Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …

Prior identification of defects in software modules can help testers to allocate limited
resources efficiently. Defect prediction techniques are helpful for this situation because they …

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 …

Software fault prediction (SFP) is typically used to predict faults in software components.
Machine learning techniques (eg, classification) are widely used to tackle this problem. With …

The software engineering field has long focused on creating highquality software despite
limited resources. Detecting defects before the testing stage of software development can …

Software defect prediction (SDP) can help testers decide allocation of resources rationally to
find defects effectively, so as to improve software quality. Naive Bayes (NB) is one of the …

In the arena of software engineering, software defects prediction is one of the most attractive
research topics. Here the main task is to predict if there is any bug in the software or not. For …

Software defect prediction (SDP) plays a key role in the timely delivery of good quality
software product. In the early development phases, it predicts the error-prone modules …

Software defect prediction is aimed to find potential defects based on historical data and
software features. Software features can reflect the characteristics of software modules …

Software defection prediction is not only crucial for improving software quality, but also
helpful for software test effort estimation. As is well-known, 80% of the fault happens in 20 …