Responsible Generative AI for Software Development Life Cycle
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.
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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 | 0.035 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".