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Project Description

Project Background

Compositional SPL Technologies

Emergent compositional SPL technologies have been the subject of intensive research in recent years [9], [10]. An example of these technologies is Aspect Oriented Software Development (AOSD) [10]–[12]. AOSD techniques have proven effective to modularize concerns (features) on artefacts such as code, models, and requirements [10]. However, until recently, incipient work on consistency has been proposed in Aspect Oriented Modelling (AOM) [13]. Another prominent approach in this category is Feature Oriented Software Development (FOSD) [1], [14]. FOSD provides formalisms, methods, languages and tools for building variable, customizable and extensible software. FOSD has been successfully used in several case studies [15], [16]. FOSD advocates modularizing features, increments in program functionality [4], as the systems building blocks. At the heart of FOSD is a feature algebra that drives the (de)composition of software artefacts [1], [17], [18]. A feature module contains all the software artefacts, or parts thereof, required for implementing the feature. In other words, feature modules capture the multiple views of a feature. Current realizations of FOSD [9], [19] work under the assumption that either feature artefacts are derived from other artefacts (e.g. via compilation) or are by default manually kept consistent. This assumption may work for the artefact types (source code, make scripts, grammars, equations, XML, and state machines) FOSD has primarily focused on, but it is a limitation as FOSD expands to other activities such as analysis and design that typically employ modelling languages such as UML. Incipient research on modelling and FOSD has been conducted [20]–[23], it works by extending FOSD composition operator to UML models.

Safe Composition

An important trait of SPL is that not all feature combinations yield correct and meaningful software products. Depending on the problem domain, selecting a feature may require the selection of other features; conversely, selecting a feature may exclude or prevent the selection of other features. Feature Models are the de facto mechanism to model the commonality and variability of SPL and there exists a significant body of work on their formal analysis [24], [25]. At a finer granularity, Safe Composition is the guarantee that all programs which are members of a product line are indeed type safe (i.e. absent of references to undefined elements) [26]. It works by including the constraints that composed programs should meet (e.g. single introduction of a class member) in addition to the domain constraints. It is important to stress that Safe Composition eliminates the need to individually check every single program that can be composed, which even for small SPL is impractical. Even though, it focuses on source code, the same principles and techniques could be applied to other artefacts.

Consistency Checking

A crucial demand of MVM systems is consistency checking to describe and preserve the semantic relationships amongst the elements of the different views. Consistency checking works by evaluating consistency rules on models to verify if they meet the semantic constraints. As the size of models increase, so does the time taken to verify them. A recent trend in consistency checking is work on incremental approaches which react to changes and evaluate only those rules and on only those model elements (previously identified by profiling) that can potentially cause an inconsistency. Among those incremental approaches is work by Egyed et al. [27]–[29] and Blanc et al. [30], [31].

Multi-Dimensional Separation of Concerns (MDSoC)

MDSoC argues that stakeholders concerns should be encapsulated across all dimensions (views) simultaneously and subsequently composed to build an entire system [32], [33]. A key novel insight is to consider model changes as increments (decrements) in the functionality expressed in the models. MDSoC can thus be regarded as a foundation of SPL compositional technologies because both propose to build complex multi-dimensional systems by assembling less complex modules in a disciplined, flexible and scalable way. Such modules are increments in functionality of multi-dimensional systems.

Research Objectives

Develop extensions to Safe Composition of diverse artefacts.

Safe Composition research has focused on source code artefacts, however techniques such as that proposed in [26] can be mapped to non-code artefacts. By adding constraints from other artefacts it is possible to consider multiple artefacts simultaneously. We refer to this as Multi-View Safe Composition. We have recently shown how to extend Safe Composition for checking consistency in basic UML models [36], and highlighted its importance and applicability in software architectures [37]. A key issue is if the underlying logic foundations (feature models and SAT solvers [38]) scale when constraints (e.g. written as OCL rules) from multiple artefacts are considered.

Develop techniques that ally compositional SPL approaches with incremental consistency checking.

Compositional SPL works by incrementally adding (composing) feature functionality across multiple artefacts. Incremental consistency checking could be applied in synchronization with feature composition with two potential benefits: improved performance to check consistency, and a lightweight non-formal empirical validation that composition is implemented consistently across all the artefacts kinds.

Devise and apply an assessment plan to evaluate usability and usefulness of the techniques proposed.

For this objectives, several case studies of different sizes and domains will be considered. They will be drawn from examples publicly available from projects websites and those developed by our group and our collaborators. They will be used to evaluate performance, applicability, and usability of the tool support.

Related Bibliography

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