Problem Detection (EVO 2008)
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This document discusses software metrics and design problems. It provides examples of common code metrics like lines of code, cyclomatic complexity, and coupling between objects. However, it notes that metrics have limitations in that they measure symptoms rather than causes of problems and don't directly lead to improvement actions on their own due to issues with thresholds and granularity. Metrics are better used to assess and improve quality when combined with design principles rather than in isolation. Reverse engineering is proposed as a way to better understand large, existing systems by analyzing components and relationships at a more abstract level.
![Noise Elimination
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Problem Detection (EVO 2008)
- 1. Metrics and Problem Detection Tudor Gîrba www.tudorgirba.com
- 2. Software is complex. 29% Succeeded 18% Failed 53% Challenged The Standish Group, 2004
- 3. How large is your project?
- 4. How large is your project? 1’000’000 lines of code
- 5. How large is your project? 1’000’000 lines of code * 2 = 2’000’000 seconds
- 6. How large is your project? 1’000’000 lines of code * 2 = 2’000’000 seconds / 3600 = 560 hours
- 7. How large is your project? 1’000’000 lines of code * 2 = 2’000’000 seconds / 3600 = 560 hours / 8 = 70 days
- 8. How large is your project? 1’000’000 lines of code * 2 = 2’000’000 seconds / 3600 = 560 hours / 8 = 70 days / 20 = 3 months
- 9. } } { { } } { { g rin ee gin en d ar rw fo
- 10. fo rw ar d en gin ee rin g { { { { { { } { { } } actual development } } } { } } }
- 11. What is the current state? fo rw ar What should we do? d en Where to start? gin ee How to proceed? rin g { { { { { { } { { } } actual development } } } { } } }
- 12. fo rw g rin ar ee d gin en gin en ee se rin erv g re { { { { { { } { { } } actual development } } } { } } }
- 13. Reverse engineering is analyzing a subject system to: identify components and their relationships, and create more abstract representations. Chikofky & Cross, 90
- 14. { { { { } } } } { } A large system contains lots of details.
- 15. ity? its qual ju dge How to { { { { } } } } { } A large system contains lots of details.
- 17. 1 Metrics 2Design Problems 3 Code Duplication
- 18. Metrics 1
- 19. Youcannot control what you cannot measure. Tom de Marco
- 20. Metrics are functions that assign numbers to products, processes and resources.
- 21. Software metrics are measurements which relate to software systems, processes or related documents.
- 22. Metrics compress system traits into numbers.
- 23. Let’s see some examples...
- 24. Examples of size metrics NOM - number of methods NOA - number of attributes LOC - number of lines of code NOS - number of statements NOC - number of children Lorenz, Kidd, 1994 Chidamber, Kemerer, 1994
- 25. McCabe cyclomatic complexity (CYCLO) counts the number of independent paths through the code of a function. McCabe, 1977 it reveals the minimum number of tests to write interpretation can’t directly lead to improvement action
- 26. Weighted Method Count (WMC) sums up the complexity of class’ methods (measured by the metric of your choice; usually CYCLO). Chidamber, Kemerer, 1994 it is configurable, thus adaptable to our precise needs interpretation can’t directly lead to improvement action
- 27. Depth of Inheritance Tree (DIT) is the (maximum) depth level of a class in a class hierarchy. Chidamber, Kemerer, 1994 inheritance is measured only the potential and not the real impact is quantified
- 28. Coupling between objects (CBO) shows the number of classes from which methods or attributes are used. Chidamber, Kemerer, 1994 it takes into account real dependencies not just declared ones no differentiation of types and/or intensity of coupling
- 29. Tight Class Cohesion (TCC) counts the relative number of method-pairs that access attributes of the class in common. Bieman, Kang, 1995 TCC = 2 / 10 = 0.2 interpretation can lead to improvement action ratio values allow comparison between systems
- 30. Access To Foreign Data (ATFD) counts how many attributes from other classes are accessed directly from a measured class. Marinescu 2006
- 31. ...
- 32. Design Problems 2
- 33. McCall, 1977
- 34. Metrics Assess and Improve Quality!
