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Comparative Analysis of the Various Data Mining Techniques for Defect Prediction using the NASA MDP Datasets for Better Quality of the Software Product

机译:使用NASA MDP数据集进行缺陷预测的各种数据挖掘技术的比较分析,以提高软件产品的质量

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摘要

Quality of the software product has been the prime area of focus in the past decade in the IT sector and software firms. Not being just able to meet the deliverables on time and possibly quicker time is needed, but also the ability to deliver good quality software product or even better quality at the same time is of utmost importance. The time crunch due to the shorter development and release cycles suppresses the engineer's ability to incorporate enough effort for the quality assurance activities. Hence having a way in order to be able to steer the tester's effort in the right direction is of prime importance. Defect Prediction activities will be able to tell as to where the most probable defects lie in the software product. Various data mining techniques are widely used for the defect prediction process. This paper mainly focuses on the comparison of the various techniques available and an insight as to where exactly to apply what data mining technique using the NASA MDP data sets.
机译:在过去的十年中,软件产品的质量一直是IT部门和软件公司关注的主要领域。不仅需要按时满足交付要求,而且可能需要更快的时间,而且同时交付高质量软件产品甚至更高质量的能力也至关重要。由于较短的开发和发布周期而导致时间紧迫,抑制了工程师为质量保证活动投入足够精力的能力。因此,有一种方法能够引导测试人员朝正确的方向努力是至关重要的。缺陷预测活动将能够确定软件产品中最可能存在的缺陷在哪里。各种数据挖掘技术已广泛用于缺陷预测过程。本文主要着眼于各种可用技术的比较,以及使用NASA MDP数据集确切地将哪种数据挖掘技术应用于何处的见解。

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