Software Test Effort Estimation Using Machine Learning Techniques
| dc.contributor.author | Perera, Miyushi | |
| dc.contributor.author | Vidanagama, VGTN | |
| dc.date.accessioned | 2022-02-25T03:50:40Z | |
| dc.date.available | 2022-02-25T03:50:40Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Software testing is the method of verifying a software product to recognize any errors, gaps, or missing requirements versus the exact requirements. Manual testing and automation testing are the two strategies of software testing. Testing requires a good amount of time and effort in the software development life cycle. The Software Development Life Cycle includes Planning, Designing, Developing, Testing, and Deploying. Software testing is acknowledged as an essential part of the software development life cycle since it concludes whether the software is ready to be delivered. This paper presents several machine learning techniques for test effort estimation. Support Vector Machine (SVM), KNearest Neighbour (KNN), and Linear regression are the techniques considered for the public dataset namely Desharnais. | en_US |
| dc.identifier.citation | Perera Miyushi, Vidanagama VGTN (2021), Software Test Effort Estimation Using Machine Learning Techniques, International Conference on Advances in Computing and Technology (ICACT–2021) Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka 40-41 | en_US |
| dc.identifier.uri | http://repository.kln.ac.lk/handle/123456789/24492 | |
| dc.publisher | Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka | en_US |
| dc.subject | software testing, machine learning,effort estimation | en_US |
| dc.title | Software Test Effort Estimation Using Machine Learning Techniques | en_US |