Furthermore, the results of the best encoding and the best machine learning technique were compared to two traditional approaches that have been widely applied for feature location as well as for traceability link recovery and bug localization. The study also considers whether a domain-independent encoding such as the ones proposed in this work can outperform an encoding that is specifically designed to exploit human experience and domain knowledge. Afterward, the new encodings are used to compare three different machine learning techniques (RankBoost, Feedforward Neural Network, and Recurrent Neural Network). The second proposed encoding is inspired by the characteristics used in benchmark datasets for research on Learning to Rank. The first proposed encoding is an extension of the source encoding to take advantage of not only the main concepts and relations of a domain but also the properties of these concepts and relations. As part of this study, we have provided two new software model encodings and compared them with the source encoding. In this work, we present more thorough research about software model encoding for feature location approaches based on machine learning. However, to apply machine learning techniques optimally, the design of an encoding is essential to be able to identify the best realization of a feature.
In our previous works, we proposed an approach for feature location in models based on machine learning, providing evidence that machine learning techniques can obtain better results than other retrieval techniques for feature location in models. Feature location is one of the main activities performed during software evolution.
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