Green Software Engineering: Cloud-based Face Detection and Static Code Analysis

Abstract

This paper presents an approach to green software engineering that integrates cloud-based face detection and static code analysis to promote sustainable software development. The proposed method uses OpenCV, a computer vision library, and a pre-trained Haar cascade classifier to detect faces in images. Faces are marked with green bounding frames that serve as visual indicators of their locations. In addition, the paper evaluates the quality of a distinct script file using Pylint library static code analysis techniques. The analysis evaluates code compliance with standards, identifies potential flaws, and identifies code odors. By integrating these practices, the proposed method seeks to reduce resource consumption, maximize energy efficiency, and enhance code maintainability, promoting environmentally friendly and sustainable software engineering practices. One outcome of our effort was creating the YasminNadiaArabcSocialMediaImages data collection, which includes faces of Arabic social media celebrities and is filled out to be accessible for public usage on the websites Kaggle and GitHub.