Context: Ethics have broad applications in different fields of study and different contexts. Like other fields of study, ethics have a significant impact on the decisions made in computing concerning software artifact production and its processes. Hence, in this research, ethics is considered in the context of requirements engineering during the software development process. Objective: The aim of this paper is to discuss the investigation results regarding ethical problems of requirements engineering processes by taking sample software developing companies and exposing existing research gaps. Method: This research uses interviewing, focus group discussions, purposive sampling, and qualitative analysis research methods. Result: This research finds an absence of industry practices, professional responsibility code of conduct standards, and other guidelines within companies when integrating ethical concerns of software during requirements engineering. It also indicates that almost all companies have no identification methods and checking mechanisms for ethical concern considerations. Furthermore, the major identified ethical concerns are classified into six categories as requirements identification problems, quality-related problems, carrying out unpermitted activities, unwillingness to give requirements, knowledge gaps and lack of legal grounds/rules for accountability. Conclusion: From the findings of this research, it can be concluded that, in the case software companies, there is no specific method for identifying ethical concerns. Additionally, there are no standards and guidelines used within the companies. This implies the need to overcome the existing and emerging ethical issues of requirements engineering.

The usage of credit cards is increasing daily for online transactions to buy and sell goods, and this has also increased the frequency of online credit card fraud. Credit card fraud has become a serious issue for financial institutions over the last decades. Recent research has developed a machine learning (ML)-based credit card fraud transaction system, but due to the high dimensionality of the feature vector and the issue of class imbalance in any credit card dataset, there is a need to adopt optimization techniques. In this paper, a new methodology has been proposed for detecting credit card fraud (financial fraud) that is a hybridization of the firefly bio-inspired optimization algorithm and a support vector machine (called FFSVM), which comprises two sequential levels. In the first level, the firefly algorithm (FFA) and the CfsSubsetEval feature section method have been applied to optimize the subset of features, while in the second level, the support vector machine classifier has been used to build the training model for the detection of credit card fraud cases. Furthermore, a comparative study has been performed between the proposed approach and the existing techniques. The proposed approach has achieved an accuracy of 85.65% and successfully classified 591 transactions, which is far better than the existing techniques. The proposed approach has enhanced classification accuracy, reduced incorrect classification of credit card transactions, and reduced misclassification costs. The evaluation results show that the proposed FFSVM method outperforms other nonoptimization machine learning techniques.

Requirements engineering is a fundamental process in software development phases. At the same time, it is a difficult phase and exposed many ethical violations. The main purpose is proposing an ethical framework for software requirements engineering that addresses the identified concerns. These concerns include problems associated with a knowledge gap, requirements identification, quality-related concerns, unwillingness to give requirements, and practicing forbidden activities. These concerns are grouped into a category as the proposed framework components. Each of the categories encompasses more than one problem domain. The proposed framework suggests resolving mechanisms as collections of clauses for each of those concerns. An expert evaluation technique is used to validate the proposed framework. The experts are purposefully selected from software industries and institutions. Questionnaires and focus group discussions were used as data-gathering tools for the validation of the proposed framework. The validity (face validity, content validity, and construct validity) and the reliability of the proposed framework were checked. The evaluation results show that the proposed framework has an acceptable range of validity and reliability. The proposed framework can be used as a guideline for software engineers to minimise the occurrence of those identified concerns during the requirements engineering process.