Separating features in source code: an exploratory study
Bibliographic record
Abstract
Most software system codebases are inflexible. Reconfiguring the software modules comprising a system to add or to delete a feature typically requires substantial effort. This lack of flexibility increases the costs of building variants of a system, amongst other problems. New languages and tools that are being developed to provide additional support for separating concerns in a system show promise to help address this problem. However, applying these mechanisms requires determining how to enable a feature to be separated from the codebase. In this paper, we investigate this problem through an exploratory study conducted in the context of two existing systems: gnu.regexp and jFTPd. The study consisted of applying three different separation of concern mechanisms---Hyper/J TM , AspectJ TM , and a lightweight, lexically-based approach---to separate features in the two packages. In this paper, we report on the study, providing contributions in two areas. First, we characterize the effect different mechanisms have on the structure of the codebase. Second, we characterize the restructuring process required to perform the separations. These characterizations can help software engineering researchers elucidate the design space of using these mechanisms, tool developers design support to aid the separation process, and early adopters apply the techniques. Keywords design space, feature separation, aspect-oriented programming, hyperspaces 1
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".