Context and Vision: Studying Two Factors Impacting Program Comprehension
Bibliographic record
Abstract
Linguistic information derived from identifiers and comments has a paramount role in program comprehension. Indeed, very often, program documentation is scarce and when available, it is almost always outdated. Previous research works showed that program comprehension is often solely grounded on identifiers and comments and that, ultimately, it is the quality of comments and identifiers that impact the accuracy and efficiency of program comprehension. Previous works also investigated the factors influencing program comprehension. However, they are limited by the available tools used to establish relations between cognitive processes and program comprehension. The goal of our research work is to foster our understanding of program comprehension by better understanding its implied underlying cognitive processes. We plan to study vision as the fundamental mean used by developers to understand a code in the context of a given program. Vision is indeed the trigger mechanism starting any cognitive process, in particular in program comprehension. We want to provide supporting evidence that context guides the cognitive process toward program comprehension. Therefore, we will perform a series of empirical studies to collect observations related to the use of context and vision in program comprehension. Then, we will propose laws and then derive a theory to explain the observable facts and predict new facts. The theory could be used in future empirical studies and will provide the relation between program comprehension and cognitive processes.
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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 | 0.005 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".