论文标题

使用对象合奏的自动改编来增强软件开发过程

Enhancing Software Development Process Using Automated Adaptation of Object Ensembles

论文作者

Emran, Md., Kabir, Humaun, Rahman, Ziaur, Islam, Nazrul

论文摘要

软件开发一直在迅速变化。这种开发过程可以通过不断变化的开发人员友好的方法来影响。如果我们可以在软件开发过程中自动指导程序员,我们可以节省时间消耗并加速开发过程。有一些方法向开发人员推荐相关的代码片段和APIITEM。某些方法采用常规代码,搜索技术和一些方法采用基于在线的存储库策略。但是,当程序员需要特定类型的转换问题时,很难帮助程序员。更具体地说,当他们想根据他们的期望调整现有界面时。在这种情况下,指导开发人员的熟悉胜利之一是通过自动改编对象合奏来调整收集和阵列。但是,这对未明确指定的实时软件开发的新手开发人员有何帮助?在本文中,我们开发了一个系统,该系统是与特定数据挖掘集成环境(DMIE)集成的插件工具,以推荐相关界面,同时他们寻求类型的转换情况。我们有一个开发的适配器类和相关API的挖掘存储库,从哪里开发人员,搜索他们的查询并使用相关的变压器类获取结果。推荐开发人员标题为“自动目标集合”(AOE插件)的系统。从我们以前进行的调查中,我们可以看到我们的方法比某些现有方法要好得多。

Software development has been changing rapidly. This development process can be influenced through changing developer friendly approaches. We can save time consumption and accelerate the development process if we can automatically guide programmer during software development. There are some approaches that recommended relevant code snippets and APIitems to the developer. Some approaches apply general code, searching techniques and some approaches use an online based repository mining strategies. But it gets quite difficult to help programmers when they need particular type conversion problems. More specifically when they want to adapt existing interfaces according to their expectation. One of the familiar triumph to guide developers in such situation is adapting collections and arrays through automated adaptation of object ensembles. But how does it help to a novice developer in real time software development that is not explicitly specified? In this paper, we have developed a system that works as a plugin-tool integrated with a particular Data Mining Integrated environment (DMIE) to recommend relevant interface while they seek for a type conversion situation. We have a mined repository of respective adapter classes and related APIs from where developer, search their query and get their result using the relevant transformer classes. The system that recommends developers titled automated objective ensembles (AOE plugin).From the investigation as we have ever made, we can see that our approach much better than some of the existing approaches.

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