Supporting Quality-Driven Software Design through Intellectual Assistants

Author:

Soria Alvaro1,Diaz-Pace J. Andres2,Bass Len2,Bachmann Felix2,Campo Marcelo1

Affiliation:

1. ISISTAN Research Institute and CONICET, Argentina

2. Software Engineering Institute, USA

Abstract

Software design decisions are usually made at early stages but have far-reaching effects regarding system organization, quality, and cost. When doing design, developers apply their technical knowledge to decide among multiple solutions, seeking a reasonable balance between functional and quality-attribute requirements. Due to the complexity of this exploration, the resulting solutions are often more a matter of developer’s experience than of systematic reasoning. It is argued that AI-based tools can assist developers to search the design space more effectively. In this chapter, the authors take a software design approach driven by quality attributes, and then present two tools that have been specifically developed to support that approach. The first tool is an assistant for exploring architectural models, while the second tool is an assistant for the refinement of architectural models into object-oriented models. Furthermore, the authors show an example of how these design assistants are combined in a tool chain, in order to ensure that the main quality attributes are preserved across the design process.

Publisher

IGI Global

Reference57 articles.

1. Amandi, A., Campo, M., & Zunino, A. (2004). JavaLog: A framework-based integration of Java and Prolog for agent-oriented programming. Computer Languages, Systems and Structures (ex Computer Languages - An International Journal), 31(1), 17-33. Elsevier

2. Anderson, D., Anderson, E., Lesh, N., Marks, J., Mirtich, B., Ratajczack, D., & Ryall, K. (2000). Human-guided simple search. In Proceedings of National Conference on Artificial Intelligence (pp. 209-216). Cambridge, MA: AAAI Press/The MIT Press.

3. Bachmann, F., Bass, L., Bianco, P., & Klein, M. (2007). Using ArchE in the classroom: One experience (Tech. Note CMU/SEI-2007-TN-001). Pittsburgh, PA: Carnegie Mellon University, Software Engineering Institute.

4. Bachmann, F., Bass, L., & Klein, M. (2004). Deriving architectural tactics: A step toward nethodical architectural design (Tech. Rep. CMU/SEI-2003-TR-004). Pittsburgh, PA: Carnegie Mellon University, Software Engineering Institute.

5. Bachmann, F., Bass, L., Klein, M., & Shelton, C. (2004). Experience using an expert system to assist an architect in designing for modifiability. In Proceedings 5th Working IEEE/IFIP Conference on Software Architecture – WICSA’04 (pp. 281-284). Washington, DC: IEEE Computer Society.

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