Bibliographic record
Abstract
Background Dependency Injection (DI) is a commonly applied mechanism to decouple classes from their dependencies in order to provide better modularization of software.In the context of Java, the availability of a DI specification and popular frameworks, such as Spring, facilitate DI usage in software projects.However, bad DI implementation practices can have negative consequences, such as increasing coupling, hindering the achievement of DI's main goal.Even though the literature suggests the existence of DI anti-patterns, there is no detailed documentation of such bad practices.Moreover, there is no evidence on their occurrence and perceived usefulness from the developer's point of view.Aims Our goal is to review the reported DI anti-patterns in order to analyze their completeness and to propose and evaluate a novel catalog of Java DI anti-patterns.Method We propose a catalog containing 12 Java DI anti-patterns.We selected 4 opensource and 2 closed-source software projects that adopt a DI framework and developed a tool to statically analyze the occurrence of the candidate DI anti-patterns within their source code.Also, we conducted a survey through face to face interviews with three experienced developers that regularly apply DI.We extended the survey in order to gather the perception of a set of 15 expert and novice developers through an online questionnaire. ResultsAt least 9 different DI anti-patterns appeared frequently in the analyzed projects.In addition, the feedback received from the developers confirmed the relevance of the catalog.Besides, the respondents expressed their willingness to refactor instances of anti-patterns from source code. ConclusionsThe catalog contains Java DI anti-patterns that occur in practice and are useful.Sharing it with practitioners may help them to avoid such anti-patterns.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".