Dynamic analysis of Ada programs for comprehension and quality measurement
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
During maintenance and particularly during corrective and perfective tasks, systems tend to exhibit a weight gain. As a result, their quality tends to degrade. Software comprehension is vital in order to assess system quality. In this paper, we aim at deploying dynamic analysis of Ada programs for obtaining comprehension, and applying measurements to assess their quality. Program instrumentation is performed non-intrusively by AspectAda, an aspect-oriented extension to Ada which we discussed in earlier work. Events which are required for this analysis are captured as execution traces. We have defined a relational database schema to save execution traces, and a set of queries to obtain measures of quality metrics. New Ada-specific metrics are introduced and existing metrics have been adopted from the literature. Automation is also provided as a proof of concept through a prototypical tool which provides information on the run-time behavior of the system, performs measurements and provides visualization of the run-time behavior of the system through a call graph. An open source Ada program is used as a case study to demonstrate our approach.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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