A systematic review of goal-oriented requirements management frameworks for business process compliance
Bibliographic record
Abstract
Legal compliance has been an active topic in Software Engineering and Information Systems for many years. However, business analysts and others recently started exploiting Requirements Engineering techniques, and in particular goal-oriented approaches, to model and reason about legal documents in system design and business process management. Many contributions involve extracting legal requirements, providing law-compliant business processes, as well as managing and maintaining compliance. In this paper, we report on a systematic literature review focusing on goal-oriented legal compliance of business processes. 88 papers were selected out of nearly 800 unique papers extracted from five search engines, with manual additions from the Requirements Engineering Journal and four relevant conferences. We grouped these papers in eight categories based on a set of criteria and then highlight their main contributions. We found that the main areas for contributions have been in extracting legal requirements, modeling them with goal modeling languages, and integrating them with business processes. We identify gaps and opportunities for future work in areas related to prioritization to improve compliance, templates for generating law-compliant processes, general links between legal requirements, goal models, and business processes, and semi-automation of legal compliance and analysis.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".