DynAMICS: A Tool-Based Method for the Specification and Dynamic Detection of Android Behavioral Code Smells
Bibliographic record
Abstract
Code smells are the result of poor design choices within software systems that complexify source code and impede evolution and performance. Therefore, detecting code smells within software systems is an important priority to decrease technical debt. Furthermore, the emergence of mobile applications (apps) has brought new types of Android-specific code smells, which relate to limitations and constraints on resources like memory, performance and energy consumption. Among these Android-specific smells are those that describe inappropriate behaviour during the execution that may negatively impact software quality. Static analysis tools, however, show limitations for detecting these behavioural code smells and properly detecting behavioural code smells requires considering the dynamic behaviour of the apps. To dynamically detect behavioural code smells, we hence propose three contributions : (1) A method, the Dynamicsmethod, a step-by-step method for the specification and dynamic detection of Android behavioural code smells; (2) A tool, the Dynamicstool, implementing this method on seven code smells; and (3) A validation of our approach on 538 apps from F-Droidwith a comparison with the static analysis detection tools,aDoctorand Paprika, from the literature. Our method consists of four steps: (1) the specification of the code smells, (2) the instrumentation of the app, (3) the execution of the apps, and (4) the detection of the behavioural code smells. Our results show that many instances of code smells that cannot be detected with static detection tools are indeed detected with our dynamic approach with an average precision of 92.8% and an average recall of 53.4%.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".