Background: Testing is a crucial activity in software development, but over time, test suites can accumulate test debt-such as flaky, redundant, or overly complex tests. As a subset of technical debt, test debt represents the long-term costs of suboptimal testing practices. Technical debt (TD) refers to the choice of quick solutions in software development instead of more robust and long-term approaches and it has a negative impact on software projects and has attracted significant interest from both industry and academia. With the increasing complexity of modern software, automated testing is essential for maintaining quality, speed, and customer satisfaction. Like other types of technical debt, test debt silently erodes productivity, increases maintenance costs, and reduces reliability in the software development lifecycle. Despite its impact, identifying and managing test debt remains challenging. Developers often face unclear test failures, redundant checks, and a lack of tools to understand root causes or determine what needs to be fixed. Many researchers have been working on leveraging machine learning to better identify and then reduce technical debt in software development. However, these methods often lack transparency-leaving practitioners unsure of how or why decisions are made. This gap between machine learning and actionable insight highlights the need for more interpretable approaches. Aims: This research aims to address this gap by applying explainable artificial intelligence (XAI) to create a tool that helps software teams detect and interpret test debt in automated testing environments. Method: XAI methods improve traceability, trust, and transparency, enabling developers to better comprehend test behaviour. Expected Results: By providing explanations, teams are empowered to have a better understanding and decision-making about test maintenance and improvement. By improving the visibility of the interpretability of test debt, this research contributes to more resilient testing, better-informed software engineers, and more sustainable software systems.

In recent years, software engineers have explored ways to assist quantum software programmers. Our goal in this paper is to continue this exploration and see if quantum software programmers deal with some problems plaguing classical programs. Specifically, we examine whether intermittently failing tests, i.e., flaky tests, affect quantum software development. To explore flakiness, we conduct a preliminary analysis of 14 quantum software repositories. Then, we identify flaky tests and categorize their causes and methods of fixing them. We find flaky tests in 12 out of 14 quantum software repositories. In these 12 repositories, the lower boundary of the percentage of issues related to flaky tests ranges between 0.26% and 1.85% per repository. We identify 46 distinct flaky test reports with 8 groups of causes and 7 common solutions. Further, we notice that quantum programmers are not using some of the recent flaky test countermeasures developed by software engineers. This work may interest practitioners, as it provides useful insight into the resolution of flaky tests in quantum programs. Researchers may also find the paper helpful as it offers quantitative data on flaky tests in quantum software and points to new research opportunities.