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Software: the Ground Rules
      Truths, Facts, Axioms



          Itay Maman
Solid

Nothing new
You can’t control what you can’t measure.

– Tom DeMarco, 1982
I can see why measuring productivity is so seductive. If
we could do it we could assess software much more
easily and objectively than we can now. But false
measures only make things worse. This is somewhere I
think we have to admit to our ignorance.

– Martin Fowler, 2003
Truth 1: Software is buggy




No systematic way to detect bugs
Truth 2: Recursiveness (I)




A module is built from similar modules
Implications

     Diffcult to estimate size (time)


A building spans a fxed-depth hierarchy
Building → Floor → Apartment → Room


A class spans a tree of unbounded depth
Truth 3: Recursiveness (II)




  Every programming language
is powerful enough to express itself
    (or every other language)
Fact 1: Long Tail




How many methods are ugly?
    How ugly are they?
JUnit
Ant
JRE
Eclipse
Normal Distribution
Power-Law Distribution
Implications


      Costs: Expect the worse


Cheap & expensive do not cancel out
Fact 2: Entropy




     Code gets messy over time
Restoring order requires explicit effort
Thermodynamics




         Chaos occurs naturally
External energy is needed to restore order
FindBugs 0.72 (March 2004)
FindBugs 1.35 (September 2008)
Ant 1.65
Axiom 1: Discontinuity




  One small step for a programmer,
One giant failure for an entire company
A few hours ago I was upgrading our continuous
integration setup when a confguration error caused it
to run against our production environment rather
than our testing environment.

– Chris Wanstrath
Today we made a change to the persistent copy of a
confguration value that was [mistakenly] interpreted
as invalid [by an automated system for verifying
confguration values].
This meant that every single client saw the invalid
value and attempted to fx it. Because the fx involves
making a query to a cluster of databases, that cluster
was quickly overwhelmed by hundreds of thousands
of queries a second.
… To make matters worse, every time a client got an
error attempting to query one of the databases it
interpreted it as an invalid value, and deleted the
corresponding cache key.
This meant that even after the original problem had
been fxed, the stream of queries continued. As long as
the databases failed to service some of the requests,
they were causing even more requests to themselves.
We had entered a feedback loop that didn’t allow the
databases to recover.

– Robert Johnson
Explanations




       Binary World
Most approximations will fail
Implications




Hard to predict effect/scope of a change
           Retest everything
Axiom 2: Super-linearity




       Size Matters
Axiom 2: Super-linearity




     Smaller is better
Axiom 2: Super-linearity




Cost of a programming task
  is super-linear in its size
Axiom 2: Super-linearity




Few large tasks more expensive
            than
     numerous small tasks
Manifestation




Source control confict resolution
       1 day vs. 1 month
Manifestation




         Debugging
Build cycle: 10 sec. vs. 5 min.
Manifestation




             Debugging
Reproduction steps: Automatic vs. GUI
Manifestation
Explanation




                7 +/- 2
S/W construction == A tree of decisions
Subsystems
             Exploitation




                Features
Benefts
                   Higher throughput


               #Features >> #Subsystems
             size(feature) << size(subsystem)
Subsystems




                         Features
Benefts

                   Less starvation


             Reduce the damage of delays
Subsystems




                       Features
Challenge


             Effcient decomposition
                       into
             Small, independent, tasks
Subsystems




                      Features
Exploitation


  Plugin-architectures
         TDD
Continuous Deployment
  Short loop to client
Old data, New Conclusions
Analogies




Appealing
Analogies




  Risky
Analogies?


Car Manufacturing
   Car Design
    Buildings
     Novels
      Cities
Imagine...


A factory that interacts with its own products,
             Can produce itself,
         Goes wild when changed,
   With unpredictable maintenance times
In my refective mood, I’m wondering, was its advice
[“You can’t control what you can’t measure”] correct
at the time, is it still relevant, and do I still believe
that metrics are a must for any successful software
development effort?
In my refective mood, I’m wondering, was its advice
[“You can’t control what you can’t measure”] correct
at the time, is it still relevant, and do I still believe
that metrics are a must for any successful software
development effort?
My answers are no, no, and no.
In my refective mood, I’m wondering, was its advice
[“You can’t control what you can’t measure”] correct
at the time, is it still relevant, and do I still believe
that metrics are a must for any successful software
development effort?
My answers are no, no, and no.

