Automating Key Phrase Extraction from Fault Logs to Support Post-Inspection Repair of Software Requirements
Author:
Affiliation:
1. St. Cloud State University, USA
2. Georgia Southern University, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3452383.3452386
Reference41 articles.
1. Using Supervised Learning to Guide the Selection of Software Inspectors in Industry
2. An empirical study of the effect of learning styles on the faults found during the software requirements inspection
3. Using Learning Styles to Staff and Improve Software Inspection Team Performance
4. Using Learning Styles of Software Professionals to Improve their Inspection Team Performance
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Fault-Prone Software Requirements Specification Detection Using Ensemble Learning for Edge/Cloud Applications;Applied Sciences;2023-07-19
2. Classification of Testable and Valuable User Stories by using Supervised Machine Learning Classifiers;2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW);2021-10
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