The Assessment of Importance of Selected Issues of Software Engineering, IT Project Management, and Programming Paradigms Based on Graphical AHP and Fuzzy C-Means
Bibliographic record
Abstract
In this study, we present the results of surveys conducted in a group of employees and students of IT faculties presenting the answers to the most important, in our opinion, issues related to software engineering (SE), IT project management, and programming paradigms. The above topics are chosen because of their high relevance to the professional community. The participants taking part in the experiments quantified their input through the process of pairwise comparisons (a so-called Analytic Hierarchy Process, AHP) using an innovative highly interactive approach based on a graphic communication means. The generic AHP method was augmented by the optimization mechanisms delivered by the Particle Swarm Optimization (PSO) in order to deliver the highest possible consistency of responses of the participants. Moreover, we demonstrate a method based on Fuzzy C-Means (FCM) filtering highly inconsistent and unreal experts' assessments. In a series of experiments, we demonstrate the accuracy and stability of the AHP method based on graphical environment. We discuss two variants of aggregation of experts' opinions according to their level of experience in the field of interest. Finally, we show the efficiency of the FCM as the method of preselection of experts' evaluations.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".