identification of variation points using dynamic analysis
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Identification of Variation Points Using Dynamic Analysis. Bas Cornelissen. Introduction. Product lines Several versions for various customers Each having its own set of features Variation points Configurable features. Introduction. Identifying variation points in source code - PowerPoint PPT PresentationTRANSCRIPT
Identification of Variation Points Using Dynamic Analysis
Bas Cornelissen
Introduction
• Product lines
• Several versions for various customers– Each having its own set of features
• Variation points– Configurable features
Introduction
• Identifying variation points in source code– Isolation of code responsible for specific features– Useful in merging product line members
• Research proposal– Dynamic analysis– Comparison of traces generated by two versions– Detection and visualization of variation points
Method
• Code instrumentation– At method level– Aspect-oriented programming
• Execution using similar scenarios– Aimed at invoking one particular feature
Method
• Detection algorithm– Sliding windows– Parameters
• Checksum size• Minimum branch length• Maximum branch length
• Visualization– Dot
Method
• Running example– Pacman– 20 classes, 1000 LOC– Various versions
• Original (reference)• Map extension• Additional game
entities, e.g. holes
Preliminary results
• Original vs. map version
Preliminary results
• Original vs. map version– Slightly different
initialization– One fork…
Preliminary results
• Original vs. map version– Slightly different
initialization– One fork…– …and a quick merge
Preliminary results
• Original vs. hole version
Preliminary results
• Original vs. hole version– Slightly different
initialization: a fork …
Preliminary results
• Original vs. hole version– Slightly different
initialization: a fork … …and a quick merge
Preliminary results
• Original vs. hole version– Slightly different
initialization: a fork … …and a quick merge
– Divergent behavior
→
Conclusions
• Efficient algorithm– Parameterized– Branch lengths have upper bounds
• Meaningful results– Architect gains quick insight into relevant source code
• Creating similar scenarios may be hard in some cases– e.g., deterministic behavior is a must
Future work
• Parameter optimization
• Improved accuracy– Incorporation of stack depths– Incorporation of method arguments
• Better visualization
• Large software systems– Support for other abstraction levels
Discussion points
• How can the parameters be optimized?– Maximum/minimum branch length
• How can we make the results more meaningful?