| Type of Material: | Thesis |
| Title: | Quantitative Analysis and Design For Software Rework Reduction Using Genetic and Deep Learning Techniques |
| Researcher: | Patchaiammal, P |
| Guide: | Thirumalaiselvi, R |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2022; D14SH509 |
| Fulltext: | Shodhganga |
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| 001 | 455122 | |
| 003 | IN-AhILN | |
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| 035 | __ | |a(IN-AhILN)th_455122 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aPatchaiammal, P|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aQuantitative Analysis and Design For Software Rework Reduction Using Genetic and Deep Learning Techniques |
| 260 | __ | |aChennai|bBharath University, Chennai|c2022 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2022|oD14SH509 |
| 520 | __ | |aHistorical data collection provides knowledge for technical analysis and predicts the fault in the early stage. Maintaining historical data helps to improve development and reduce the rework. Finding new patterns from data forms the structure of algorithms and assists in prediction and feature extraction. Data extraction plays a vital role in all prediction and classification related problems. Data extraction will be successful only by finding the root cause of a fault, and the genetic algorithm makes this process optimal. Fault taxonomy with genetic nature will help to handle similar cases and suggest future actions. This classified fault domain knowledge learns the patterns and identifies the effective strategies for hybridization. Fault prediction is the process of detecting a fault in the software life cycle phases. Various prediction and classification methods establish and evaluate software fault prediction. These approaches provide relatively promising prediction results for different software projec |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aThirumalaiselvi, R|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/397759|yShodhganga |
| 905 | __ | |afromsg |
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