Investigating the Predictability of Empirical Software Failure Data with Artificial Neural Networks and Hybrid Models
Andreas S. Andreou, Alexandros Koutsimpelas · 2006
Software failure and software reliability are strongly related concepts. Introducing a model that would perform successful failure prediction could provide the means for achieving higher software reliability and quality. In this context, we have employed artificial neural networks and genetic algorithms to investigate whether software failure can be accurately modeled and forecasted based on empirical data of real systems. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.