- 35. Metrics Assess and Improve Quality! a lly ? Re
- 36. McCall, 1977
- 37. Problem 1: metrics granularity ? capture symptoms, not causes of problems in isolation, they don’t lead to improvement solutions
- 38. Problem 1: metrics granularity ? capture symptoms, not causes of problems in isolation, they don’t lead to improvement solutions Problem 2: implicit mapping we don’t reason in terms of metrics, but in terms of design principles
- 39. 2 big obstacles in using metrics: Thresholds make metrics hard to interpret Granularity make metrics hard to use in isolation
- 40. Can metrics help me in what I really care for? :)
- 41. fo rw ar d en gin ee rin g { { { { { { } { { } } actual development } } } { } } }
- 42. fo rw ar How do I understand code? d en gin ee rin g { { { { { { } { { } } actual development } } } { } } }
- 43. fo rw ar How do I understand code? d en How do I improve code? gin ee rin g { { { { { { } { { } } actual development } } } { } } }
- 44. etr ics! ith m de al w an t to on ot w Id fo rw ar How do I understand code? d en How do I improve code? gin ee rin g { { { { { { } { { } } actual development } } } { } } }
- 45. How to get an initial understanding of a system?
- 46. Metric Value LOC 35175 NOM 3618 NOC 384 CYCLO 5579 NOP 19 CALLS 15128 FANOUT 8590 AHH 0.12 ANDC 0.31
- 47. Metric Value LOC 35175 NOM 3618 NOC 384 CYCLO 5579 NOP 19 CALLS 15128 FANOUT 8590 AHH 0.12 ANDC 0.31
- 48. Metric Value LOC 35175 NOM 3618 NOC 384 CYCLO 5579 NOP 19 CALLS 15128 FANOUT 8590 ha t? ww AHH An d no 0.12 ANDC 0.31
- 49. We need means to compare.
- 50. hierarchies? coupling?
- 51. The Overview Pyramid provides a metrics overview. Lanza, Marinescu 2006 Inheritance ANDC 0.31 AHH 0.12 20.21 NOP 19 9.42 NOC 384 9.72 NOM 3618 NOM 418 0.15 LOC 35175 15128 CALLS 0.56 CYCLO 5579 8590 FANOUT Size Communication
- 52. The Overview Pyramid provides a metrics overview. Lanza, Marinescu 2006 ANDC 0.31 AHH 0.12 20.21 NOP 19 9.42 NOC 384 9.72 NOM 3618 NOM 418 0.15 LOC 35175 15128 CALLS 0.56 CYCLO 5579 8590 FANOUT Size
- 53. The Overview Pyramid provides a metrics overview. Lanza, Marinescu 2006 ANDC 0.31 AHH 0.12 20.21 NOP 19 9.42 NOC 384 9.72 NOM 3618 NOM 418 0.15 LOC 35175 15128 CALLS 0.56 CYCLO 5579 8590 FANOUT Communication
- 54. The Overview Pyramid provides a metrics overview. Lanza, Marinescu 2006 Inheritance ANDC 0.31 AHH 0.12 20.21 NOP 19 9.42 NOC 384 9.72 NOM 3618 NOM 418 0.15 LOC 35175 15128 CALLS 0.56 CYCLO 5579 8590 FANOUT
- 55. The Overview Pyramid provides a metrics overview. Lanza, Marinescu 2006 ANDC 0.31 AHH 0.12 20.21 NOP 19 9.42 NOC 384 9.72 NOM 3618 NOM 418 0.15 LOC 35175 15128 CALLS 0.56 CYCLO 5579 8590 FANOUT
- 56. Java C++ LOW AVG HIGH LOW AVG HIGH CYCLO/LOC 0.16 0.20 0.24 0.20 0.25 0.30 LOC/NOM 7 10 13 5 10 16 NOM/NOC 4 7 10 4 9 15 ...
- 57. The Overview Pyramid provides a metrics overview. Lanza, Marinescu 2006 ANDC 0.31 AHH 0.12 20.21 NOP 19 9.42 NOC 384 9.72 NOM 3618 NOM 418 0.15 LOC 35175 15128 CALLS 0.56 CYCLO 5579 8590 FANOUT close to high close to average close to low
- 58. The Overview Pyramid provides a metrics overview. Lanza, Marinescu 2006 close to high close to average close to low
- 59. etr ics! ith m de al w an t to on ot w Id fo rw ar How do I understand code? d en How do I improve code? gin ee rin g { { { { { { } { { } } actual development } } } { } } }
- 60. How do I improve code?
- 61. Quality is more than 0 bugs. Breaking design principles, rules and best practices deteriorates the code; it leads to design problems.
- 62. Imagine changing just a small design fragment
- 63. Imagine changing just a small design fragment
- 64. Imagine changing just a small design fragment and33% of all classes would require changes
- 65. expensive Design problems are frequent unavoidable
- 66. expensive Design problems are frequent unavoidable th em? ate limin nd e ete ct a to d How
- 67. God Classes tend to centralize the intelligence of the system, to do everything and to use data from small data-classes. Riel, 1996
- 68. God Classes tend to centralize the intelligence of the system, to do everything and to use data from small data-classes.
