Reverse Engineering Feature Models. . . S. She, R. Lotufo, T. - PowerPoint PPT Presentation

Reverse Engineering Feature Models. . . S. She, R. Lotufo, T. Berger, A. Wasowski, K. Czarnecki Generative Software Development Lab University of Waterloo . . International Conference on Software Engineering, Honolulu, Hawaii, USA.

Outline. 1 . . . . Conclusions 5 Evaluation 4 Procedures 3 Feature Models 2 Introduction Steven She. Reverse Engineering Feature Models. 2 / 29

Timeline. . . . . . parts of the FreeBSD FMs. Rev. engineering Linux, eCos, and . Linux and eCos feature models. . Evolution of Linux feature models. . Initial study on the Linux FM. . Sample spaces and FMs. . Propositional logic and FMs. . SPLC ’07 . SPLC ’08 . VaMoS ’10 . SPLC ’10 . ASE ’10 . ICSE ’11 . Steven She. Reverse Engineering Feature Models. 3 / 29

Motivation. software product lines (SPLs), e.g. Linux and eCos. understand for users and developers. project artifacts. . . . . • Feature models are great for representing variability in • Some SPLs don’t have feature models, e.g. FreeBSD. • Without a feature model, dependencies are hard to • Solution: Reverse engineer a feature model from existing • We present procedures to address this task. Steven She. Reverse Engineering Feature Models. 4 / 29

Outline. 1 . . . . Conclusions 5 Evaluation 4 Procedures 3 Feature Models 2 Introduction Steven She. Reverse Engineering Feature Models. 5 / 29

A Propositional Feature Model… is a Tree + Cross-Tree Constraints. . . . . acpi_system pm acpi performance cpu_freq cpu_hotplug powersave powersave ∧ acpi → cpu_hotplug Steven She. Reverse Engineering Feature Models. 6 / 29

Configuration Semantics. A feature model describes a set of legal configurations. Legal configurations can be represented by a formula. . . . . ] ≡ φ [ [ FM ] Steven She. Reverse Engineering Feature Models. 7 / 29

Formula. . cpu_hotplug cpu_freq performance acpi pm acpi_system powersave . . . powersave ∧ acpi → cpu_hotplug φ = child-parent impl. ∧ mandatory impl. ∧ implies edges ∧ excludes edges ∧ feature groups ∧ cross-tree constraints Steven She. Reverse Engineering Feature Models. 8 / 29

Propositional Logic to Feature Models. that describe its legal configurations. FM = tree + cross-tree constraints. . . . . Given φ , there are many possible feature models Steven She. Reverse Engineering Feature Models. 9 / 29

Ontological semantics. dst is an experimental feature. . . . . assistance. Linux different ontological semantics. dst staging net Linux dst staging net • Two FMs with the same configuration semantics, but • We need a human to decide the placement of a feature. • But, names and descriptions can be used to provide Steven She. Reverse Engineering Feature Models. 10 / 29

Ontological semantics. dst is an experimental feature. . . . . assistance. Linux different ontological semantics. dst staging net Linux dst staging net • Two FMs with the same configuration semantics, but • We need a human to decide the placement of a feature. • But, names and descriptions can be used to provide Steven She. Reverse Engineering Feature Models. 10 / 29

Outline. 1 . . . . Conclusions 5 Evaluation 4 Procedures 3 Feature Models 2 Introduction Steven She. Reverse Engineering Feature Models. 11 / 29

Overview. . Reverse-Engineering Procedures. building hierarchy. Key challenge. edges are automatically detected. . . . • Building the feature hierarchy. • Task—Select a parent for each feature. • Interactive—procedures provides assistance to the user for • Robust—we consider and evaluate on incomplete input. • Mandatory features, feature groups, implies and excludes Steven She. Reverse Engineering Feature Models. 12 / 29

Overview. . Reverse-Engineering Procedures. building hierarchy. Key challenge. edges are automatically detected. . . . • Building the feature hierarchy. • Task—Select a parent for each feature. • Interactive—procedures provides assistance to the user for • Robust—we consider and evaluate on incomplete input. • Mandatory features, feature groups, implies and excludes Steven She. Reverse Engineering Feature Models. 12 / 29

