Architecture style selection using statistics of quality attributes to reduce production costs

Abstract EN

As time goes by and software systems grow in complexity and size, there is an increasing need for software architecture as an important tool in software design.

Designing an appropriate architecture is necessary in producing a high-quality software, which also suits stakeholders.

In order to design the desired high-quality software program, style-based architectures can be used.

That is, with the selection of appropriate style architecture, we will get an ideal architecture for design.

With the same attitude in this research, using a statistical computational algorithm, we have attempted to select the appropriate software architecture style to meet stakeholders’ requirements.

In meeting Non-Functional Requirements (NFRs) of stakeholders, increase of one NFR does not increase the others necessarily, and they may be at odds with each other, thus the best quality for all cannot be achieved.

In the designing stage of an ideal software, we must take into account the production and maintenance costs as well as a trade-off between stakeholders’ desired needs.

The proposed algorithm structure involves a method using Gamma Probability Distribution Function (PDF).

In a way that, a statistical estimate for each present style is created, and finally in the design of the software, the best style (based on the mentioned statistical estimate) is used for meeting the stakeholder’s needs.

The method not only creates NFRs in the software program, but also gives importance to production and maintenance costs.

This requires that the qualitative data of the problem be converted into quantitative data.

It will be fully described in the introduction to the algorithm.

In order to verify the validity of the proposed algorithm, the resulted architecture style ranking will be compared with the results of alternative methods namely Analytic Hierarchy Process (AHP) and A Lightweight Value-based Software Architecture Evaluation (LiVASAE).

The results confirm the applicability of the proposed algorithm and moreover it has less time complexity with respect to other methods.