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Variability Modeling in the Real: A Perspective from the Operating Systems Domain 25 th IEEE/ACM International Conference on Automated Software Engineering ASE 2010 Antwerp, Belgium, Sept. 22 nd , 2010 Thorsten Berger, Steven She, Rafael Lotufo,

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Variability Modeling in the Real: A Perspective from the Operating Systems Domain

Thorsten Berger 1

25th IEEE/ACM International Conference on Automated Software Engineering ASE 2010 Antwerp, Belgium, Sept. 22nd, 2010

Thorsten Berger, Steven She, Rafael Lotufo, Andrzej Wasowski, Krzysztof Czarnecki

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SLIDE 2

John Liebenberg / Barcroft Media andfinally.tv/2009/06/white-lions-released-into-wild

Biologists go into the Wild for their Studies…

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SLIDE 3

John Liebenberg / Barcroft Media andfinally.tv/2009/06/white-lions-released-into-wild

Biologists go into the Wild for their Studies…

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SLIDE 4

John Liebenberg / Barcroft Media andfinally.tv/2009/06/white-lions-released-into-wild

Feature Model

Biologists go into the Wild for their Studies…

Mane Appearance Sex Male Female Lion Habitat Size Small Medium Large Color

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SLIDE 5

John Liebenberg / Barcroft Media andfinally.tv/2009/06/white-lions-released-into-wild

Feature Model

Biologists go into the Wild for their Studies…

Mane Appearance Sex Male Female Lion Habitat Size Small Medium Large Color

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Variability Modeling

Thorsten Berger 6

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Variability Modeling

Thorsten Berger 7

Variability Model

(15810 possible configurations)

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Variability Modeling

Thorsten Berger 8

Variability Model

(15810 possible configurations)

Configuration #1

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Variability Modeling

Thorsten Berger 9

Variability Model

(15810 possible configurations)

Configuration #1 Configuration #2

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Variability Modeling

Thorsten Berger 10

Variability Model

(15810 possible configurations)

Configuration #1 Configuration #2

“Variability needs in software are constantly increasing, because:

  • Variability moves from mechanics and

hardware to software,

  • Design decisions are delayed as long as

economically feasible.“ Jan Bosch

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Feature Modeling

Thorsten Berger 11

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Feature Modeling

Thorsten Berger 12

Other Domains

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Feature Modeling

Thorsten Berger 13

FMP

Tools Other Domains

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eclipse.org/proposals/feature-model

Common Variability Language (CVL)

Feature Modeling

Thorsten Berger 14

FMP

Tools Other Domains Standardization Efforts

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NOW, WHAT‘S THE PROBLEM?

Thorsten Berger 15

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What about Empirical Research?

  • A lot of research on variability, but not on real models!

Thorsten Berger 16

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What about Empirical Research?

  • A lot of research on variability, but not on real models!
  • Some industry reports available, but not the models!

Thorsten Berger 17

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What about Empirical Research?

  • A lot of research on variability, but not on real models!
  • Some industry reports available, but not the models!
  • Assumptions of real models used for synthetic examples!

Thorsten Berger 18

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SLIDE 19

What about Empirical Research?

  • A lot of research on variability, but not on real models!
  • Some industry reports available, but not the models!
  • Assumptions of real models used for synthetic examples!
  • Literature studies:

Thorsten Berger 19

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SLIDE 20

What about Empirical Research?

  • A lot of research on variability, but not on real models!
  • Some industry reports available, but not the models!
  • Assumptions of real models used for synthetic examples!
  • Literature studies:
  • Hubaux et al. [VAMOS10]:

° Only 2% of reviewed papers (8 of 415) discuss applications of Feature Models in practice ° Few details about their usage given

Thorsten Berger 20

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SLIDE 21

What about Empirical Research?

