A design-rule-based constructive approach to building traceable software
Bibliographic record
Abstract
The maintenance of large-scale software systems without trace information between development artifacts is a challenging task. This thesis focuses on the problem of supporting software maintenance through a mechanism for establishing traceability relations between the system requirements and its code elements. The core of the proposed solution is a set of design rules that regulates the positional (e.g., package), structural (e.g., class), and behavioral (e.g., method) aspects of the system elements, thus establishing traceability between requirements and code. We identify several types of requirements each of which can be supported by design rules. We introduce a rule-based approach to software construction and demonstrate that such a process can support maintainability through two mechanisms: (a) traceability and (b) reduction of defect rate. We distinguish our work from traditional traceability approaches in that we regard traceability as an intrinsic structural property of software systems. This view of traceability is in contrast to traditional traceability approaches where traceability is achieved extrinsically through creating maps such as the traceability matrices or allocation tables. The approach presented in this thesis has been evaluated through conducting several empirical studies as well as building a proof-of-concept system. The results we obtained demonstrate the effectiveness and usefulness of our approach.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".