Software defect prediction (SDP) is an effective approach to ensure software reliability. Machine learning models have been widely employed in SDP, but they ignore the impact of class imbalance, noise and outliers on the prediction performance. This study proposes a fuzzy cost broad learning system (FC‐BLS). FC‐BLS not only handles class imbalance problems but also considers the specific sample distribution to address noise and outliers in software defect datasets. Our approach draws fully on the idea of the cost matrix and fuzzy membership functions. It introduces them to BLS, where the cost matrix prioritises the training errors on the minority samples. Hence, the classification hyperplane position is more reasonable, and fuzzy membership functions calculate the membership degree of the sample in a feature mapping space to remove the prediction error caused by noise and outlier samples. Then, the optimisation problem is constructed based on the idea that the minority class and normal instances have relatively high costs. By contrast, the majority class and noise and outlier instances have relatively small costs. This study conducted experiments on nine NASA SDP datasets, and the experimental findings demonstrated the effectiveness of the proposed methodology on most datasets.

Researchers in the microservices community often resort to demonstrating the impact of their proposed advancements on custom-made microservices projects. This is a possible source of bias that can reduce the trustworthiness of the results. Moreover, it is hard to compare advances in small projects, often developed due to lack of time. It is common across disciplines to recognize benchmarks that mitigate bias and unify the advancements' impact. To facilitate the identification of available open-source microservice projects (OSS-MS), we performed a comprehensive study to identify, curate, and catalog OSS-MS. We started with 389559 projects and filtered them down to 3804 projects that we manually labeled. After manual labeling, our dataset contains 378 projects with three or more microservices and with over 100 commits. We document the projects from many perspectives, including project size, platform, number of contributors, project purpose, and foundation support. This dataset can serve researchers as a roadmap to identify benchmarks, as our dataset can be used to answer questions such as whether the number of services impacts the issue count.