Modelling large-scale information systems using ADLs – An industrial experience report

An organisation that had developed a large information system wanted to embark on a programme that would involve large-scale evolution of it. As a precursor to this, it was decided to create a comprehensive architectural description to capture and understand the system’s design. This undertaking faced a number of challenges, including a low general awareness of software modelling and software architecture practices. The approach taken by the software architects tasked with this project included the definition of a simple, very specific, architecture description language. This paper reports our experience of the project and a simple ADL that we created as part of it. 

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.

Representing Variability in Software Architecture

Software Architecture is a high level description of a software intensive system that enables architects to have a better intellectual control over the complete system. It is also used as a communication vehicle among the various system stakeholders. Variability in software-intensive systems is the ability of a software artefact (e.g., a system, subsystem, or component) to be extended, customised, or configured for deployment in a specific context. Although variability in software architecture is recognised as a challenge in multiple domains, there has been no formal consensus on how variability should be captured or represented. In this research, we addressed the problem of representing variability in software architecture through a three phase approach. First, we examined existing literature using the Systematic Literature Review (SLR) methodology, which helped us identify the gaps and challenges within the current body of knowledge. Equipped with the findings from the SLR, a set of design principles have been formulated that are used to introduce variability management capabilities to an existing Architecture Description Language (ADL). The chosen ADL was developed within our research group (ALI) and to which we have had complete access. Finally, we evaluated the new version of the ADL produced using two distinct case studies: one from the Information Systems domain, an Asset Management System (AMS); and another from the embedded systems domain, a Wheel Brake System (WBS). This thesis presents the main findings from the three phases of the research work, including a comprehensive study of the state-of-the-art; the complete specification of an ADL that is focused on managing variability; and the lessons learnt from the evaluation work of two distinct real-life case studies.

Measuring energy footprint of software features

Abstract—With the proliferation of Software systems and the rise of paradigms such the Internet of Things, Cyber- Physical Systems and Smart Cities to name a few, the energy consumed by software applications is emerging as a major concern. Hence, it has become vital that software engineers have a better understanding of the energy consumed by the code they write. At software level, work so far has focused on measuring the energy consumption at function and application level. In this paper, we propose a novel approach to measure energy consumption at a feature level, cross-cutting multiple functions, classes and systems. We argue the importance of such measurement and the new insight it provides to non-traditional stakeholders such as service providers. We then demonstrate, using an experiment, how the measurement can be done with a combination of tools, namely our program slicing tool (PORBS) and energy measurement tool (Jolinar).