  • A lot of research on variability, but not on real models!
  • Some industry reports available, but not the models!
  • Assumptions of real models used for synthetic examples!
  • Literature studies:
  • Hubaux et al. [VAMOS10]:

° Only 2% of reviewed papers (8 of 415) discuss applications of Feature Models in practice ° Few details about their usage given

  • Chen et al. [SPLC09]:

° “There is only little, if any, experimental or detailed comparative analysis … of different VM approaches.” ° All VM approaches share similar concepts ° Some sort of reference model needed for model transformations, tools and future research

Thorsten Berger 21

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SLIDE 22

OUR STUDY…

Thorsten Berger 22

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Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 23

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SLIDE 24

Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 24

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SLIDE 25

Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 25

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SLIDE 26

CONCEPTS, SEMANTICS AND USAGE OF… We study

Thorsten Berger 26

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Kconfig and CDL

Thorsten Berger 27

Linux Kernel 2.6.32

(22 hardware architectures, 6.4 mio. SLOC)

eCos 3.0

(embedded RTOS, 116 architectures, ~1 mio. SLOC)

6320 Features (X86) 1244 Features (I386) Kconfig language CDL language

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Kconfig and CDL

Thorsten Berger 28

Kconfig language

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Kconfig and CDL

Thorsten Berger 29

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Kconfig and CDL

Thorsten Berger 30

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SEMANTICS?

What do we mean by variability model

Thorsten Berger 31

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Variability Model Semantics

  • Configuration Space Semantics

Thorsten Berger 32

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Variability Model Semantics

  • Configuration Space Semantics

Thorsten Berger 33

menuconfig MISC_FILESYSTEMS bool "Miscellaneous filesystems" if MISC_FILESYSTEMS config JFFS2_FS tristate "Journalling Flash FS" if MTD select CRC32 if MTD config JFFS2_FS_DEBUG int "JFFS2 Debug level (0=quiet, 2=noisy)" depends on JFFS2_FS default 0 range 0 2

  • -- help ---

Debug verbosity of ...

C1={(JFFS2,y),(JFFS2_DEBUG,2),…} C2={(JFFS2,m),(JFFS2_DEBUG,0),…} Cn={(JFFS2,n),(JFFS2_DEBUG,0),…} Kconfig Model

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METHODOLOGY

Thorsten Berger 34

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Methodology - Design Space

Thorsten Berger 35

  • Reverse-engineered formal semantics (denotational style)
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Methodology - Design Space

Thorsten Berger 36

examples tools and their source code documentation

  • Reverse-engineered formal semantics (denotational style)
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Methodology - Design Space

Thorsten Berger 37

examples tools and their source code documentation

  • Reverse-engineered formal semantics (denotational style)

Abstract Syntax Semantic Domain Semantic Function

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Methodology - Design Space

Thorsten Berger 38

examples tools and their source code documentation

  • Reverse-engineered formal semantics (denotational style)

Abstract Syntax Semantic Domain Semantic Function

Propositional Semantic Domain Propositional Semantic Function Propositional Abstraction for Reasoners

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Methodology - Language Usage

Thorsten Berger 39

extension of configurators

LibCDL XConfig

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Methodology - Language Usage

Thorsten Berger 40

model export CDL

Intermediate Model

Kconfig

Intermediate Model

model export

extension of configurators

LibCDL XConfig

Linux and eCos models

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Methodology - Language Usage

Thorsten Berger 41

model export CDL

Intermediate Model

Kconfig

Intermediate Model

model export

extension of configurators

LibCDL XConfig

analysis infrastructure

parsing parsing

Linux and eCos models

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Methodology - Language Usage

Thorsten Berger 42

model export CDL

Intermediate Model

Kconfig

Intermediate Model

model export

extension of configurators

LibCDL XConfig

analysis infrastructure

parsing parsing

Linux and eCos models

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Methodology - Language Usage

Thorsten Berger 43

model export CDL

Intermediate Model

Kconfig

Intermediate Model

model export

extension of configurators

LibCDL XConfig

analysis infrastructure

parsing parsing

ϕecos

propositional abstraction

ϕlinux

propositional abstraction

Linux and eCos models

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Methodology - Language Usage

Thorsten Berger 44

model export CDL

Intermediate Model

Kconfig

Intermediate Model

model export

extension of configurators

LibCDL XConfig

analysis infrastructure

parsing parsing

ϕecos

propositional abstraction

ϕlinux

propositional abstraction

SAT-based analysis Linux and eCos models

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SLIDE 45

RESULTS

Thorsten Berger 45

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Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 46

