Aug 2026· 2026 6th International Conference on Emerging Smart Technologies and Applications (eSmarTA)· pp. 1-7· 0 citations· 42 references
Abstract
Refactoring is widely used to improve internal software quality; however, its impact on external functionality remains insufficiently explored. This study investigates how different refactoring operations influence software functionality through a controlled experimental analysis. A set of ten commonly used refactoring operations was applied to the jEdit system. Software functionality was quantitatively evaluated using a composite metric derived from cohesion, polymorphism, interface size, design size, and inheritance hierarchy. The selected ten refactoring operations were performed 453 times across ten independent experiments in jEdit. The results reveal that refactoring operations do not have uniform effects on functionality. Operations that enhance encapsulation and modular distribution significantly improve functionality, with Encapsulate Field achieving the highest increase. In contrast, operations that reduce abstraction, such as Inline Method and Inline Class, negatively impact functionality. Additionally, some operations show no measurable effect, indicating limitations in metric sensitivity. These findings demonstrate that refactoring should not be assumed to universally improve software functionality. Instead, its impact depends on the nature and context of the operations being applied. The study provides empirical evidence and practical guidance for selecting refactoring operations that effectively enhance functional quality while avoiding potential degradation.
This work proposes an approach based on a foundation model oracle that analyzes git-style diffs to identify behavioral changes introduced by Python refactorings and uncovered 13 distinct bugs among the seven refactoring types studied.
Jonhnanthan Oliveira, Rohit Gheyi, Márcio Ribeiro et al.· 0 citations
This paper operationalizes structural testability as a seven-dimensional construct capturing controllability, observability, branching complexity, asynchronous coordination, event-driven behaviour, encapsulation, and side-effect intensity and applies this framework to 30 open-source JavaScript projects spanning diverse domains and sizes.
Shahrzad Mirzaei, Saba Alimadadi· arXiv.org· 0 citations
Results indicate that static checks and test-guided, context-aware agentic repair can increase the reliability of LLM-generated refactorings, bringing them closer to practical integration within developer workflows.
Jonathan Cordeiro, Shayan Noei, Ying Zou· 1 citation
Refactoring improves software maintainability while preserving functional behavior, yet behavior preservation does not imply energy neutrality. Existing studies primarily examine isolated refactorings under fixed or simple workloads, leaving the effects of workload variation, real-world refactoring practices, explanatory factors, and energy regression identification insufficiently understood. We present the first large-scale empirical study of the energy impact of refactoring across two complementary Java benchmarks: a Micro-benchmark, comprising 68 refactoring types evaluated under diverse workloads, and a Practical-benchmark, containing 481 real-world refactoring commits from 430 GitHub projects. Using repeated paired energy measurements, we analyze workload sensitivity, refactoring patterns, explanatory factors, and the effectiveness of metric- and LLM-based regression identification. In the Micro-benchmark, 199 of 384 refactoring-workload pairs (51.8%) exhibit statistically significant energy differences, and 45.3% of refactoring instances change energy-impact classification across workloads. In the Practical-benchmark, only 36 commits (7.5%) show significant energy changes, although two-thirds differ by at least 10%. Refactoring type alone is insufficient to predict energy outcomes, while certain recurring refactoring combinations are associated with energy reductions. Changes in execution time consistently explain energy variation in the controlled benchmark but correlate weakly with energy changes in real-world commits. Our findings highlight the need for workload-diverse evaluation of the energy impact of refactoring; neither existing metric-based approaches nor LLM-based predictors can reliably identify refactoring-induced energy regressions, motivating the development of more accurate techniques for predicting the energy impact of refactoring.
Modern software development involves parallel work and concurrent changes, requiring code merging. Prior studies report that 10% to 20% of merge attempts result in conflicts, often requiring manual intervention. The literature explores factors that generate conflicts, including refactorings, but does not analyze how individual refactoring types influence the manual effort required to resolve them. We analyzed 64 open-source Java projects and applied association rule mining to measure the strength of associations between specific refactoring types and merge effort. Our results show that refactoring types relate to merge effort with varying strength. In particular, Rename Attribute, Move Class, Extract Variable, Change Return Type, and Split Parameter exhibit some of the strongest associations, especially when a higher number of such refactorings is present in the merge branches. We also find that both the number of refactorings and their diversity independently increase merge effort, both in terms of occurrence and intensity. Additionally, the co-occurrence of refactorings across parallel branches is associated with higher merge effort, particularly when combining structural transformations with changes to method signatures and data-structure representations, whereas more localized changes are less frequent in the most impactful combinations.
A. Oliveira, João Victor Monteiro, V. Neves et al.· 0 citations
Results show that ML-enhanced recommendations outperform traditional methods in accuracy, relevance, and impact on maintainability metrics, and highlight the potential of integrating ML into modern development practices to support developers in producing cleaner, more maintainable software systems.
Rohit Malhotra· International Journal of Mod...· 0 citations
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