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@article{144915,
        author = {K. DILEEP REDDY and DR. V.B.NARSIMHA},
        title = {A RANKING MODEL, A FINE GRAINED BENCHMARK, A FEATURE EVALUATION: A SURVEY ON MAPPING BUG REPORTS TO RELEVANT FILES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {2},
        pages = {274-277},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144915},
        abstract = {Once a novel bug report is received, developers generally have to be compelled to be compelled to breed the bug and perform code reviews to hunt out the cause, a way that will be tedious and time overwhelming. A tool for ranking all the provision files with relation to but in all probability they are to contain the rationale for the bug would modify developers to slender down their search and improve productivity. This paper introduces associate degree adaptive ranking approach that leverages project data through purposeful decomposition of computer code computer file, API descriptions of library parts, the bug-fixing history, the code modification history, and so the file dependency graph. Given a bug report, the ranking score of each offer file is computed as a weighted combination of associate degree array of choices, where the weights unit of measurement trained automatically on antecedently solved bug reports using a learning-to-rank technique. We’ve an inclination to worth the ranking system on six huge scale open offer Java comes, exploitation the before-fix version of the project for every bug report. The experimental results show that the learning-to-rank approach outperforms three recent progressive ways.  Specially, our technique makes correct recommendations at intervals the best ten stratified offer files for over seventy p.c of the bug reports at intervals the Eclipse Platform and Felis domesticus comes.},
        keywords = {Bug reports, software maintenance, learning to rank},
        month = {},
        }