Investigating the Effect of Aspect-Oriented Refactoring on the Unit Testing Effort of Classes: An Empirical Evaluation
Bibliographic record
Abstract
This paper aims at investigating empirically the effect of aspect-oriented (AO) refactoring on the unit testability of classes in object-oriented software. The unit testability of classes has been addressed from the perspective of the unit testing effort, and particularly from the perspective of the unit test cases (TCs) construction. We investigated, in fact, different research questions: (1) the impact of AO refactoring on source code attributes (size, complexity, coupling, cohesion and inheritance), attributes that are mostly related to the unit testability of classes, (2) the impact of AO refactoring on unit test code attributes (size, assertions, invocations and data creation), attributes that are indicators of the effort involved to write the code of unit TCs, and (3) the relationships between the variations observed after AO refactoring in both source code and unit test code attributes. We used in the study different techniques: correlation analysis, statistical tests and linear regression. We performed an empirical evaluation using data collected from three well-known open source (Java) software systems (JHOTDRAW, HSQLBD and PETSTORE) that have been refactored using AO programming (AspectJ). Results suggest that: (1) overall, the effort involved in the construction of unit TCs of refactored classes has been reduced, (2) the variations of source code attributes have more impact on methods invocation between unit TCs, and finally (3) the variations of unit test code attributes are more influenced by the variation of the complexity of refactored classes compared to the other class attributes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".