Coupling and cohesion metrics for knowledge-based systems using frames and rules

Abstract

Software systems and in particular also knowledge-based systems (KBS) become increasingly large and complex. In response to this challenge, software engineering has a long tradition of advocating modularity. This has also heavily influenced object-oriented development. For measuring certain important aspects of modularity, coupling and cohesion metrics have been developed. Metrics have also attracted considerable attention for object-oriented development. For KBS development, however, no such metrics are available yet.This article presents the core of the first metrics suite for KBS development, its coupling and cohesion metrics. These metrics measure modularity in terms of the relations induced between slots of frames through their common references in rules. We show the soundness of these metrics according to theory and report on their usefulness in practice. As a consequence, we propose using our metrics in order to improve KBS development, and developing other important metrics and assessing their theoretical soundness along these lines.

Using structural and textual information to capture feature coupling in object-oriented software

“…Such evolutionary information has been used for impact analysis (Sherriff and Williams 2008), much like coupling metrics. Additionally, coupling metrics have been defined for other applications such as knowledge-based (Kramer and Kaindl 2004) and aspect-oriented (Zhao 2004) systems. …”

The Knowledge Frame System based on Principles of Topological Functioning Model

“…Frame-based representation of declarative and procedural knowledge has a wide application, but the tendency of the last decade was health care and biomedicine (mostly for ontologies of terms) [14], [16], forecasting [16], [22], text/natural language processing [10], [16], [18]- [20], [23]- [29], and structural modelling for intellectual information systems [30].…”

“…Limitations mentioned by authors are inadequate representation of knowledge [23], greater expressiveness that can lead pure ontologies to the loss of information in case of transformation into them [16], [17], [25], necessity to work with the completely known characteristics and static knowledge domain [18], representation of the procedural knowledge as a programming code inside frames [18], and the fact that complex structures can decrease the performance of the system inference and execution [15], [19].…”

Using Relational Topic Models to capture coupling among classes in object-oriented software systems

“…Other coupling metrics have been defined to capture coupling metrics for specific types of software applications, such as knowledge-based [23] and aspect-oriented software systems [39].…”