An Empirical Study on the Impact of Refactoring on Quality Metrics in Android Applications
Bibliographic record
Abstract
Mobile applications must continuously evolve, sometimes under such time pressure that poor design or implementation choices are made, which inevitably result in structural software quality problems. Refactoring is the widely-accepted approach to ameliorating such quality problems. While the impact of refactoring on software quality has been widely studied in object-oriented software, its impact is still unclear in the context of mobile apps. This paper reports on the first empirical study that aims to address this gap. We conduct a large empirical study that analyses the evolution history of 300 open-source Android apps exhibiting a total of 42,181 refactoring operations. We analyze the impact of these refactoring operations on 10 common quality metrics using a causal inference method based on the Difference-in-Differences (DiD) model. Our results indicate that when refactoring affects the metrics it generally improves them. In many cases refactoring has no significant impact on the metrics, whereas one metric (LCOM) deteriorates overall as a result of refactoring. These findings provide practical insights into the current practice of refactoring in the context of Android app development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".