Required Input. . . . Feature Names . Descriptions . . Dependencies . . D φ F Steven She. Reverse Engineering Feature Models. 13 / 29

Names and Descriptions. pm Power management, CPU and ACPI options acpi Adv. Configuration and Power Interface support acpi_system Enable your system to shut down using ACPI cpu_freq CPU frequency scaling cpu_hotplug Allows turning CPU on and off powersave This CPU governor uses the lowest frequency performance This CPU governor uses the highest frequency . . . . Steven She. Reverse Engineering Feature Models. 14 / 29

Dependencies. . . . . ( acpi → acpi_system ∧ pm ) ∧ ( acpi_system → acpi ) ∧ ( cpu_freq → pm ) ∧ ( cpu_freq → powersave ∨ performance ) ∧ ( cpu_hotplug → powersave ) ∧ ( cpu_hotplug → ¬ performance ) ∧ ( cpu_hotplug → acpi ∧ cpu_freq ) ∧ ( powersave → ¬ performance ) ∧ ( powersave → cpu_freq ) ( performance → cpu_freq ) ∧ ( powersave ∧ acpi → cpu_hotplug ) ∧ Steven She. Reverse Engineering Feature Models. 15 / 29

Parent Candidates. acpi_system 4. 5. cpu_freq powersave performance acpi ... 2. feature implies sorted by similarity. similarity. . . . . Ranked Implied Features 3. . 5. . 2. Ranked All-Features 3. 1 4. powersave Selected: cpu_hotplug acpi acpi_system cpu_freq pm CPU frequency scaling. 1 • Ranked Implied Features (RIFs) : set of features the selected • Ranked All-Features (RAFs) : all features sorted by their Steven She. Reverse Engineering Feature Models. 16 / 29

Implication Graph. . cpu_freq powersave performance acpi acpi_system cpu_hotplug pm . . . • An edge v → w : implication from v to w exists in φ . • Direct implications are the thick edges. • Calculated using a SAT solver. Steven She. Reverse Engineering Feature Models. 17 / 29

Similarity Heuristic. how many words they share. document frequency (IDF) between the two features. . . . . • Similarity function δ compares two features and measures • Removes stop words, then calculates the sum of the inverse • RIFs ( f ): Using the implication graph, prioritize direct implications then all remaining implications, sorted by δ . • RAFs ( f ): All features, sorted by δ . Steven She. Reverse Engineering Feature Models. 18 / 29

Groups and Cross-Tree Constraints. groups, and the remaining edges in the implication and mutex graphs. . . . . • Construct a Mutex Graph to detect exclusions. • Detect mutex and xor -groups. • Implies and excludes edges are found using the hierarchy, Steven She. Reverse Engineering Feature Models. 19 / 29

Outline. 1 . . . . Conclusions 5 Evaluation 4 Procedures 3 Feature Models 2 Introduction Steven She. Reverse Engineering Feature Models. 20 / 29

Linux and eCos Feature Models. . . . . . . • Kconfig and CDL languages. • Reference models to evaluate our procedures. Steven She. Reverse Engineering Feature Models. 21 / 29

FreeBSD Reference Model. . . FreeBSD kernel. . . . . • Dependencies and descriptions extracted from various artifacts in • Manually created ontology of 192 features. • Reference FM of 90 features extracted from ontology. • Thorsten’s area of expertise. Steven She. Reverse Engineering Feature Models. 22 / 29

Characterization of Input Data. . . . 200 50 150 40 ecos 30 100 20 50 10 0 0 600 800 500 freebsd 600 400 Number of Features 300 400 200 200 100 0 0 10 freebsd−ref 80 8 60 6 40 4 20 2 0 0 2000 700 600 1500 500 linux 400 1000 300 200 500 100 0 0 0 50 100 150 0 20 40 60 80 100 Number of Words Number of Implications Steven She. Reverse Engineering Feature Models. 23 / 29

Measuring Effectiveness. . RAFs Is the reference parent in the Top 5 results? How many features do we have to examine to have a 75% chance of finding the reference parent? RIFs randomly removing implications and words. . . . • Applied measures to complete and incomplete data sets. • Generated sets of incomplete data for Linux and eCos by Steven She. Reverse Engineering Feature Models. 24 / 29