Simulation-based decision support for bringing a project back on track: the case of RUP-based software construction
Bibliographic record
Abstract
RUP-based development has proven successful in various contexts. The iterative and phased development approach provides a framework for how to develop software efficiently and effectively. Yet, there are plenty of occasions that the projects go off-track in terms of the key parameters of the project such as quality, functionality, cost, and schedule. The challenge for the software project manager is to bring the project back on track. Simulation, in general, and system dynamics based simulation in particular, is established as a method to pro-actively evaluate possible scenarios and decisions. The main contribution of this paper is a method called SIM-DASH; it combines three established techniques for providing decision support to the software project manager in the context of RUP-based development. SIM-DASH consists of (i) a system dynamics modeling and simulation component for RUP-based construction, (ii) dashboard functionality providing aggregated and visualized information for comparing actual versus targeted performance, and (iii) knowledge and experience base describing possible actions that have proven successful in the past for how to bring a project back on track. As part of (iii), decision trees and experience-based guidelines are used. The interplay between these three components provides pre-evaluated actions for bringing the current project iteration back on track. As proof-of-concept, a case study is provided to illustrate the key steps of the method and to highlight its principal advantages. For this purpose, SIM-DASH was substantiated retrospectively for a real-world SAP web system development project within the banking field. While the method is applicable for different issues and scenarios, we study its impact for the specific issue of adding personnel to testing and/or development in order to ensure improved project performance to achieve established quality levels of feature development.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".