Feature location (FL) is performed to find the relationships between domain concepts and other software artifacts. One major problem in maintaining a software system is to understand how many functional features exist in a system and how these features are implemented. Also, poor security is the prime problem in the FL system. However, the existing recent FL techniques use a textual and dynamic approach, which is not found to be secure, keeping in view the changes in the description of security attacks. To overcome this drawback, this work proposed a novel secure approach for FL utilizing data fusion as well as data mining for the internet of things environment. Firstly, the repeated test cases (TC) are eradicated as of the labeled TC. Next, important attributes are selected using the artificial flora optimization algorithm from the removed labeled TC. Then, association rule mining is performed to ascertain closed attributes. Subsequently, encrypt the closed attributes utilizing Caesar Cipher-Rivest, Shamirs, as well as Adelman algorithm. After that, the score value of the closed attributes counts was found utilizing entropy calculation. Finally, the score value is given as input to the normalized-K-Means (N-[K-Means]) algorithm, where the score value is normalized utilizing min-max normalization and then grouped utilizing K-Means algorithm (KMA). It proffers better results for FL in the source code. The proposed N-(K-Means) performance is found better in comparison to the KMA and latent semantic indexing methods. The proposed system proffered better FL results in comparison to the other prevailing methods.

中文翻译：

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一种有效的物联网环境下基于数据融合和数据挖掘的安全特征定位方案

执行特征定位 (FL) 以查找域概念和其他软件工件之间的关系。维护软件系统的一个主要问题是了解系统中存在多少功能特性以及这些特性是如何实现的。此外，安全性差是 FL 系统中的主要问题。然而，现有的最新 FL 技术使用文本和动态方法，发现不安全，同时考虑到安全攻击描述的变化。为了克服这个缺点，这项工作提出了一种新的安全方法，用于 FL，利用数据融合以及物联网环境的数据挖掘。首先，重复测试用例 (TC) 从标记的 TC 开始被根除。下一个，使用人工植物区系优化算法从去除的标记 TC 中选择重要属性。然后，执行关联规则挖掘以确定封闭属性。随后，利用 Caesar Cipher-Rivest、Shamirs 以及 Adelman 算法对封闭属性进行加密。之后，使用熵计算找到封闭属性计数的得分值。最后，得分值作为归一化 K-Means (N-[K-Means]) 算法的输入，其中得分值使用 min-max 归一化进行归一化，然后使用 K-Means 算法 (KMA) 进行分组。它在源代码中为 FL 提供了更好的结果。与 KMA 和潜在语义索引方法相比，提出的 N-(K-Means) 性能更好。