Comprehensive Integrated Checklists for Requirements Engineering and Software Project Management
Bibliographic record
Abstract
Software engineering processes are often challenged by overlooked tasks and misguided decisions that are simple but yet essential for the success of the system to be developed. A list of guidelines and steps is required to help facilitate software development processes by efficiently guiding engineers and managers at different stages of software development. Moreover, the ensuring of the execution of required tasks is essential to reduce the likelihood of the system failure. In other critical areas such as medical surgery or aerospace control, the use of checklists has proven as a successful practice to ensure the effective and efficient execution of the tasks in a process. Although several of the current practices in software development include using some art of checklists to monitor the software development processes, there are no unified and integrated checklists from research and industry resulting in significant disparities. Such ambiguity could be even counter-productive instead of easing the software development processes. In order to fill this gap, we developed comprehensive and integrated software engineering checklists for the critical areas of requirements engineering and software project management. To create such comprehensive checklists and ensure their completeness, we applied the systematic literature review method. We analyzed 323 documents from academia and industry, and identified a total of 183 requirements engineering checklist items and 263 project management checklist items.
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 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 | 0.142 | 0.291 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.042 | 0.031 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".