Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM).

In recent years, the software industry has invested substantial effort to improve software quality in organizations. Applying proactive software defect prediction will help developers and white box testers to find the defects earlier, and this will reduce the time and effort. Traditional software defect prediction models concentrate on traditional features of source code including code complexity, lines of code, etc.

Adaptive model to support business process reengineering.

The one constant in the world is change. The changing dynamics of business environment enforces the organizations to re-design or reengineer their business processes. The main objective of such reengineering processes is to provide services or produce products with the possible lowest cost, shortest time, and best quality.