An Evolving Neuro-PSO-based Software Maintainability Prediction

An approach to feature-based software construction for enhancing maintainability

While the way we build software affects significantly its maintenance in terms of the effort and cost, the experience level of the maintainer in a software acquirers’ organization is also one of concern. In this context, often the maintainer is the user of the system. Unfortunately, it is quite possible to lose the trustworthiness of the software due to the inexperience of the maintainer, espec...

Hybrid-Based Maintainability Impact Analysis for Evolving Systems

Software maintenance becomes an integral part of software life cycle and constitutes the most important fraction of the total cost of the software lifecycle. Around 50-80 percent of the total lifecycle cost is consumed by maintenance for evolving system. Thus systems with poor maintainability are difficult to modify and require more cost to maintain. This difficulty arises from the impact of th...

Dynamic Evolving Neuro-Fuzzy Inference System for Mortality Prediction

In this paper we propose a dynamic evolving neuro-fuzzy inference system (DENFIS) to forecast mortality. DENFIS is an adaptive intelligent system suitable for dynamic time series prediction. An Evolving Cluster Method (ECM) drives the learning process. The typical fuzzy rules of the neurofuzzy systems are updated during the learning process and adjusted according to the features of the data. Th...

Measuring software maintainability

Simulator for Software Maintainability

According to IEEE Standard Glossary of Software Engineering Terminology: maintainability is the ease with which a software system or component can be modified to correct faults, improve performance or other attributes, or adapt to a changed environment. [1]. Maintainability can also be defined as the probability that a specified maintenance action on a specified item can be successfully perform...