Information Systems Analysis and Design: Past Revolutions, Present Challenges, and Future Research Directions
Bibliographic record
Abstract
Systems Analysis and Design (SAND) is undoubtedly a pillar in the field of Information Systems (IS). Some researchers have even claimed that SAND is the field that defines the Information Systems discipline and is the core of information systems. The past decades have seen the development of Structured SAND methodologies and Object-Oriented Methodologies. In the early 1990s, key players in the field collaborated to develop the Unified Modeling Language and the Unified Process. Agile approaches followed, as did other dynamic methods. These approaches remain heavily employed in the development of contemporary information systems. At the same time, new approaches such as DevOps and DevSecOps continue to emerge. This paper curates these trends in SAND. It reviews past and present SAND research, discusses current challenges, and provides insights that can assist SAND researchers in identifying future SAND research streams and important future research directions.
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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 | 0.042 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".