An intelligent decision support system for effective handling of IT projects
Bibliographic record
Abstract
Software projects are failing for several decades due to multiple reasons. In this regard a lot of research has been done to investigate the reasons behind the failure. However, most of this research was executed in developed countries while under-developed and developing countries got little attention. The main objective of this study is to assess the impact of critical factors on the success of software projects for under-developed countries (like Pakistan), because enterprise environmental factors along with staff working habits, their experience and expertise level also have an impact on the success of a project. To accomplish this, a survey was conducted through the Pakistan Software Export Board (PSEB), and logistic regression enquiry was executed to measure the relationship between various factors affecting software. The results reflect that improper planning along with wrong cost and time estimation are positively and significantly associated with software failure. Based on the finding of the survey, a model is proposed for intelligent decision support system (IDSs). The proposed model keeps track of the previous knowledge and behavior in a well-structured manner that might be helpful for project managers in the estimation and decision-making process of upcoming software projects. This research adds new knowledge from an under-developed country which will open new dimensions for the IT industry and project manager working under similar circumstances.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".