Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/221073
Title: Software defect prediction using data mining techniques
Researcher: Lamba, tripti
Guide(s): Kavita and a. K. Mishra
Keywords: Engineering and Technology
University: Jagannath University
Completed Date: 2017
Abstract: The success of any software system entirely depends on the accuracy of the results of the system and whether it is without any flaws. Software defect prediction problems have an extremely beneficial research potential. Software defects are the major issue in any software industry. Software defects not only reduce the software quality, increase costing but it also suspends the development schedule. Software bugs lead to inaccurate and discrepant results. As an outcome of this, the software projects run late, are cancelled or become unreliable after deployment. Quality and reliability are the major challenges faced in a secure software development process. There are major software cost overruns when a software product with bugs in its various components is deployed at client s side. The software warehouse is commonly used as record keeping repository which is mostly required while adding new features or fixing bugs. Many data mining techniques and dataset repository are available to predict the software defects. Bug prediction technique is an important part in software engineering area for last one decade. Software bugs which detect at early stage are simple and inexpensive for rectifying the software. Software quality can be enhanced by using the bug prediction techniques and the software bug can be reduced if applied accurately. Dependent and independent variable are considered in Software bug prediction. To prevent defect based on software metrics software prediction model are used. Metrics based classification categorize component as defective and non-defective. newline
URI: http://hdl.handle.net/10603/221073
Appears in Departments:Faculty of Engineering and Technology

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File129.32 kBAdobe PDFView/Open
02_candidate declaration.pdf129.81 kBAdobe PDFView/Open
03_certificate of the supervisor.pdf132.89 kBAdobe PDFView/Open
04_acknowledgements.pdf84.54 kBAdobe PDFView/Open
05_preface.pdf138.67 kBAdobe PDFView/Open
06_contents.pdf110.42 kBAdobe PDFView/Open
07_lists of tables.pdf105.2 kBAdobe PDFView/Open
08_lists of figures.pdf149.02 kBAdobe PDFView/Open
09_list of abbreviations.pdf92.03 kBAdobe PDFView/Open
10_abstract_tripti lamba.pdf73.18 kBAdobe PDFView/Open
11_chapter 1.pdf213.83 kBAdobe PDFView/Open
12_chapter 2.pdf95.92 kBAdobe PDFView/Open
13_chapter 3.pdf13 kBAdobe PDFView/Open
14_chapter 4.pdf366.18 kBAdobe PDFView/Open
15_chapter 5.pdf1.5 MBAdobe PDFView/Open
16_conclusion.pdf38.28 kBAdobe PDFView/Open
17_references.pdf165.04 kBAdobe PDFView/Open
18_list of publications.pdf144.41 kBAdobe PDFView/Open


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