Microservices enable scalable and flexible software systems but introduce significant testing challenges, particularly at the interaction level between services. Traditional test adequacy criteria, such as branch coverage and traditional mutation operators are unsuitable for evaluating interactions across microservices due to their fine-grained focus and reliance on language-specific, resource-intensive methods. Despite recent advancements in end-to-end coverage metrics and mutation operators, no approach currently targets mutation testing for integration-level tests in microservice systems. This thesis acknowledges the need for a novel mutation testing approach tailored to microservice systems, leveraging a fault taxonomy derived from real-world projects and literature. The proposed tool enables runtime fault injection without requiring system redeployment, addressing the polyglot nature and high deployment costs of microservices. Preliminary results include a validated and published fault taxonomy and a prototype tool. Future work involves further refinement of the tool, comprehensive evaluations, and comparisons with traditional methods to evaluate its effectiveness and efficiency.

Microservices have emerged as a popular architectural paradigm, offering a flexible and scalable approach to software development. However, their distributed nature and diverse technology stacks introduce inherent complexities, surpassing those of monolithic systems. The integration of microservices presents numerous challenges, from communication failures to compatibility issues, compromising system reliability. Understanding faults in these distributed components is crucial for preventing defects, devising test strategies, and implementing robustness testing. Despite the significance of these software systems, existing taxonomies are limited, as they primarily focus on non-functional attributes or lack empirical validation. To address these gaps, this paper proposes an extensive taxonomy of the most common integration-relevant faults observed in large-scale microservice systems in industry. Leveraging insights from a systematic literature review and ten semi-structured interviews with industry experts, we identify common integration-related faults encountered in real-world microservice projects. Our final taxonomy was validated through a survey with an additional set of 16 practitioners, confirming that almost all fault categories (21/23) were experienced by at least 50% of the survey participants.

Over the last few decades, a variety of static code metrics have been published and promoted to measure the maintainability of software systems.This study evaluates 12 common static code metrics for their correlation with observed maintenance efforts. Leveraging modern repository mining techniques, we examine the historical data of three large open-source software systems with a combined size of over 1M LOC and over 10k classes. We automatically identify maintenance activities and measure the effort needed to perform them through revised lines of code. Then, we investigate if the state of the system as captured by these metrics is an indicator for the required maintenance effort.In contrast to earlier research, our results could not validate a general correlation between any of the examined metrics and maintainability. Instead, all evaluated metrics showed positive and negative correlations with maintenance effort depending on the considered time interval. Strong correlations only hold for specific projects, and within these projects, only for limited time spans. Across the project history, however, all metrics showed moderate correlations at most.As no metric was found to be a good indicator for high maintenance efforts in all contexts, we advocate against using any of the evaluated metrics without project-specific validation. If metrics are to be used to monitor the maintainability of a system, either directly or through models based on these metrics, engineers have to validate their applicability not just for the project at hand, but also for the current time span.