Effective log anomaly detection can help operators locate and solve problems quickly,
ensure the rapid recovery of the system, and reduce economic losses. However, recent log …

The study aims to identify soft-computing-based software fault prediction models that assist
in resolving issues related to the quality, reliability, and cost of the software projects. It …

During recent years, the enormous increase in demand for software products has been
experienced. High quality software is the major demand of users. Predicting the faults in …

Cross-projects software defect prediction improves the quality of new software projects or
projects with a shortage of historical data. Therefore, various data mining techniques are …

The reliability of software product is seen as critical quality factor that cannot be
overemphasized. Since real world application is loaded with high amount of uncertainty …

Software engineering companies strive to improve software quality by predicting software
defects‐prone modules. Although various data mining methods have been developed …

The objective of this study is to identify a metric suite for software fault prediction that can
solve challenges related to reliability, quality, time, and cost of the software project. This …

The use of machine learning concepts in the software engineering field is now ubiquitous to
predict software defects. Most of the Software Defect Prediction (SDP) datasets are highly …

Finding a software failure prediction model that can address issues with reliability, quality,
timeliness, and cost of the software project is the primary objective of this study. The decision …

In this paper, in-depth analysis of faults in the code phase is detected through integral
methods that identify the error in software. The repositories of the data set are collected …