Roadmap Analysis of Artificial Intelligence Engineering Method
Bibliographic record
Abstract
The characteristics of AI-based software have the potential to reshape traditional software development paradigms.Consequently, this study conducts a systematic literature review (SLR) within the field of AI Engineering to identify the unique challenges in software engineering for AI-based systems, which are transforming traditional software development paradigms.The scope of the SLR includes literature from academic journals and conference proceedings published between 2018 and 2023, selected through a rigorous process.The methodology involved using specific search keywords across databases such as Scopus, ScienceDirect, ACM Digital Library, and IEEE Xplore, with a stringent application of Kitchenham's inclusion and exclusion criteria to ensure a focused and relevant review.This review provides a consolidated summary of diverse research endeavors addressing challenges, issues, and methodologies relevant to AI-based software development.Highlighted topics encompass challenges in requirements engineering for AI-intensive system development, responsible software development (responsible AI), the formulation of a software engineering roadmap for responsible AI, the application of TrustOps as a risk management methodology in AI system development, the necessity of incorporating software engineering methods in AI-based systems, as well as studies exploring requirements engineering practices, AI-intensive system development, and the utilization of tools in machine learning model development.Key findings include the importance of recognizing ethical requirements in AI development, the role of risk management and ethical attributes, and the challenges of connecting requirements between software developers, data scientists, and machine learning specialists.This research provides valuable insights for practitioners and researchers involved in developing AI-based software to overcome existing challenges and apply appropriate methods in the development process.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".