Responsible Generative AI for Software Development Life Cycle
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
Software practitioners are driving a paradigm shift in software engineering practices by integrating generative AI technology into software development and lifecycle management. Integration of generative AI to plan, design, develop, test and maintain software brings productivity gains and enables rapid software releases, however it also presents ethical challenges. This paper examines strategies for developing software through integration of responsible Generative AI that endures, emphasizing primarily the ethical considerations, and the responsible use of Generative technology. It covers the benefits and challenges of collaborative development with responsible Generative AI technologies. The paper focuses on responsible use of generative AI considerations which are likely to induce software integrity and trust. The paper presents best practices, audits, assessments and benchmarking concepts for Gen AI integrated software development and lifecycle management. Subsequently, the paper highlights the importance of safeguarding the integrity of the software development lifecycle through incorporating responsible AI principles, mainly fairness, bias mitigation, privacy and data security, transparency and accountability. Lastly, presenting recommendation for built-in and add-on capabilities for responsible use of GenAI integration into SDLC which paves the way to the trusted ecosystem of GenAI integration for software practitioners.
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 | 0.005 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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