Bibliographic record
Abstract
Software Evolution is unavoidable because software systems are subject to continuous change, continuing growth and increasing complexity. As software systems become mission-critical and large in size, the complexity in software development is now focused on software evolution rather than construction. In this work, we view a software system as an entity that is evolving throughout its lifetime, during development and maintenance. Based on a broad survey of software evolution approaches, we propose an architecture-based solution for software evolution, which is defined in terms of evolution specific operations on architectural elements, that is, adding, removing, replacing components and (or) connectors, transforming configurations according to the required changes. In our view of software architectures, connectors are more likely to change since they are the architectural elements which reflect business rules. This work is focused on the evolution of connectors in architectures describing detailed design. Coordination contracts are introduced by Fiadeiro et al. as a realization of connectors at this detailed architecture level, which enables a three-layer architecture to separate concerns of components, connectors and configuration during evolution. Furthermore, to constrain the evolution in a predictable direction, we have established a matching scheme for justifying behavioral relationships between coordination contracts by specification matching based on pre- and postconditions of contracts and methods. A number of specification matches, with various degrees of similarity between the evolved and evolving contracts, have been developed for system behaviors after evolution operations. Case studies are exhibited give a better understanding of these matches.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".