Learning Software Project Management From Analyzing Q&A’s in the Stack Exchange
Bibliographic record
Abstract
Software Project Management (SPM) is considered the key driver for the success or failure of software projects. Project failure is caused by various factors, the most important of which is poor SPM. Thus, we investigated the needs of practitioners by focusing on Project Management Q&A communities. More precisely, we targeted Stack Exchange to identify the primary needs of software project managers. More than 5000 SPM questions were analyzed from the conceptual model given by the Project Management Body of Knowledge PMBOK. For pre-training of the Machine Learning classifiers, we implemented Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec text embedding and compared their performance. Our results showed that BERT outperforms Doc2Vec for pre-training in almost all scenarios. Schedule management, followed by resource management, are the main PMBOK knowledge areas of concern for project managers. Among the process groups, the emphasis of the questions is on planning. We compared the findings with the learning and training status quo in 11 top Canadian universities. We analyzed 46 SPM-related courses and found that the rank correlation of PMBOK knowledge areas is 0.23 between the key content of the analyzed courses and the focus of Q&A’s knowledge areas analyzed from Stack Exchange.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".