Maintaining feature traceability with embedded annotations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Features are commonly used to describe functional and nonfunctional aspects of software. To effectively evolve and reuse features, their location in software assets has to be known. However, locating features is often difficult given their crosscutting nature. Once implemented, the knowledge about a feature's location quickly deteriorates, requiring expensive recovering of these locations. Manually recording and maintaining traceability information is generally considered expensive and error-prone. In this paper, we argue to the contrary and hypothesize that such information can be effectively embedded into software assets, and that arising costs will be amortized by the benefits of this information later during development. We test this hypothesis in a study where we simulate the development of a product line of cloned/forked projects using a lightweight code annotation approach. We identify annotation evolution patterns and measure the cost and benefit of these annotations. Our results show that not only the cost of adding annotations, but also that of maintaining them is small compared to the actual development cost. Embedding the annotations into assets significantly reduced the maintenance cost because they naturally co-evolve with the assets. Our results also show that a majority of these annotations provides a benefit for feature-related code maintenance tasks, such as feature propagation and migrating clones into a platform.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it