Machine learning Model for Generating Software Engineering Work Ethic’s Course Syllabus Designed for Iraq’s Work Environment

Abstract

In this research paper, Machine learning techniques have been used to discover the highest information value occurs in the contents of the targeted factors that have a greater impact among a group of variables related to work ethics. The objectives of this work, are summarized to improve the work ethics systems, spreading awareness of applying work ethics principles in the education process, develops an innovative Information Model to determine the designing curriculum to integrate ethical concepts and personal motives into formal education and it’s customized to be suitable for Iraqi culture and relevant to its special work environment. To achieve the objectives, the dataset has been collected from software engineering employees; from the government sector and the private sector. The dataset is based on the ACM/IEEE-CS Software Engineering Code of Ethic’s principles against personal motives that include cultural values, personal beliefs, financial incentives, and moral development at the university and/or at the workplace. Several Classifiers have been used to evaluate the worth of an attribute by using a user-specified classifier. Random Tree algorithm has been used to determine the association rules over the selected Code of Ethic’s principles. The obtained results from the generated tree have been used to ensure the prominent variable obtained from the four attribute classifier evaluators. The Correlation coefficient score 0.7385, Mean absolute error score 0.4183, and Root mean squared error score 0.7774. The conducted experiments indicate that properties related to Colleges and Job types have been extracted through machine learning algorithms and business intelligence software, discovered a strong relation effect on changing the level of work ethics within the Iraqi environent in the field of software engineering.