An Intelligent Methodology to Enhance Requirements Engineering in Multidisciplinary Projects
Bibliographic record
Abstract
The multidisciplinary nature of a team has been identified as one of the success criteria of both user-centric approaches and agile methods. Stakeholder involvement is considered to be essential for agile processes in order to meet project objectives and ensure results are aligned with stakeholder expectations. However, establishing a collaborative process involving designers, programmers, stakeholders, and users can be challenging; particularly during the requirements engineering stage. Agile methodologies, such as scrum, offer a powerful way of effectively managing software projects and generates a great deal of useful data through tools such as Jira and GitHub. The aim of this research is two folds: 1) analyze the project data from aforementioned sources using process mining techniques to discover deficiencies in the software development process, and 2) propose an automated effort estimation process to address the identified challenges in this study and provide decision support in the development process. This approach is applied to a case study of a virtual healthcare intervention system. The results are indicative that these enhancements helped boost the decision-making and release planning processes by providing the development team a more clear picture, which ultimately mitigated the number of change requests.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".