Prediction of software modules into fault-prone (FP) and not-fault-prone (NFP) categories using software metrics allows prioritization of testing resources to fault-prone modules for achieving higher reliability growth and cost effectiveness. This paper proposes an Artificial Neural Network (ANN) model with use of Sensitivity Analysis (SA-ANN) and Principal Component Analysis (PCA-ANN) for dime...

The installation or deployment of a wireless sensor network (WSN) in a real world application is prone to innumerable failures such as software or hardware malfunctioning, environmental hazards, radio interference, battery exhaustion, etc. In a safety critical application such as landslide prediction, fault tolerant approaches have to be followed to ensure the availability of sensor data at the...

This paper presents the topic of context prediction as one possibility to exploit context histories. It lists some expected benefits of context prediction for certain application areas and discusses the associated issues in terms of accuracy, fault tolerance, unobtrusive operation, user acceptance, problem complexity and privacy. After identifying the challenges in context prediction, a first a...

Accurate prediction of fault prone modules in software development process enables effective discovery and identification of the defects. Such prediction models are especially valuable for the large-scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. This paper presents a methodology for predicting fault prone m...

We explore the software metrics and build a Bayesian Network Model for defect prediction. Much previous work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness, based on the hypnosis that these metrics are independent. But in reality, software metric values are predicted not only correlated with fault-proneness, but also observed internal com...

The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction res...