A Data-Centric Approach to Evaluate Requirements Engineering in Multidisciplinary Projects
Bibliographic record
Abstract
Multidisciplinary teams are often a necessity for software projects as they provide the required expertise to effectively solve complex problems. However, efficient collaboration between teams with different disciplines is challenging due to several factors such as considering gaps in knowledge areas, establishing a development process, and understanding different requirements from various groups or stakeholders. The agile methodology, such as scrum, offers a powerful approach to managing the software development process effectively. As part of the agile methodology, some techniques and tools are used to manage requirements change, which is a common practice in multidisciplinary teams. This research aims to leverage process-mining techniques to analyze data from Jira and GitHub to analyze the efficacy of software development process, particularly in multidisciplinary teams. This approach is applied to a case study of a virtual healthcare intervention system to measure the team’s productivity. The results indicate several deficiencies in the process with respect to requirements engineering task that cause loss of time and increase rework rates. Results indicate that there are some challenges in the development process that contribute to some deficiencies. The rework rate is high and the number of tasks that are intended to be completed is less than what was planned. These factors can contribute to the lengthening of the software development process. Most of these challenges can be addressed by improving the requirement engineering process in order to obtain the requirements and manage change requests more efficiently.
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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 | 0.029 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".