Assessment of Software Project Proposal using Analytical Hierarchy Process: A Framework
Bibliographic record
Abstract
Introduction: Application software helps organizations to perform effectively and efficiently in the competitive environment and hence provide value-added services to customers. High significance of application software stimulates organizations to carrying out thorough evaluation of software project proposals that vendors submit with the view of selecting best proposal with optimal performance when implemented. This process entails a number of assessment criteria, multiple conflicting goals, and increasingly turbulent environment. Therefore the need arises for the use of Analytical Hierarchy Process (AHP) for assessment. Aim: This research focused on development of AHP based model for software project proposal assessment and select proposal that guarantees optimal performance when implemented. Materials and Methods: AHP process was divided into 3 phases: Decomposition phase for identification of decision alternatives and evaluation criteria; Measurement of Preference phase for identifying relative importance of criteria using pairwise comparison matrix; and Synthesis phase to establish percentage of relative priorities for ranking proposals and select the best. Results: 64 variables were established and were hierarchically arranged into 4 levels based on degree of preference. It was evident from the priority graph that functionality (35.26%), quality (22.00%) and usability (19.34%) had the higher priority weights, while cost (2.47%) and vendor services (6.26%) had the least. Conclusion: AHP based software project proposal evaluation framework was presented whereby functionality, quality and usability have more consideration than cost elements in the assessment of software projects. Future work attempts to include organizations size, type of business, and experience criteria in the AHP model and implement the framework.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".