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SLIDE 47

Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 47

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SLIDE 48

Feature Modeling Concepts

  • Concepts:
  • Boolean (optional), Integer and String features (attributes)
  • Hierarchy
  • Group constraints
  • Cross-tree constraints

Thorsten Berger 48

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Feature Modeling Concepts

  • Concepts:
  • Boolean (optional), Integer and String features (attributes)
  • Hierarchy
  • Group constraints
  • Cross-tree constraints
  • But intricate semantic interactions
  • Needed many iterations to get the formal semantics right
  • Often slightly different from what we expected

Thorsten Berger 49

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Feature Modeling Concepts

  • Concepts:
  • Boolean (optional), Integer and String features (attributes)
  • Hierarchy
  • Group constraints
  • Cross-tree constraints
  • But intricate semantic interactions
  • Needed many iterations to get the formal semantics right
  • Often slightly different from what we expected
  • Some (but minor) violations of feature modeling rules

Thorsten Berger 50

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SLIDE 51

Feature Modeling Concepts

  • Concepts:
  • Boolean (optional), Integer and String features (attributes)
  • Hierarchy
  • Group constraints
  • Cross-tree constraints
  • But intricate semantic interactions
  • Needed many iterations to get the formal semantics right
  • Often slightly different from what we expected
  • Some (but minor) violations of feature modeling rules
  • Languages benefit from being domain-specific

Thorsten Berger 51

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SLIDE 52

Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 52

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SLIDE 53

Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 53

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Beyond Feature Modeling

  • Concepts for scalability

Thorsten Berger 54

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Beyond Feature Modeling

  • Concepts for scalability
  • Visibility

Thorsten Berger 55

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Beyond Feature Modeling

  • Concepts for scalability
  • Visibility
  • Modularization

Thorsten Berger 56

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Beyond Feature Modeling

  • Concepts for scalability
  • Visibility
  • Modularization
  • Derived defaults /

derived features

Thorsten Berger 57

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Beyond Feature Modeling

  • Concepts for scalability
  • Visibility
  • Modularization
  • Derived defaults /

derived features

  • Expressive constraints
  • Kconfig: Three-state logic (follows Kleene‘s rules)
  • CDL: Comparison, arithmetic and String operators

Thorsten Berger 58

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Beyond Feature Modeling

  • Concepts for scalability
  • Visibility
  • Modularization
  • Derived defaults /

derived features

  • Expressive constraints
  • Kconfig: Three-state logic (follows Kleene‘s rules)
  • CDL: Comparison, arithmetic and String operators
  • Code mappings / build specifications

Thorsten Berger 59

Debug Level :int Compress Data Journalling Flash File System

  • Misc. Filesystems

Support ZLIB Default Compression None Size Priority

Mapping

Source files

… #IFDEF F1 … #IF defined(F4 & F6 ) … #ENDIF … ENDIF

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SLIDE 60

Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 60

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SLIDE 61

Three Research Questions

  • 1. Can we provide quantitative and qualitative empirical

evidence whether Feature Modeling concepts are used in real-world languages?

  • 2. Are additional concepts needed?
  • 3. Are the assumptions about real models in the literature

correct?

Thorsten Berger 61

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Assumptions

  • We always see nicely balanced trees…

Thorsten Berger 62

www.feasiple.de/description/bsp_ess_en.html code.google.com/p/dslvariantmanagement/wiki/DemoShowCase msdn.microsoft.com/en-us/library/aa925157.aspx

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Assumption

  • But Linux and eCos models are very shallow!

Thorsten Berger 63

  • Avg. depth
  • Max. depth
  • Max. branching
  • No. of leaves

Linux 4 8 158 5316 (84%) eCos 3 6 29 947 (76%)

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Assumption

  • But Linux and eCos models are very shallow!

Thorsten Berger 64

eCos model

  • Avg. depth
  • Max. depth
  • Max. branching
  • No. of leaves

Linux 4 8 158 5316 (84%) eCos 3 6 29 947 (76%)

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SLIDE 65

Assumption

  • But Linux and eCos models are very shallow!

