… used for Software bug prediction [11]. The different classifier is applied with Machine Learning
for … In this paper, ML techniques are utilized with feature selection and K-means clustering …

… objectYoriented software system. Four publicly available pROmISE datasets and four machine
learning algorithms were used in this study. they have used eighteen different metrics and …

… [12] “used a version of the … learning technique for software defect prediction. The research
concluded that trust fitting could be used as a replacement for existing supervised algorithms. …

… the system using machine learning-based software defect prediction techniques. Due to the
dynamic nature of software data collected, Instance-based learning algorithms are proposed …

… This study analyzes the performance of various machine learning classifiers on software
defect prediction by using NASA benchmark datasets. Each dataset includes several features …

… at establishing an improved defect estimation model using additional learning algorithms
and well-identified datasets. In the implementation of our research, we used three different …

… of various machine learning techniques for software defect … software defect is indeed a
major issue in software engineering. Software defect module prediction using different machine …

… The purpose of this study is to analyze and summarize the empirical evidence regarding
the use of machine learning techniques for SDP. The following research questions are …

… evaluation measures will be used to assess software defect prediction accuracy. According
to … This paper evaluated software defect prediction accuracy using different ML algorithms. …

… Software Bugs Prediction In this section, we will discuss different measurements for software
defect prediction … TP denotes the number of defective software instances that are correctly …