Code smells are poor implementation choices applied by developers during software
evolution that often lead to critical flaws or failure. Much in the same way, community smells …

It is well-known that software defect prediction is one of the most important tasks for software
quality improvement. The use of defect predictors allows test engineers to focus on defective …

With the rise of the Mining Software Repositories (MSR) field, defect datasets extracted from
software repositories play a foundational role in many empirical studies related to software …

Background: Software vulnerabilities are weaknesses in source code that might be exploited
to cause harm or loss. Previous work has proposed a number of automated machine …

Existing defect prediction models use product or process metrics and machine learning
methods to identify defect-prone source code entities. Different classifiers (eg, linear …

Context: Just-in-time software defect prediction (JIT-SDP) aims to conduct defect prediction
on code changes, which have finer granularity. A recent study by Yang et al. has shown that …

Sorting software modules in order of defect count can help testers to focus on software
modules with more defects. One of the most popular methods for sorting modules is …

Defects are common in software systems and cause many problems for software users.
Different methods have been developed to make early prediction about the most likely …

In recent years, many traditional software systems have migrated to cloud computing
platforms and are provided as online services. The service quality matters because system …

Software defect prediction has been one of the key areas of exploration in the domain of
software quality. In this paper, we perform a systematic mapping to analyze all the software …