Employee turnover intention poses challenges for IT organizations, affecting performance and strategic goals. Despite research efforts, the factors driving turnover remain fragmented. This study conducted a systematic literature review of research published between 2015 and 2024, using PRISMA guidelines. A total of 41 studies were analyzed, identifying 29 internal and 25 external factors. Job satisfaction was the most cited internal factor, while organizational commitment was the most frequent external factor. Burnout also emerged as a key driver. These findings support IT organizations in developing targeted HR strategies to improve retention.
In this paper, we propose a method for prioritizing regression test cases based on the probability of detecting software execution failures without source code analysis. To achieve this, our method employs the SentenceBERT model to extract embeddings from textual information of development commits and test scripts. These embeddings are then used by machine learning models to predict the probability of detecting a failure. Our experiments show that the proposed method achieves results equal to or better than those of human experts in 92.52% to 94.24% of scenarios when evaluating the APFD (Average Percentage Faults Detected) metric, an overall gain of 10% in APFD mean and a potential gain of up to 6.03% in test plan prioritization counting cases.
