Data-Driven Elicitation and Optimization of Dependencies between Requirements
Bibliographic record
Abstract
Requirement dependencies affect many activities in the software development life cycle such as design, implementation, testing, release planning and change management. They are the basis for various software development decisions. However, requirements dependencies extraction is not only error-prone but also a cognitively and computationally complex problem that consumes substantial efforts, since most of the requirements are documented in natural language. This paper proposes a novel approach to extracts requirements dependencies utilizing natural-language processing (NLP) and weakly supervised learning (WSL) in two stages. In the first stage, binary dependencies (basic dependencies:dependent/independent) are identified, which are further analyzed to detect the type of the dependency in the second stage. An initial evaluation of this approach on the PURE data set - European Rail Traffic Management System - was carried out using three machine learners (Random Forest, Support Vector Machine and Naïve Bayes), which were then compared and tested. Results showed that all the three learners exhibited similar accuracy measures, while SVM needed additional parameter tuning. The machine learners' accuracy was further improved by applying weakly supervised learning to generate pseudo annotations for unlabelled data. Based on these results, agenda is to provide decision support under a dynamic use case scenario that includes (i) continuous updates and analysis of dependencies, (ii) identification of the general types of dependencies, and (iii) dependencies as a key driver of the decision support for the product releases.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".