– Tom DeMarco, 2009
Thank you




http://javadots.blogspot.com

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Ground rules

  • 1. Software: the Ground Rules Truths, Facts, Axioms Itay Maman
  • 3. You can’t control what you can’t measure. – Tom DeMarco, 1982
  • 4. I can see why measuring productivity is so seductive. If we could do it we could assess software much more easily and objectively than we can now. But false measures only make things worse. This is somewhere I think we have to admit to our ignorance. – Martin Fowler, 2003
  • 5. Truth 1: Software is buggy No systematic way to detect bugs
  • 6. Truth 2: Recursiveness (I) A module is built from similar modules
  • 7. Implications Diffcult to estimate size (time) A building spans a fxed-depth hierarchy Building → Floor → Apartment → Room A class spans a tree of unbounded depth
  • 8. Truth 3: Recursiveness (II) Every programming language is powerful enough to express itself (or every other language)
  • 9. Fact 1: Long Tail How many methods are ugly? How ugly are they?
  • 10. JUnit
  • 11. Ant
  • 12. JRE
  • 16.
  • 17. Implications Costs: Expect the worse Cheap & expensive do not cancel out
  • 18. Fact 2: Entropy Code gets messy over time Restoring order requires explicit effort
  • 19. Thermodynamics Chaos occurs naturally External energy is needed to restore order
  • 23.
  • 24. Axiom 1: Discontinuity One small step for a programmer, One giant failure for an entire company
  • 25. A few hours ago I was upgrading our continuous integration setup when a confguration error caused it to run against our production environment rather than our testing environment. – Chris Wanstrath
  • 26. Today we made a change to the persistent copy of a confguration value that was [mistakenly] interpreted as invalid [by an automated system for verifying confguration values]. This meant that every single client saw the invalid value and attempted to fx it. Because the fx involves making a query to a cluster of databases, that cluster was quickly overwhelmed by hundreds of thousands of queries a second.
  • 27. … To make matters worse, every time a client got an error attempting to query one of the databases it interpreted it as an invalid value, and deleted the corresponding cache key. This meant that even after the original problem had been fxed, the stream of queries continued. As long as the databases failed to service some of the requests, they were causing even more requests to themselves. We had entered a feedback loop that didn’t allow the databases to recover. – Robert Johnson
  • 28. Explanations Binary World Most approximations will fail
  • 29. Implications Hard to predict effect/scope of a change Retest everything
  • 30. Axiom 2: Super-linearity Size Matters
  • 31. Axiom 2: Super-linearity Smaller is better
  • 32. Axiom 2: Super-linearity Cost of a programming task is super-linear in its size
  • 33. Axiom 2: Super-linearity Few large tasks more expensive than numerous small tasks
  • 34. Manifestation Source control confict resolution 1 day vs. 1 month
  • 35. Manifestation Debugging Build cycle: 10 sec. vs. 5 min.
  • 36. Manifestation Debugging Reproduction steps: Automatic vs. GUI
  • 38. Explanation 7 +/- 2 S/W construction == A tree of decisions
  • 39. Subsystems Exploitation Features
  • 40. Benefts Higher throughput #Features >> #Subsystems size(feature) << size(subsystem) Subsystems Features
  • 41. Benefts Less starvation Reduce the damage of delays Subsystems Features
  • 42. Challenge Effcient decomposition into Small, independent, tasks Subsystems Features
  • 43. Exploitation Plugin-architectures TDD Continuous Deployment Short loop to client
  • 44. Old data, New Conclusions
  • 47. Analogies? Car Manufacturing Car Design Buildings Novels Cities
  • 48.
  • 49. Imagine... A factory that interacts with its own products, Can produce itself, Goes wild when changed, With unpredictable maintenance times
  • 50. In my refective mood, I’m wondering, was its advice [“You can’t control what you can’t measure”] correct at the time, is it still relevant, and do I still believe that metrics are a must for any successful software development effort?
  • 51. In my refective mood, I’m wondering, was its advice [“You can’t control what you can’t measure”] correct at the time, is it still relevant, and do I still believe that metrics are a must for any successful software development effort? My answers are no, no, and no.
  • 52. In my refective mood, I’m wondering, was its advice [“You can’t control what you can’t measure”] correct at the time, is it still relevant, and do I still believe that metrics are a must for any successful software development effort? My answers are no, no, and no. – Tom DeMarco, 2009