Existing evidence suggests that neural responses to errors were exaggerated in individuals at risk of depression and anxiety. This phenomenon has led to the possibility that the error-related negativity (ERN), a well-known neural correlate of error monitoring could be used as a diagnostic tool for several psychological disorders. However, conflicting evidence between psychopathology and the ERN suggests that this phenomenon is modulated by variables are yet to be identified. Socioeconomic status (SES) could potentially play a role in the relationship between the ERN and psychopathological disorders, given that SES is known to be associated with depression and anxiety. In the current study, we first tested whether SES was related to ERN amplitude. Second, we examined whether the relationship between the ERN and depression was explained by differences in SES. We measured error-related negativity (ERN) from a sample of adult participants from low to high socioeconomic backgrounds while controlling their depression scores. Results show that SES correlated with variations in ERN amplitude. Specifically, we found that low-SES individuals had a larger ERN than wealthier individuals. In addition, the relationship between depression and the ERN was fully accounted for by variations in SES. Overall, our results indicate that SES predicts neural responses to errors. Findings also indicate that the link between depression and ERN may be the result of SES variations. Future research examining the links between psychopathology and error monitoring should control SES differences, and caution is needed if they are to be used as a diagnostic tool in low-income communities. concept" Traditional quality assurance (QA) methods face significant challenges in addressing the complexity, scale, and rapid iteration cycles of modern software systems and are strained by limited resources available, leading to substantial costs associated with poor quality. The object of this research is the Quality Assurance processes for modern distributed software applications. The subject of the research is the assessment of the benefits, challenges, and prospects of integrating modern AI-oriented tools into quality assurance processes. We performed comprehensive analysis of implications on both verification and validation processes covering exploratory test analyses, equivalence partitioning and boundary analyses, metamorphic testing, finding inconsistencies in acceptance criteria (AC), static analyses, test case generation, unit test generation, test suit optimization and assessment, end to end scenario execution. End to end regression of sample enterprise application utilizing AI-agents over generated test scenarios was implemented as a proof of concept highlighting practical use of the study. The results, with only 8.3% flaky executions of generated test cases, indicate significant potential for the proposed approaches. However, the study also identified substantial challenges for practical adoption concerning generation of semantically identical coverage, "black box" nature and lack of explainability from state-of-the-art Large Language Models (LLMs), the tendency to correct mutated test cases to match expected results, underscoring the necessity for thorough verification of both generated artifacts and test execution results. The research demonstrates AI's transformative potential for QA but highlights the importance of a strategic approach to implementing these technologies, considering the identified limitations and the need for developing appropriate verification methodologies.