- 69. God Classes centralize the intelligence of the system, do everything and use data from small data-classes.
- 70. God Classes are complex, are not cohesive, access external data.
- 71. God Classes are complex, WMC is high are not cohesive, TCC is low access external data. ATFD more than few
- 72. God Classes are complex, WMC is high are not cohesive, TCC is low access external data. ATFD more than few sing uer ies u to q s s in ator etric per se m ical o Co mpo log
- 73. Detection Strategies are metric-based queries to detect design flaws. Lanza, Marinescu 2006 Rule 1 METRIC 1 > Threshold 1 AND Quality problem Rule 2 METRIC 2 < Threshold 2
- 74. A God Class centralizes too much intelligence in the system. Lanza, Marinescu 2006 Class uses directly more than a few attributes of other classes ATFD > FEW Functional complexity of the class is very high AND GodClass WMC ! VERY HIGH Class cohesion is low TCC < ONE THIRD
- 75. An Envious Method is more interested in data from a handful of classes. Lanza, Marinescu 2006 Method uses directly more than a few attributes of other classes ATFD > FEW Method uses far more attributes of other classes than its own AND Feature Envy LAA < ONE THIRD The used "foreign" attributes belong to very few other classes FDP ! FEW
- 76. Data Classes are dumb data holders. Lanza, Marinescu 2006 Interface of class reveals data rather than offering services WOC < ONE THIRD AND Data Class Class reveals many attributes and is not complex
- 77. Data Classes are dumb data holders. Lanza, Marinescu 2006 More than a few public data NOAP + NOAM > FEW AND Complexity of class is not high WMC < HIGH Class reveals many OR attributes and is not Class has many public complex data NOAP + NOAM > MANY AND Complexity of class is not very high WMC < VERY HIGH
- 78. Shotgun Surgery depicts that a change in an operation triggers many (small) in a lot of different operation and classes. Lanza, Marinescu 2006
- 79. Code Duplication 3
- 80. What is Code Duplication?
- 81. What is Code Duplication? obl em? a pr hy is it A nd w
- 82. Code Duplication Detection Lexical Equivalence Syntactical Equivalence Semantic Equivalence
- 83. Visualization of Copied Code Sequences File A File B File A File B
- 84. Transformation Comparison Source Code Transformed Code Duplication Data Author Level Transformed Code Comparison Technique Johnson 94 Lexical Substrings String-Matching Ducasse 99 Lexical Normalized Strings String-Matching Baker 95 Syntactical Parameterized Strings String-Matching Mayrand 96 Syntactical Metrics Tuples Discrete comparison Kontogiannis 97 Syntactical Metrics Tuples Euclidean distance Baxter 98 Syntactical AST Tree-Matching
- 85. Noise Elimination … //assign same fastid as container fastid=NULL; fastid = NULL; constchar*fidptr=get_fastid(); const char* fidptr = get_fastid(); if(fidptr!=NULL) if(fidptr != NULL) { intl=strlen(fidptr) int l = strlen(fidptr); fastid = newchar[l+] fastid = newchar[ l + 1 ];
- 86. Enhanced Simple Detection Approach
- 87. lines from source a b c d a b c d lines from source
- 88. lines from source a b c d a x y d lines from source
- 89. lines from source a b c a b x y c lines from source
- 90. lines from source a x b x c x d x lines from source
- 91. lines from source 2 lines from source 1
- 92. lines from source 2 lines from source 1
- 93. lines from source 2 lines from source 1 exact chunk
- 94. lines from source 2 lines from source 1 exact line chunk bias
- 95. lines from source 2 lines from source 1 exact line exact chunk bias chunk
- 96. Significant Duplication is large and should be looked at Lanza, Marinescu 2006
- 97. 1 Metrics 2Design Problems 3 Code Duplication
- 98. Shotgun Surgery has uses is has (partial) Feature Data Envy uses Class is partially God has Intensive Class Coupling Brain has has Method Extensive Brain has Significant Coupling Class Duplication has is is has Refused is Tradition Parent Breaker Bequest has (subclass) Futile Hierarchy Lanza, Marinescu 2006 Identity Collaboration Classification Disharmonies Disharmonies Disharmonies
- 99. Follow a clear and repeatable process
- 100. Follow a clear and repeatable process
- 101. Follow a clear and repeatable process
- 102. Follow a clear and repeatable process mb ers! so f nu in term qu ality ab out re ason D on’t
- 103. QA is part of the the Development Process http://loose.upt.ro/incode
- 104. Tudor Gîrba www.tudorgirba.com creativecommons.org/licenses/by/3.0/