Self-composable Programming
This work addresses the problem of practical adaptation for variability management techniques in software engineering, though it appears incremental as it builds on existing object-oriented concepts.
The paper tackles the challenge of variability management in software development by introducing Self-composable Programming, a language-driven approach that uses object-oriented principles to model and compose behaviors, and it compares this method to Aspect-oriented Programming to evaluate its effectiveness.
Many variability management techniques rely on sophisticated language extension or tools to support it. While this can provide dedicated syntax and operational mechanism but it struggling practical adaptation for the cost of adapting new technology as part of development process. We present Self-composable Programming, a language-driven, composition-based variability implementation which takes an object-oriented approach to modeling and composing behaviors in software. Self-composable Programming introduces hierarchical relationship of behavior by providing concepts of abstract function, which modularise commonalities, and specific function which inherits from abstract function and be apply refinement to contain variabilities to fulfill desired functionality. Various object-oriented techniques can applicable in the refinement process including explicit method-based, and implicit traits-based refinement. In order to evaluate the potential independence of behavior from the object by applying object-orientation to function, we compare it to Aspect-oriented Programming both conceptually and empirically.