A neural net-based approach to software metrics

Software metrics provide an effective method for characterizing software. Metrics have traditionally been composed through the definition of an equation. This approach is limited by the fact that all the interrelationships among all the parameters be fully understood. This paper explores an alternative, neural network approach to modeling metrics. Experiments performed on two widely accepted metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics

Using Neural Networks In Software Metrics

Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach is limited by the fact that all the interrelationships among all the parameters be fully understood. Derivation of a polynomial providing the desired characteristics is a substantial challenge. In this paper instead of using conventional methods for obtaining software metrics, we will try to use a neural network for that purpose. Experiments performed in the past on two widely known metrics, McCabe and Halstead, indicate that this approach is feasible.neural networks, software metrics, halstead, mccabe

Integrating interface slicing into software engineering processes

Interface slicing is a tool which was developed to facilitate software engineering. As previously presented, it was described in terms of its techniques and mechanisms. The integration of interface slicing into specific software engineering activities is considered by discussing a number of potential applications of interface slicing. The applications discussed specifically address the problems, issues, or concerns raised in a previous project. Because a complete interface slicer is still under development, these applications must be phrased in future tenses. Nonetheless, the interface slicing techniques which were presented can be implemented using current compiler and static analysis technology. Whether implemented as a standalone tool or as a module in an integrated development or reverse engineering environment, they require analysis no more complex than that required for current system development environments. By contrast, conventional slicing is a methodology which, while showing much promise and intuitive appeal, has yet to be fully implemented in a production language environment despite 12 years of development

A Neural Net-Based Approach to Software Metrics

Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach is limited by the fact that all the interrelationships among all the parameters be fully understood. Derivation of a polynomial providing the desired characteristics is a substantial challenge. This paper explores an alternative, neural network approach to generating metrics. Experiments performed on two widely known metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics