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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Software Defect Prediction using Machine Learning Algorithms |
| Authors Name: | Pikki Lovaraju , T Kishore Kumar , V Venkata Gopi , T Rama Kotaiah , T Lalas Maruthi |
| Unique Id: | IJSDR2403108 |
| Published In: | Volume 9 Issue 3, March-2024 |
| Abstract: | Software Defect Prediction [SDP] plays an important role in the active research areas of software engineering. A software defect is an error, bug, flaw, fault, malfunction or mistake in software that causes it to create a wrong or unexpected outcome. The major risk factors related with a software defect which is not detected during the early phase of software development are time, quality, cost, effort and wastage of resources. Defects may occur in any phase of software development. Booming software companies focus concentration on software quality, particularly during the early phase of the software development. Thus, the key objective of any organization is to determine and correct the defects in an early phase of Software Development Life Cycle at testing phase by using machine learning algorithms and JM1 dataset. To improve the quality of software, machine learning techniques have been applied to build predictions regarding the failure of software components by exploiting past data of software components and their defects. This project reviewed the state of art in the field of software defect management and prediction, and offered machine learning techniques. |
| Keywords: | Software Defect Prediction, JM1 Dataset, Machine Learning, Random Forest, Naive Bayes, Decision Tree, Support Vector Machine (SVM), Accuracy, etc. |
| Cite Article: | "Software Defect Prediction using Machine Learning Algorithms ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 3, page no.750 - 754, March-2024, Available :http://www.ijsdr.org/papers/IJSDR2403108.pdf |
| Downloads: | 000337362 |
| Publication Details: | Published Paper ID: IJSDR2403108 Registration ID:210487 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: 750 - 754 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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