Automatically Extracting Configuration Constraints Sarah Nadi Thorsten Berger

Automatically Extracting Configuration Constraints Sarah Nadi *, Thorsten Berger *, Christian Kästner +, and Krzysztof Czarnecki * Product Line Engineering Workshop, Univ. of Waterloo Dec. 9 th 2013 * +

Variability in real life Command navigation package • Integrated Garage Door Opener • Electronic Compass Premium Package • Black Dacota Leather • Automatic Trunk Selection will result in: + Addition of premium package Comfort Seats, Front Black Leatherette - Removal of Black Leathertte Sarah Nadi 2

Handling variability Build Independently Clone & Own Share Assets Software Product Lines (SPL) Product configuration Variability modeling Components DSLs Generators Preprocessors Design patterns … [Dubinski et al. , CSMR ‚ 13] Sarah Nadi Slide credits: T. Berger 3

But. . How can we build an SPL? Build from scratch X P 1 P 2 P 3 … Migrate Expensive and not always possible Sarah Nadi 4

What is involved in migrating? Identify Configurable Features Detect Variation Points Identify Feature Dependencies Refactor Code Create Variability Model Refactor Architecture [She et al. , ICSE‘ 11] Sarah Nadi 5

Variability model constraints Hierarchy Constraint: MP 3 => Media Cross-tree Constraint: Camera => High Res. [Benavides et al. , 2010] Sarah Nadi 6

Can we automatically extract constraints? To what extent? Sarah Nadi 7

Scope • C based systems using conditional compilation • Focus on build-time variability • Identify two sources of constraints Sarah Nadi 8

1. Conditional build-time errors Specification 1: Every valid configuration of the system must not contain build-time errors. Sarah Nadi 9

Pre-processor Error if ASH && NOMMU Invalid Configuration Constraint: ASH => !NOMMU Parser Error if ASH && EDITING && !MAX_LEN Type Error if ASH && EDITING_VI && MAX_LEN && !EDITING Sarah Nadi 10

2. Feature effect • Avoid meaningless configurations which do not add/remove parts of the code • If we add/remove a feature, we want to get different functionality • Determine under which configurations, a feature has an effect on the code Specification 2: Every valid configuration should yield a lexically different program Sarah Nadi 11

Feature effect MAX_LEN && EDITING && ASH MAX_LEN && EDITING_VI && ASH MAX_LEN => ASH && (EDITING || EDITING_VI) Sarah Nadi 12

Extract constraints by brute force P 1 P 2 P 3 … Pn Not scalable--- 2 n combinations Build Individually If every configuration with feature X compiles except when Y is also selected X => !Y If every configuration with feature Z does not change the selected code except if W is also selected Z => W Sarah Nadi 13

Extracting constraints in a single pass • Make use of variability-aware parsing & type checking to mimic build-time behaviour. [Kästner et al. , OOPSLA ‘ 11] Sarah Nadi 14

Infrastructure • Developed tool • Far. CE – Feature Constraints Extraction https: //bitbucket. org/tberger/farce • Rely on previous work: • Type. Chef – Type-Checking Ifdef Variability https: //github. com/ckaestne/Type. Chef • KBuild. Miner http: //code. google. com/p/variability/wiki/Presence. Conditions. Extraction • To analyze variability models: • LVAT https: //code. google. com/p/linux-variability-analysis-tools • CDLTools https: //bitbucket. org/tberger/cdltools Sarah Nadi 15

Empirical study • Objectives: • O 1: Evaluate accuracy and scalability of extraction • O 2: Quantitatively and qualitatively study kinds of (extractable) constraints in real-world systems • Used four systems with existing variability models u. Clibc 1, 628 C files 367 features Busy. Box 535 C files 844 Features e. Cos 579 C files 1, 254 features Linux Kernel 7, 691 C files 6, 559 Features • Compare extracted constraints to existing hierarchy & crosstree edges in the model Sarah Nadi 16

Is the extraction accurate? Specification 1 is 95% accurate Specification 2 is 76% accurate Sarah Nadi 17

Which constraints are recovered? Hierarchy edges reflected in code nesting Crosstree edges prevent build-time errors Can automatically recover 23% of variability model constraints! 5% by Specification 1 17% by Specification 2 Sarah Nadi 18

What about constraints not found? • Qualitative analysis of 144 unrecovered constraints Manual analysis of constraints is hard! 29% unknown 21% additional analyses 19% limitation in comparison Some constraints are non-technical & need expert knowledge Qualitative analysis is subjective 19% configurator-related 9% domain knowledge 3% limitation in extraction Sarah Nadi 19

Challenges • Presence conditions and constraints explode • Limit complexity of constraints used • Use non-SAT based constraint combination techniques • Different ways to compare constraints • Our comparison is limited to binary constraints • Other techniques which may be used? • Understand the intent of different constraints without interviewing developers Sarah Nadi 20

Extracting configuration constraints from code Automatically Done Conditional Build-time errors & Feature Effect Accurate (95% & 76% respectively) Extracts substantial parts of VM An expert may still be needed (Avg. 23% & up to 65%) Questions? Sarah Nadi (finishing soon … interested in a post doc) snadi@uwaterloo. ca Sarah Nadi http: //swag. uwaterloo. ca/~snadi 21

Pre-processor Error if ASH && NOMMU Invalid Configuration Constraint: ASH => !NOMMU Linker Error if ASH && EDITING && !INIT Parser Error if ASH && EDITING && !MAX_LEN Type Error if ASH && EDITING_VI && MAX_LEN && !EDITING Sarah Nadi 22

Constraint formulas Preprocessor, parser, and type-checking constraints Linker constraints conditional symbol table Feature effect Sarah Nadi 23

How are constraints used? • Hierarchy edges are mainly reflected in how features are used/nested in the code (Spec 2: feature effect analysis) • Cross tree edges are often used to prevent build-time errors (Spec 1: conditional build-time errors) Sarah Nadi 24