A Vision on Intentions in Software Engineering
Bibliographic record
Abstract
Intentions are fundamental in software engineering, but they are typically only implicitly considered through different abstractions, such as requirements, use cases, features, or issues. Specifically, software engineers develop and evolve (i.e., change) a software system based on such abstractions of a stakeholder’s intention—something a stakeholder wants the system to be able to do. Unfortunately, existing abstractions are (inherently) limited when it comes to representing stakeholder intentions and are mostly used for documenting only. So, whether a change in a system fulfills its underlying intention (and only this one) is an essential problem in practice that motivates many research areas (e.g., testing to ensure intended behavior, untangling intentions in commits). We argue that none of the existing abstractions is ideal for capturing intentions and controlling software evolution, which is why intentions are often vague and must be recovered, untangled, or understood in retrospect. In this paper, we reflect on the role of intentions (represented by changes) in software engineering and sketch how improving their management may support developers. Particularly, we argue that continuously managing and controlling intentions as well as their fulfillment has the potential to improve the reasoning about which stakeholder requests have been addressed, avoid misunderstandings, and prevent expensive retrospective analyses. To guide future research for achieving such benefits for researchers and practitioners, we discuss the relationships between different abstractions and intentions, and propose steps towards managing intentions.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.013 |
| 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".