software system!
– At each stage you are typically performing mini-waterfall
development cycles … differently!
– The problems, tensions, solutions, processes are very different!
• Need to understand what is appropriate when.
– Lots of research needs to be done here!
• What is an evolvable architecture?
• What are the likely effects of different kinds of change?
… etc
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Staged model of maintenance and evolution
[Bennet/Rajlich 2000]
• Initial development
– First version developed, already
possess the architecture and the
fundamental system knowledge.
• [Active] evolution
– Takes place only when the initial
development was successful, the
goal is to adapt the application to
the ever-changing user
requirements.
• Servicing
– Once the architecture or the
knowledge disappears, product is
no longer evolvable (i.e., enters
the service stage)
• Phase out
– no more servicing.
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Staged model of maintenance and evolution
[Bennet/Rajlich 2000]
• Initial development
– System architecture is devised
• Often create a runnable infrastructure as soon as possible, gradually fill in
missing/fudged bits [aka staged delivery]
– Design for change!
• Consider strategically where you expect changes to occur and design your
architecture accordingly
• Ideal: Cost of making a future change is proportional to size/complexity of
change, not size/complexity of system
– Change is the rule, not the exception.
• Developers are aware of this.
• QA artifacts developed
– Knowledge about “the system” is fresh, accurate; experts can be found
easily
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Staged model of maintenance and evolution
[Bennet/Rajlich 2000]
• Active evolution
– Knowledge about system is good; some personnel turnover
since initial development.
– Simple changes and adaptation to new
requirements/environments are mostly straightforward
– System architecture is flexible enough to allow for more
wide-ranging changes to be planned and executed
successfully
• Architecture can be redesigned if necessary, and still be coherent
and reasonably faithful to the original system
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Staged model of maintenance and evolution
[Bennet/Rajlich 2000]
• Servicing
– Simple changes and adaptation to new
requirements/environments can still be done
• Architectural change is harder and risky!
– Knowledge about system is disappearing
• JIT maintenance starts to occur
• Effects of change (e.g., on other parts of system) become harder to
predict
– Start to treat system more like a caged animal than an
intellectual creation
• May perform preventative maintenance (program comprehension,
reverse engineering) tasks to recover design, stave off death
– In practice, it’s very hard to swim upstream back to “evolution” stage
• Legacy wrapping, adapters, regression testing, impact analysis, …
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Staged model of maintenance and evolution
[Bennet/Rajlich 2000]
• Phase out
– System is getting too risky and expensive ($$$,
performance, time/effort) to keep.
• A new solution is sought
• An exit strategy must be devised
– Common approaches:
• Create new adapter-style interface for clients/rest of system
• Perform data recovery and migration
• Deploy new solution to fit adapter interface, integrate with
recovered data
• Shut down old system
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Maintenance vs. evolution
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Software maintenance vs. evolution
• Maintenance connotes
– Fixing, rather than intellectually enhancing
– Short-term, not long-term goals
• Many prefer the term evolution:
– Fundamental change and adaptation
– Short- and long-term change
– Planned and unplanned phenomena
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[my two cents]
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What is evolution?
• My answer:
– Essential change in some identifiable
thing (or family) over time.
• Warning: Some very personal views follow.
– They are far from widely accepted.
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Evolution: Software vs. biology
• Biology has the hard scientific theory and
empirical results, but evolution is a generally
occurring phenomenon!
– Frogs, economies, ideas, music all evolve… and
software does too!
– Although the phenomenon of evolution is apparent
widely, it is hard to be truly scientific in its study
outside of biology.
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Three fundamentals ideas of evolution
1. Mechanisms that increase variation / act as an agent
for essential change.
2. Mechanisms that decrease variation / act as an
inhibitor for essential change.
3. It’s all relative, man.
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Change occurs within an environment, which also evolves.
That is, what succeeds today may not succeed tomorrow
(but may succeed the day after).
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Biological evolution in a nutshell
“[Biological] evolution is change in a gene pool of a
population over time.”
[talk.origins FAQ]
• Genes/alleles are the essential encoding that is inherited by
living creatures; they comprise the genotype of the individual.
– A gene is the dedicated “slot” or memory location
– Alleles are the set of abstract values at those genes
– DNA are the bits that encode the allele messages (ACGT)
• The phenotype is the totality of what an individual becomes.
– The phenotype is determined by the genotype PLUS interaction with the
environment.
– Non-genotypic change is not inheritable! [Lamarck was wrong!]
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Biological evolution in a nutshell
• Consider:
– Biston betularia, an English moth
• 2% dark in 1848, 95% dark in 1898 [Manchester]
– Hair colour, tattoos, height
• Morphological change  evolution
• Evolution  morphological change
– Sickle cell anemia
– Evolution is not progress!
• Biological evolution is a greedy algorithm
– Individuals are embedded in and alter their environments!
• They consume resource, alter the competitive balances, and
modify their environment to suit themselves as they are able
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Biological evolution in a nutshell
• Mechanisms that increase genetic variation
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Mutation (new values)
Recombination (new combinations)
Gene flow
Genetic drift (chance increase in existing allele freq.)
• Mechanisms that decrease genetic variation
– Natural selection (!!)
– Sexual selection
– Genetic drift (chance decrease in existing allele freq.)
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Biological vs. software evolution
• How hard should we push the analogy of
biology into software?
– OK, genotype == source code, but what is a
software phenotype?
– What is a software individual/population?
I won’t try to answer this …
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Biological vs. software evolution
• One important fact:
– Unlike biology, the prime agent for new
values/traits of software is not chance, it’s
marketing!
• Rate of change is much, much faster!
• Software evolution is (arguably) Lamarckian!
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Biological vs. software evolution
• One important observation:
– Not all of the effects of software change are
planned or foreseen.
• Software reacts with its environment (development,
deployment, political) and forms a complex feedback
system that influences the future evolution of the
software
[Lehman]
• Software systems exhibit interesting emergent
phenomena
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Summary:
Biological vs. software evolution
• If you take away nothing else from this, at least
remember this:
– “We shape our tools, then our tools shape us.”
[McLuhan]
– We can plan and execute changes …
• … but we cannot understand in advance all of the effects of the
changes, both on the environment and feeding back into the
development of the software system.
– Understanding how software evolves requires studying
both the planned and unplanned phenomena of software
change.
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Further reading
• Evolutionary biology:
– The talk.origins archives
• http://www.talkorigins.org
– The Selfish Gene, Richard Dawkins
• “Other” evolution:
– The Meme Machine, Susan Blackmore
– The Nature of Economies, Jane Jacobs
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