Thorsten Berger 65

eCos model

  • Avg. depth
  • Max. depth
  • Max. branching
  • No. of leaves

Linux 4 8 158 5316 (84%) eCos 3 6 29 947 (76%)

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Assumption

  • But Linux and eCos models are very shallow!

Thorsten Berger 66

eCos model Linux model

  • Avg. depth
  • Max. depth
  • Max. branching
  • No. of leaves

Linux 4 8 158 5316 (84%) eCos 3 6 29 947 (76%)

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Assumptions

Thorsten Berger 67

  • Branching factors with

high variation!

  • Avg. depth
  • Max. depth
  • Max. branching
  • No. of leaves

Linux 4 8 158 5316 (84%) eCos 3 6 29 947 (76%)

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Assumptions

Thorsten Berger 68

  • Branching factors with

high variation!

  • Avg. depth
  • Max. depth
  • Max. branching
  • No. of leaves

Linux 4 8 158 5316 (84%) eCos 3 6 29 947 (76%)

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SLIDE 69

Assumptions

Thorsten Berger 69

  • Branching factors with

high variation!

  • Avg. depth
  • Max. depth
  • Max. branching
  • No. of leaves

Linux 4 8 158 5316 (84%) eCos 3 6 29 947 (76%)

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SLIDE 70

Some More Findings

  • Cross-Tree Constraints ratio
  • Literature: 30-40%
  • Our models: 86%

Thorsten Berger 70

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Some More Findings

  • Cross-Tree Constraints ratio
  • Literature: 30-40%
  • Our models: 86%
  • Very large constraints exist (Linux: 56 features, eCos: 21)

Thorsten Berger 71

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Some More Findings

  • Cross-Tree Constraints ratio
  • Literature: 30-40%
  • Our models: 86%
  • Very large constraints exist (Linux: 56 features, eCos: 21)
  • Very few group constraints (OR, XOR, MUTEX)

Thorsten Berger 72

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SLIDE 73

Some More Findings

  • Cross-Tree Constraints ratio
  • Literature: 30-40%
  • Our models: 86%
  • Very large constraints exist (Linux: 56 features, eCos: 21)
  • Very few group constraints (OR, XOR, MUTEX)
  • Kconfig and CDL configurators
  • Configuration process is Re-Configuration!
  • Limited or no reasoning support

° Kconfig relies on an imperative construct for choice propagation ° CDL has an inference engine, which is correct but incomplete

Thorsten Berger 73

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Some More Findings

  • Cross-Tree Constraints ratio
  • Literature: 30-40%
  • Our models: 86%
  • Very large constraints exist (Linux: 56 features, eCos: 21)
  • Very few group constraints (OR, XOR, MUTEX)
  • Kconfig and CDL configurators
  • Configuration process is Re-Configuration!
  • Limited or no reasoning support

° Kconfig relies on an imperative construct for choice propagation ° CDL has an inference engine, which is correct but incomplete

  • Many more details in the paper!

Thorsten Berger 74

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CONCLUSIONS

Thorsten Berger 75

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Conclusions

  • Empirical studies are fundamentally necessary in the VM

field to guide future research and to provide requirements for tool developers.

Thorsten Berger 76

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Conclusions

  • Empirical studies are fundamentally necessary in the VM

field to guide future research and to provide requirements for tool developers.

  • The more ways we look at how real languages are designed

and how models look like, the more confidence we have that we understand Software Product Lines.

Thorsten Berger 77

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SLIDE 78

Conclusions

  • Empirical studies are fundamentally necessary in the VM

field to guide future research and to provide requirements for tool developers.

  • The more ways we look at how real languages are designed

and how models look like, the more confidence we have that we understand Software Product Lines.

  • In studying the models in-depth, our findings have confirmed

– and refuted – previous knowledge about variability languages and models.

Thorsten Berger 78

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SLIDE 79

Conclusions

  • Empirical studies are fundamentally necessary in the VM

field to guide future research and to provide requirements for tool developers.

  • The more ways we look at how real languages are designed

and how models look like, the more confidence we have that we understand Software Product Lines.

