Software metrics
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The document discusses various software metrics that quantify code complexity, maintainability, and coupling in software development. It outlines concepts such as cyclomatic complexity, Halstead measures, efferent and afferent coupling, and their implications on the stability and readability of code. The presentation emphasizes the importance of these metrics in improving software design and maintainability.
Software metrics
- 1. Measure twice, cut once SOFTWARE METRICS
- 3. Make software measurable instead of relying on intuition. Software metrics
- 8. Every code path adds complexity Counts # of code paths Software metrics » Complexity Cyclomatic complexity
- 9. • Analyze control flow • Start at 1 • Increase at every decision path (if, for, case, …) Software metrics » Complexity Cyclomatic complexity
- 10. if ($user->isLoggedIn()) { echo 'Welcome back, ' . $user->getName(); } else { echo 'Hi there, stranger!'; } Cyclomatic complexity = 2 Software metrics » Complexity Cyclomatic complexity
- 11. Every code path adds complexity Counts # possible outcomes Software metrics » Complexity NPath complexity
- 12. Software metrics » Complexity NPath complexity if ($user->getSalutation()) { echo $user->getSalutation().' '; } if ($user->getName()) { echo $user->getName(); } else { echo 'anon'; } Outcomes: • $salutation anon • anon • $salutation $name • $name
- 13. Every operation adds complexity Software metrics » Complexity Halstead measures
- 14. • Analyze vocabulary • Count amount of operators (+, -, =, &&, ... and reserved words, like if and for) • Count amount of operands (values, variables & function names) Software metrics » Complexity Halstead measures
- 15. if ($user->isLoggedIn()) { echo 'Welcome back, ' . $user->getName(); } else { echo 'Hi there, stranger!'; } Operands = 4 (4 unique) Software metrics » Complexity Halstead measures
- 16. if ($user->isLoggedIn()) { echo 'Welcome back, ' . $user->getName(); } else { echo 'Hi there, stranger!'; } Operators = 11 (7 unique) Software metrics » Complexity Halstead measures
- 17. Engineered to match human analysis Software metrics » Complexity Maintainability index
- 18. Combination of: • Cyclomatic complexity • Halstead measures • Lines of code Software metrics » Complexity Maintainability index
- 19. Metrics for matthiasmullie/minify, MatthiasMullieMinifyJS::stripWhitespace See https://www.cauditor.org/matthiasmullie/minify/c17eb048daa44b43fa98bfa405147e77a040df76/metrics Software metrics » Complexity Maintainability index
- 20. Questions? Software metrics » Complexity
- 22. Amount of incoming dependencies = other classes using this one Common for core functionality Software metrics » Coupling Afferent coupling
- 23. class Afferent { function __construct() {} } class Efferent { function __construct(Afferent $a) {} } Afferent: 1, Efferent: 0 Software metrics » Coupling Afferent coupling
- 24. Changes here may break things elsewhere Must be stable & thoroughly tested Software metrics » Coupling Afferent coupling
- 25. Amount of outgoing dependencies = other classes being used Common for controllers Software metrics » Coupling Efferent coupling
- 26. class Afferent { function __construct() {} } class Efferent { function __construct(Afferent $a) {} } Afferent: 0, Efferent: 1 Software metrics » Coupling Efferent coupling
- 27. • Harder to read/maintain • Harder to reuse • Harder to test in isolation • Brittle Software metrics » Coupling Efferent coupling
- 28. Changes elsewhere may break things here Probably violates single responsibility principle Software metrics » Coupling Efferent coupling
- 29. = efferent coupling / total coupling = efferent coupling / (efferent + afferent) Resilience to change Software metrics » Coupling Instability
- 30. High efferent coupling = likely to be impacted by changes in = other classes = not stable Software metrics » Coupling Instability
- 31. Low afferent coupling = change is easy because there isn’t much = that depends on this = not stable Software metrics » Coupling Instability
- 32. Stable = good … as long as you don’t need to change the implementation! Software metrics » Coupling Instability
- 33. Aim for extremes, make classes as stable or unstable as possible! Unstables can be refactored/decomposed later. Better than having dependencies everywhere. Software metrics » Coupling Instability
- 34. Counts # of ancestors Software metrics » Coupling Depth of inheritance
- 35. class Ancestor {} DIT: 0 class Descendant1 extends Ancestor {} DIT: 1 class Descendant2 extends Descendant1 {} DIT: 2 Software metrics » Coupling Depth of inheritance
- 36. Inheritance is not bad! • Code reuse • Polymorphism Software metrics » Coupling Depth of inheritance
- 37. But too much is! • Less stable: change in ancestor can affect descendants • More complexity: methods of all ancestors are inherited Software metrics » Coupling Depth of inheritance
- 38. Questions? Software metrics » Coupling
- 39. WHY WOULD I CARE? Software metrics
- 40. Presentation titleSoftware metrics » Usage Which would you rather maintain?
- 41. Presentation titleSoftware metrics » Usage
- 42. Software metrics » Usage Complexity This guy has something going on! Let’s see…
- 43. It’s not too crazy! But: • Recursion • 3 loops • 4 conditions • Callback function Software metrics » Usage Complexity
- 44. Method does too much: • Data transformation • Processing • Cleanup Hard to test & prove right! Software metrics » Usage Complexity
- 45. Software metrics » Usage Complexity This guy has a lot going on…
- 47. Software metrics » Usage Coupling • Lots of outgoing dependencies • Some inheritance Very brittle!
- 48. Changes in any of these could affect this class! Software metrics » Usage Coupling
- 49. Software metrics » Usage Refactoring • 1000+ LOC class • Pretty complex • Very unstable • Self-contained, barely reusable
- 50. Software metrics » ObservationsSoftware metrics » Usage Reduced complexity everywhere Decreased + increased stability
- 51. Software metrics » Observations Refactoring • Greatly reduced “monster class” • Split into multiple reusable pieces Software metrics » Usage
- 52. Questions? Software metrics » Usage
- 54. Software metrics » Observations Spikes!
- 55. Complexity often caused by a few commits! Watch out with new features & rewrites. Software metrics » Observations Spikes!
- 56. Presentation titleSoftware metrics » Observations
- 57. Software metrics » Observations No evolution
- 58. • No change over time • No change across projects Do developers have a “signature”? Software metrics » Observations No evolution
- 59. Software metrics » Observations Personalities? 2 guys working on same project: • One was very fast (but messier) • One was a lot more thorough Who’s who?
- 60. Software metrics » Observations Personalities?
- 61. Software metrics » Observations Personalities? M essy Thorough
- 62. Software metrics » Observations Personalities? Can metrics show personality traits? Please help me figure that out! https://www.cauditor.org/user/feedback
- 65. mullie.eu • mullie.eu/measuring-software-complexity • mullie.eu/measuring-software-coupling • cauditor.org/help/metrics Software metrics Resources