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Mining Plausible Hypotheses from the Literature Via Meta-Analysis

机译:通过META分析从文献中挖掘合理的假设

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Meta-analysis is highly advocated in many fields of empirical research such as medicine and psychology, due to its capability to synthesize quantitative evidence of effects from the literature, based on statistical analysis. However, the adoption of meta-analysis to software engineering is still suffering from inertia, despite the fact that many software engineering researchers have long been arguing the need for it. As an attempt to move beyond the lockstep, we in this paper explore a different use of meta-analysis. Our proposition is that meta-analysis is useful for mining hypotheses because their plausibility is backed by evidence accumulated in the literature, and thus researchers could focus their effort on the areas that are of particular need. We assess our proposition by conducting a lightweight case study on the literature of defect prediction. We found that three out of five hypotheses we extract from our meta-analysis were indeed investigated in separate papers, indicating the usefulness of our approach. We also recognize two uninvestigated hypotheses whose validity we plan to investigate in the future.
机译:在许多经验研究领域,如医学和心理学的许多领域,荟萃分析,这是由于其基于统计分析,其综合文献的效果的量化证据的能力。然而,尽管许多软件工程研究人员长期以来,但许多软件工程研究人员仍然争论了惯性的惯性。作为超越洛克斯特步骤的尝试,我们在本文中探索了Meta分析的不同使用。我们的命题是,Meta分析对于挖掘假设是有用的,因为它们的合理性被文学中积累的证据支持,因此研究人员可以将他们的努力集中在特殊需要的领域。我们通过对缺陷预测文献进行轻量级案例研究来评估我们的主张。我们发现,我们从荟萃分析中提取的五个假设中的三个确实在单独的论文中进行了调查,表明我们方法的有用性。我们还认识到两个未实现的假设,我们计划在将来调查。

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