  • In studying the models in-depth, our findings have confirmed

– and refuted – previous knowledge about variability languages and models.

  • Understanding languages and extracting these models that

were evolved over 10 years kept us PhD students and the professors busy for almost half a year!

Thorsten Berger 79

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SLIDE 80

Variability Modeling in the Real: A Perspective from the Operating Systems Domain

Thorsten Berger 80

25th IEEE/ACM International Conference on Automated Software Engineering ASE 2010 Antwerp, Belgium, Sept. 22nd, 2010

Thorsten Berger, Steven She, Rafael Lotufo, Andrzej Wasowski, Krzysztof Czarnecki

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SLIDE 81

APPENDIX

Thorsten Berger 81

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EXAMPLES

Thorsten Berger 82

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Hierarchy

  • Children can exclude their parent

Thorsten Berger 83

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Hierarchy

  • Children can exclude their parent

Thorsten Berger 84 config VIDEO_HELPER_CHIPS_AUTO bool "Autoselect pertinent encoders/decoders and other helper chips" default y

  • --help---

Most video cards may require additional modules to encode or config VIDEO_IR_I2C tristate "I2C module for IR" if !VIDEO_HELPER_CHIPS_AUTO depends on I2C && VIDEO_IR default y

  • --help---

Most boards have an IR chip directly connected via GPIO. menu "Encoders/decoders and other helper chips" depends on !VIDEO_HELPER_CHIPS_AUTO comment "Audio decoders" config VIDEO_TVAUDIO tristate "Simple audio decoder chips" depends on VIDEO_V4L2 && I2C

  • --help---

Support for several audio decoder chips found on some bt8xx boards:

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Defaults

  • Defaults can impose constraints in Kconfig

Thorsten Berger 85 config DW_DMAC tristate "Synopsys DesignWare AHB DMA support" depends on AVR32 select DMA_ENGINE default y if CPU_AT32AP7000

  • --help---

Support the Synopsys DesignWare AHB DMA controller. This can be integrated in chips such as the Atmel AT32ap7000.

  • We thought just
  • DW_DMAC → DMA_Engine Ʌ AVR32
  • But instead also
  • !AVR32 Ʌ CPU_AT32AP7000 → DW_DMAC Ʌ DMA_ENGINE
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SLIDE 86

Computed features

  • Computation of test cases

Thorsten Berger 86 cdl_component CYGPKG_HAL_TESTS { display "Common HAL tests" flavor data no_define calculated { "tests/context tests/basic„ . ((!CYGINT_HAL_TESTS_NO_CACHES) ? " tests/cache" : "") . ((CYGPKG_HAL_BUILD_COMPILER_TESTS) ? " tests/cpp1 tests/vaargs" : "") . ((!CYGVAR_KERNEL_COUNTERS_CLOCK) ? " tests/intr" : "") } description " This option specifies the set of tests for the common HAL."

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VARIABILITY MODELING APPROACHES

Thorsten Berger 87

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SLIDE 88

Variability Modeling Techniques

Thorsten Berger 88

Feature Models (FODA)

CDL Kconfig OVM COVAMOF Koalish Kumbang VPM KobrA FDL RequiLine CBFM DRM CONSUL Gears Forfamel VSL ConIPF

Concepts

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Feature Modeling Example

  • JFFS2 filesystem

Thorsten Berger 89

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Feature Modeling Example

  • JFFS2 filesystem

Thorsten Berger 90

Feature res

(Boolean (optional), String, Int)

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Feature Modeling Example

  • JFFS2 filesystem

Thorsten Berger 91

Hiera rarc rchy Feature res

(Boolean (optional), String, Int)

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Feature Modeling Example

  • JFFS2 filesystem

Thorsten Berger 92

Hiera rarc rchy Feature res

(Boolean (optional), String, Int)

Group p Co Const nstraints

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SLIDE 93

Feature Modeling Example

  • JFFS2 filesystem

Thorsten Berger 93

Hiera rarc rchy Feature res

(Boolean (optional), String, Int)

Group p Co Const nstraints

Cross ss-Tree Const strai aints