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Identifier-Based Context-Dependent API Method Recommendation

机译:基于标识符的上下文相关的API方法推荐

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Reuse recommendation systems support the developer by suggesting useful API methods, classes or code snippets based on code edited in the IDE. Existing systems based on structural information, such as type and method usage, are not effective in case of general purpose types such as String. To alleviate this, we propose a recommendation system based on identifiers that utilizes the developer's intention embodied in names of variables, types and methods. We investigate the impact of several variation points of our recommendation algorithm and evaluate the approach for recommending methods from the Java and Eclipse APIs in 9 open source systems. Furthermore, we compare our recommendations to those of a structure-based recommendation system and describe a metric for predicting the expected precision of a recommendation. Our findings indicate that our approach performs significantly better than the structure-based approach.
机译:重用推荐系统通过建议基于IDE中编辑的代码建议有用的API方法,类或代码片段来支持开发人员。 基于结构信息的现有系统,例如类型和方法使用,在诸如字符串的通用类型的情况下无效。 为了缓解这一点,我们提出了一种基于标识符的推荐系统,该标识利用开发人员的意图体现在变量,类型和方法的名称。 我们调查了我们推荐算法的若干变体点的影响,并评估了9个开源系统中Java和Eclipse API的推荐方法的方法。 此外,我们将我们的建议与基于结构的推荐系统的建议进行比较,并描述了预测建议的预期精度的指标。 我们的研究结果表明,我们的方法比基于结构的方法更好地表现得显着。

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