TL;DR
This research presents a reuse-aware fault prediction model at the package level for java applications that leverages architectural metrics to capture cohesion, dependency structures, and code reuse patterns and underscores the importance of interpretable AI in guiding quality assurance initiatives.
This study evaluates whether AI-assisted refactoring prioritization can reduce technical debt more effectively than traditional rule-based static analysis, using cyclomatic complexity, maintainability index, and remediation effort as debt indicators.
The analysis indicates that combining predictive defect-risk scores with automated test selection can potentially reduce redundant testing, concentrate computational resources on high-risk software components, and improve feedback speed, but model reliability depends on historical defect data, feature quality, distribu...
Modern software systems operate in increasingly complex and heterogeneous environments, intensifying challenges related to reliability. Automated software testing (AST) helps address these demands, yet existing evidence on how widely used tools support reliability-oriented attributes remains dispersed. A consolidated v...
Existing code analysis systems address individual aspects of software quality (complexity, duplication, style) but do not combine multi-level analysis, metric aggregation, and learning-based inference within a single formal framework, nor do they close the loop between refactoring outcomes and assessment. This paper pr...
AlHAZEN and AVICENNA show that effective debugging tools have tradeoffs between accuracy and interpretability to support developers’ decision-making in increasingly complex software environments.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
