Malhotra et al., 2014 - Google Patents

An empirical comparison of machine learning techniques for software defect prediction

Malhotra et al., 2014

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Document ID
4844641910405866447
Author
Malhotra R
Raje R
Publication year
Publication venue
Proceedings of the 8th International Conference on Bioinspired Information and Communications Technologies

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Snippet

Software systems are exposed to various types of defects. The timely identification of defective classes is essential in early phases of software development to reduce the cost of testing the software. This will guide the software practitioners and researchers for planning …
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    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30289—Database design, administration or maintenance
    • G06F17/30303—Improving data quality; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0766—Error or fault reporting or storing
    • G06F11/0775—Content or structure details of the error report, e.g. specific table structure, specific error fields
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    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
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