arbiter
JournalsPricingAccount

Quality evaluation of software engineering professional talent training under the background of new engineering

Lijuan Liu et al.

International Journal of Knowledge-Based Development2025https://doi.org/10.1504/ijkbd.2025.145472article
AJG 1
Weight
0.37

What the paper says

In order to address the issues of low recall rate, long clustering time, and low accuracy in the quality assessment of traditional software engineering talent cultivation methods, a new quality evaluation method of software engineering professional talent training under the background of new engineering is proposed. The intrinsic dependency relationship among the evaluation indicators of software engineering talent cultivation quality is analysed in depth using factor analysis, and a talent cultivation quality assessment indicator system is constructed. Indicator data is collected. The ant colony clustering algorithm is used to cluster the collected data, and the processed data is inputted into the talent cultivation quality assessment model based on fuzzy comprehensive evaluation to obtain relevant assessment results. The experimental results showed that the recall rate of this method is between 95% and 99%, the average clustering time of indicators is 7.75 s, and the maximum accuracy rate is 97%.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijkbd.2025.145472

Or copy a formatted citation

@article{lijuan2025,
  title        = {{Quality evaluation of software engineering professional talent training under the background of new engineering}},
  author       = {Lijuan Liu et al.},
  journal      = {International Journal of Knowledge-Based Development},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijkbd.2025.145472},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Quality evaluation of software engineering professional talent training under the background of new engineering

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.