Software Defect Prediction using Machine Learning Algorithms
Pikki Lovaraju
, T Kishore Kumar , V Venkata Gopi , T Rama Kotaiah , T Lalas Maruthi
Software Defect Prediction, JM1 Dataset, Machine Learning, Random Forest, Naive Bayes, Decision Tree, Support Vector Machine (SVM), Accuracy, etc.
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.
"Software Defect Prediction using Machine Learning Algorithms ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 3, page no.750 - 754, March-2024, Available :https://ijsdr.org/papers/IJSDR2403108.pdf
Volume 9
Issue 3,
March-2024
Pages : 750 - 754
Paper Reg. ID: IJSDR_210487
Published Paper Id: IJSDR2403108
Downloads: 000347089
Research Area: Information Technology
Country: Bapatla , Andhra Pradesh , India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
