Employee attrition can become a serious issue because of the impacts on the organization’s competitive advantage. It can become costly for an organization. The cost of employee attrition would be the cost related to the human resources life cycle, lost knowledge, employee morale, and organizational culture. This study aimed to analyze employee attrition using logistic regression. The result obtained can be used by the management to understand what modifications they should perform to the workplace to get most of their workers to stay. The data for the study were around four thousand employees, covering 261 days (one year working days) during 2015 — the data period between January and December. We use R for data integration, exploratory data analysis, data preparation, logistic regression, model evaluation, and visualization. The study has five steps: (1) data collection and business understanding, (2) data pre-processing, (3) exploratory data analysis, (4) model selection and training, and (5) test and evaluation of the model. The result of the study found eleven variables as key driving factors for employee attrition. It also showed that the model has 75 percent accuracy with 73 percent sensitivity and 75 percent specificity.

Software under Test (SUT) is an essential aspect of software testing research activities. Preparation of the SUT is not simple. It requires accuracy, completeness and will affect the quality of the research conducted. Currently, there are several ways to utilize an SUT in software testing research: building an own SUT, utilization of open source to build an SUT, and SUT from the repository utilization. This article discusses the results of SUT identification in many software testing studies. The research is conducted in a systematic literature review (SLR) using the Kitchenham protocol. The review process is carried out on 86 articles published in 2017-2020. The article was selected after two selection stages: the Inclusion and Exclusion Criteria and the quality assessment. The study results show that the trend of using open source is very dominant. Some researchers use open source as the basis for developing SUT, while others use SUT from a repository that provides ready-to-use SUT. In this context, utilization of the SUT from the software infrastructure repository (SIR) and Defect4J are the most significant choice of researchers.