Predicting software energy consumption using time-series based recurrent neural network with Natural Language Processing on Stack Overflow Data

In recent years, there has been an increasing number of software solutions presented to tackle the issue of energy usage at the application level. Nevertheless, there is little knowledge about the level of concern among software developers over energy use, the specific areas of energy consumption that they deem significant, and the potential solutions they propose for enhancing energy efficiency. In order to address this problem, academics and professionals have been investigating several strategies to enhance energy efficiency in computer systems. It may be an interesting project to use deep learning algorithms, especially those that make use of natural language processing (NLP) methods, to estimate software energy usage based on Stack Overflow data. This study examines the concerns of practitioners about energy consumption on Stack Overflow (SO) via the utilisation of Lexicon-based Sentiment Analysis, a concept in Natural Language Processing (NLP), combined with recurrent neural networks. The objective is to improve energy efficiency by forecasting time series data. The results of this study indicate that the practitioners' desire to start conversations in the field of energy is closely linked to the utilisation of ideas. This analysis of software energy consumption issues may assist academics in identifying the most significant concerns for software developers and end users.