… these faulty parts in software before its launch. In this paper, the K- Nearest Neighbor (KNN)
machine learning algorithm is used to predict faulty software projects. Experimental studies …

… We present a method for performing fault localization using similar program spectra. Our …
and no more information from the user than a classification of the runs as either “correct” or “…

… variable, we introduce the classical classification method, K-Nearest Neighbors (KNN), and
the … Finally, we will further study the ways in which faults in deployed software may or may not …

… function, the number of nearest neighbor cases used for fault prediction, and a solution …
to software quality classification. In contrast, this paper presents a CBR-based approach for …

… of the fault's location is the most important part of software flaw … in software is known as
software defect prediction. To defect in software prediction, a machine learning classification …

… When users detect a fault on a software system, they prepare an error-reporting log (what …
how Probabilistic model, k-nearest neighbor classification (for which we experienced that the …

… This matrix contains information regarding both actual and predicted classification collected
using fault prediction technique. Table 9 shows the elements in the confusion matrix for our …

… class imbalance nature of classification dataset. The experimental … results in classifying
the fault-prone software modules. … None of the studies have implemented the neighborhood …

… tend to underperform, as well as linear discriminant analysis and k-nearest neighbor. … in a
software product that causes it to perform unexpectedly.Software defects or software faults are …

… software fault prediction datasets and performance is compared with decision tree classifier,
SVM and its three variations, random forest, KNN, and classification … -pruned classification …