Defect prediction on projects with limited historical data has attracted great interest from both
researchers and practitioners. Cross-project defect prediction has been the main area of …

Unlike traditional defect prediction models that identify defect-prone modules, Just-In-Time
(JIT) defect prediction models identify defect-inducing changes. As such, JIT defect models …

Abstract Cross-Company Churn Prediction (CCCP) is a domain of research where one
company (target) is lacking enough data and can use data from another company (source) …

To predict files with defects, a suitable prediction model must be built for a software project
from either itself (within-project) or other projects (cross-project). A universal defect …

Software defects can lead to undesired results. Correcting defects costs 50% to 75% of the
total software development budgets. To predict defective files, a prediction model must be …

Defect prediction models help software organizations to anticipate where defects will appear
in the future. When training a defect prediction model, historical defect data is often mined …

Establishing credible thresholds is a central challenge for promoting source code metrics as
an effective instrument to control the internal quality of software systems. To address this …

Modeling languages, just as all software artifacts, evolve. This poses the risk that legacy
models of a company get lost, when they become incompatible with the new language …

Code smells are archetypes of design shortcomings in the code that can potentially cause
problems during maintenance. One known approach for detecting code smells is via …

Model-driven software development comes in different styles. While standard-based
approaches leverage existing language standards (eg UML), tooling, and even …