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Full-Text Articles in Physical Sciences and Mathematics
Concernlines: A Timeline View Of Co-Occurring Concerns, Christoph Treude, Margaret-Anne Storey
Concernlines: A Timeline View Of Co-Occurring Concerns, Christoph Treude, Margaret-Anne Storey
Research Collection School Of Computing and Information Systems
Understanding the evolution of a software system requires understanding how information about the release history, non-functional requirements and project milestones relates to functional requirements on the software components. This short paper describes a new tool, called CONCERNLINES, that supports this cognitive process by visualizing co-occurring concerns over time.
Multi-Level Modeling Of Software On Hardware In Concurrent Computation, Joann M. Paul, Arne Suppe, Henele I. Adams, Donald E. Thomas
Multi-Level Modeling Of Software On Hardware In Concurrent Computation, Joann M. Paul, Arne Suppe, Henele I. Adams, Donald E. Thomas
Research Collection School Of Computing and Information Systems
The fundamental modeling differences between hardware and software modeling can be thought of as reasoning about connectedness vs. reasoning about interleaved (shared) access to resources. A natural design hierarchy for physical systems is component-based because of the existence of a consistent basis for interconnect between design levels. However, performance modeling and design of concurrent, programmable systems require new ways of thinking about what it means to abstract detail, add detail and partition a model of software executing on hardware. We motivate frequency interleaving (FI) as a common simulation foundation for these systems because it resolves flow and partitioning with software …
Connectionist Expert System With Adaptive Learning Capability, B. T. Low, Hochung Lui, Ah-Hwee Tan, Hoonheng Teh
Connectionist Expert System With Adaptive Learning Capability, B. T. Low, Hochung Lui, Ah-Hwee Tan, Hoonheng Teh
Research Collection School Of Computing and Information Systems
A neural network expert system called adaptive connectionist expert system (ACES) which will learn adaptively from past experience is described. ACES is based on the neural logic network, which is capable of doing both pattern processing and logical inferencing. The authors discuss two strategies, pattern matching ACES and rule inferencing ACES. The pattern matching ACES makes use of past examples to construct its neural logic network and fine-tunes itself adaptively during its use by further examples supplied. The rule inferencing ACES conceptualizes new rules based on the frequencies of use on the rule-based neural logic network. A new rule could …