Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313391350> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4313391350 endingPage "65" @default.
- W4313391350 startingPage "58" @default.
- W4313391350 abstract "During software development and maintenance, predicting software bugs becomes critical. Defect prediction early in the software development life cycle is an important aspect of the quality assurance process that has received a lot of attention in the previous two decades. Early detection of defective modules in software development can support the development team in efficiently and effectively utilizing available resources to provide high-quality software products in a short amount of time. The machine learning approach, which works by detecting hidden patterns among software features, is an excellent way to identify problematic modules. The software flaws in NASA datasets MC1, MW1, KC3, and PC4 are predicted using multiple machine learning classification algorithms in this work. A new model was developed based on altering the parameters of the previous XGBoost model, including N_estimator, learning rate, max depth, and subsample. The results were compared to those obtained by state-of-the-art models, and our model outperformed them across all datasets." @default.
- W4313391350 created "2023-01-06" @default.
- W4313391350 creator A5010177934 @default.
- W4313391350 creator A5019664223 @default.
- W4313391350 creator A5044548207 @default.
- W4313391350 creator A5060149079 @default.
- W4313391350 creator A5083165183 @default.
- W4313391350 date "2023-01-30" @default.
- W4313391350 modified "2023-09-25" @default.
- W4313391350 title "Prediction of Software Defects using Ensemble Machine Learning Techniques" @default.
- W4313391350 cites W1625712304 @default.
- W4313391350 cites W2148143831 @default.
- W4313391350 cites W2755823703 @default.
- W4313391350 cites W2887331249 @default.
- W4313391350 cites W2911964244 @default.
- W4313391350 cites W2945339223 @default.
- W4313391350 cites W2966280563 @default.
- W4313391350 cites W3048348389 @default.
- W4313391350 cites W3102476541 @default.
- W4313391350 doi "https://doi.org/10.35940/ijrte.e7421.0111523" @default.
- W4313391350 hasPublicationYear "2023" @default.
- W4313391350 type Work @default.
- W4313391350 citedByCount "0" @default.
- W4313391350 crossrefType "journal-article" @default.
- W4313391350 hasAuthorship W4313391350A5010177934 @default.
- W4313391350 hasAuthorship W4313391350A5019664223 @default.
- W4313391350 hasAuthorship W4313391350A5044548207 @default.
- W4313391350 hasAuthorship W4313391350A5060149079 @default.
- W4313391350 hasAuthorship W4313391350A5083165183 @default.
- W4313391350 hasBestOaLocation W43133913501 @default.
- W4313391350 hasConcept C1009929 @default.
- W4313391350 hasConcept C10272871 @default.
- W4313391350 hasConcept C115903868 @default.
- W4313391350 hasConcept C117447612 @default.
- W4313391350 hasConcept C119857082 @default.
- W4313391350 hasConcept C127413603 @default.
- W4313391350 hasConcept C154945302 @default.
- W4313391350 hasConcept C180152950 @default.
- W4313391350 hasConcept C199360897 @default.
- W4313391350 hasConcept C21547014 @default.
- W4313391350 hasConcept C2776969324 @default.
- W4313391350 hasConcept C2777904410 @default.
- W4313391350 hasConcept C41008148 @default.
- W4313391350 hasConcept C42669973 @default.
- W4313391350 hasConcept C45942800 @default.
- W4313391350 hasConcept C48002344 @default.
- W4313391350 hasConcept C529173508 @default.
- W4313391350 hasConcept C82214349 @default.
- W4313391350 hasConcept C98045186 @default.
- W4313391350 hasConceptScore W4313391350C1009929 @default.
- W4313391350 hasConceptScore W4313391350C10272871 @default.
- W4313391350 hasConceptScore W4313391350C115903868 @default.
- W4313391350 hasConceptScore W4313391350C117447612 @default.
- W4313391350 hasConceptScore W4313391350C119857082 @default.
- W4313391350 hasConceptScore W4313391350C127413603 @default.
- W4313391350 hasConceptScore W4313391350C154945302 @default.
- W4313391350 hasConceptScore W4313391350C180152950 @default.
- W4313391350 hasConceptScore W4313391350C199360897 @default.
- W4313391350 hasConceptScore W4313391350C21547014 @default.
- W4313391350 hasConceptScore W4313391350C2776969324 @default.
- W4313391350 hasConceptScore W4313391350C2777904410 @default.
- W4313391350 hasConceptScore W4313391350C41008148 @default.
- W4313391350 hasConceptScore W4313391350C42669973 @default.
- W4313391350 hasConceptScore W4313391350C45942800 @default.
- W4313391350 hasConceptScore W4313391350C48002344 @default.
- W4313391350 hasConceptScore W4313391350C529173508 @default.
- W4313391350 hasConceptScore W4313391350C82214349 @default.
- W4313391350 hasConceptScore W4313391350C98045186 @default.
- W4313391350 hasIssue "5" @default.
- W4313391350 hasLocation W43133913501 @default.
- W4313391350 hasLocation W43133913502 @default.
- W4313391350 hasOpenAccess W4313391350 @default.
- W4313391350 hasPrimaryLocation W43133913501 @default.
- W4313391350 hasRelatedWork W1514039603 @default.
- W4313391350 hasRelatedWork W1539900598 @default.
- W4313391350 hasRelatedWork W2045235365 @default.
- W4313391350 hasRelatedWork W2098915451 @default.
- W4313391350 hasRelatedWork W2117554915 @default.
- W4313391350 hasRelatedWork W2152524519 @default.
- W4313391350 hasRelatedWork W2425606848 @default.
- W4313391350 hasRelatedWork W2744194139 @default.
- W4313391350 hasRelatedWork W4235782973 @default.
- W4313391350 hasRelatedWork W4313391350 @default.
- W4313391350 hasVolume "11" @default.
- W4313391350 isParatext "false" @default.
- W4313391350 isRetracted "false" @default.
- W4313391350 workType "article" @default.