Software engineering economics
Bibliographic record
Abstract
The field of software economics seeks to develop technical theories, guidelines, and practices of software development based on sound, established, and emerging models of value and value-creation---adapted to the domain of software development as necessary. The premise of the field is that software development is an ongoing investment activity---in which developers and managers continually make investment decisions requiring the expenditure of valuable resources, such as time, talent, and money. The overriding aim of this activity is to maximize the value added subject to an equitable distribution among the participating stakeholders. The goal of the tutorial is to expose the audience to this line of thinking and introduce the tools pertinent to its pursuit. The tutorial is designed to be self-contained and will cover concepts from introductory to advanced. Both practitioners and researchers with an interest in the impact of value considerations in software decision-making will benefit from attending it.This tutorial is offered in conjunction with the Fourth International Workshop on Economics-Driven Software Engineering Research (EDSER-4). The tutorial is meant in part to enable those who would like to participate in the workshop, but who might not possess the requisite background, to come up to speed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".