Sentence examples for defect predictions from inspiring English sources

In the paper we also evaluate how information about defect inflow distribution from historical projects is applied for modeling the prior beliefs/experience in Bayesian analysis which is useful for making software defect predictions early during the software project lifecycle.

They showed that naive Bayes machine learning methods outperform rule-based or decision tree learning methods and they showed, on the other hand, that the choice of learning methods used for defect predictions can be much more important than used attributes.

Human Splicing Finder (HSF, http://www.umd.be/HSF/) outputs splicing defect predictions based on the PWM and MED models as well as the predictions of branch points, ESEs, and ESSs.

Software defect prediction predicts fault-prone modules which will be tested thoroughly.

Without sufficient local data, defects can be predicted via cross-project defect prediction (CPDP) utilizing data from other projects to build a classifier.

Minimizing these adverse effects using software defect prediction models via guiding testers with defective parts of software system is an attractive research area.

While there has been many research efforts on discovering more predictive software metrics and more effective defect prediction algorithms, the cost-effectiveness of the QA resource allocation approaches has always been evaluated by counting the number of defects per selected artifact.

Burak et al. [26] analyzed 25 projects of the largest GSM operator in Turkey, Turkcell to predict defect before the testing phase, they used a defect prediction model that is based on static code attributes like lines of code, Halstead and McCabe.

This is known as within-project defect prediction (WPDP).

This is otherwise known as cross-project defect prediction (CPDP).

Nam and Kim [80] proposed an HTL method called Heterogeneous Defect Prediction (HDP) for software defect prediction, though it can also be applied for other tasks.