A Rationale-based Model for Architecture Design Reasoning

12.3. Future work
umented, but the survey does not allow us to observe the actual capture process that
practising architects go through. We plan to design and execute a large scale field study
into the software design process. This study will consist of multiple case studies, as described
by [174] and successfully demonstrated by [27]. Some of the techniques we plan
to use include studying practitioners’ attitudes towards design rationale use and documentation.
We will conduct in-depth interviews and examination of design specifications.
We expect these experimental techniques will enable us to discover the answers to many
questions regarding the behaviour of architects during design.
Although we can show AREL’s explanatory and reasoning power to help understand
an existing system with the empirical study, we cannot demonstrate AREL’s reasoning
capabilities to support a new system’s development in this thesis. It is our intention to
test this aspect of AREL in the future. In a new experiment, we will apply AREL to the
architecture design process to assess its performance on the effectiveness of capture and
its usefulness in supporting architecture design. The results will allow us to further refine
AREL.
The experts from the empirical study have reacted positively towards the AREL approach,
but they have also questioned the cost-effectiveness of the method. A cost-benefit
analysis on AREL’s applications would be necessary. Different perspectives could be taken
in this analysis: (a) the cost and benefit of using AREL and ARM in different phases of
the software development life-cycle; (b) the cost and benefit of applying each of the techniques
such as traceability, risk analysis using QNR, and impact analysis using BBN; (c)
the cost and benefit of applying these techniques to specific domains or different project
scales.
The AREL tool-set is only a proof-of-concept, its effectiveness in practice requires a
formal evaluation. The tool-set itself also requires better integration between UML modelling
and BBN computation. Design rationale capture using AREL and ARM relies very
much on architects recording the information diligently. Any automated design rationale
capture to enhance this process would be an improvement. Ideas such as language
processing, white board capture tool can be expolored.
In this thesis, we have provided a high-level classification of the architecture elements
by viewpoints. This classification supports the scoping of traceability results. We think
that further classifications are required because it will provide further scoping of the trace
results when using the AREL traceability methods.
The application of BBN to AREL has provided a foundation for change impact analysis.
Some further research opportunities in architecture design and software engineering
are possible: (a) to predict the cost of change impact to architecture enhancements; (b)
211