BEYOND INFORMATION SILOS — AN OMNIPRESENT APPROACH TO SOFTWARE EVOLUTION
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
Nowadays, software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant for a particular software maintenance task is typically dispersed over a wide range of artifacts in different representational formats and at different abstraction levels, resulting in isolated 'information silos'. An increasing number of task-specific software tools aim to support developers, but this often results in additional challenges, as not every project member can be familiar with every tool and its applicability for a given problem. Furthermore, historical knowledge about successfully performed modifications is lost, since only the result is recorded in versioning systems, but not how a developer arrived at the solution. In this research, we introduce conceptual models for the software domain that go beyond existing program and tool models, by including maintenance processes and their constituents. The models are supported by a pro-active, ambient, knowledge-based environment that integrates users, tasks, tools, and resources, as well as processes and history-specific information. Given this ambient environment, we demonstrate how maintainers can be supported with contextual guidance during typical maintenance tasks through the use of ontology queries and reasoning services.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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