Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science
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
To foster a sustainable society within a sustainable environment, we must dramatically reshape our work and consumption activities, most of which are facilitated through software. Yet, most software engineers hardly consider the effects on the sustainability of the IT products and services they deliver. This issue is exacerbated by a lack of methods and tools for this purpose. Despite the practical need for methods and tools that explicitly support consideration of the effects that IT products and services have on the sustainability of their intended environments, such methods and tools remain largely unavailable. Thus, urgent research is needed to understand how to design such tools for the IT community properly. In this article, we describe our experience using design science to create the Sustainability Awareness Framework (SusAF), which supports software engineers in anticipating and mitigating the potential sustainability effects during system development. More specifically, we identify and present the challenges faced during this process. The challenges that we have faced and addressed in the development of the SusAF are likely to be relevant to others who aim to create methods and tools to integrate sustainability analysis into their IT products and services development. Thus, the lessons learned in SusAF development are shared for the benefit of researchers and other professionals who design tools for that end.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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