Fault proneness data available in the early software life cycle from previous releases or similar kind of projects will aid in improving software quality estimations. Various techniques have been proposed in the literature which includes statistical method, machine learning methods, neural network techniques and clustering techniques for the prediction of faulty and non faulty modules in the pr...

Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify ...

Finding security vulnerabilities requires a different mindset than finding general faults in software thinking like an attacker. Therefore, security engineers looking to prioritize security inspection and testing efforts may be better served by a prediction model that indicates security vulnerabilities rather than faults. At the same time, faults and vulnerabilities have commonalities that may ...

The task of software diagnosis algorithms is to identify which software components are faulty, based on the observed behavior of the system. Software diagnosis algorithms have been studied in the Artificial Intelligence community, using a model-based and spectrum-based approaches. In this work we show how software fault prediction algorithms, which have been studied in the software engineering ...

As the cost of software application failures grows and as these failures increasingly impact business performance, software reliability will become progressively more important. Employing effective software reliability engineering techniques to improve product and process reliability would be the industry’s best interests as well as major challenges. As software complexity and software quality ...

The use of the statistical technique of mixture model analysis as a tool for early prediction of fault-prone program modules is investigated. The Expectation-Maximum likelihood (EM) algorithm is engaged to build the model. By only employing software size and complexity metrics, this technique can be used to develop a model for predicting software quality even without the prior knowledge of the ...

Machine learning approaches have been widely used for fault-prone module detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spamfiltering technique. To use our approach in the conventional fault-prone module prediction approaches, we construct a ...

Software quality assurance is necessary to increase the level of confidence in the developed software and reduce the overall cost for developing software projects. The problem addressed in this research is the prediction of fault prone modules using data mining techniques. Predicting fault prone modules allows the software managers to allocate more testing and resources to such modules. This ca...