In the rapid development of mobile applications, frequent code commits pose significant challenges for quality assurance (QA). Just-in-Time Software Defect Prediction (JIT-SDP) helps at the commit level but often struggles due to insufficient labeled data, particularly in newer applications. To address this issue, we introduce JMFM, a novel approach leveraging Multi-Task Learning (MTL), Fuzzy C-Means (FCM) clustering, and Multi-Head Attention (MHA) for JIT-SDP. JMFM integrates multiple projects for training under the MTL framework, treating each project as a distinct task and enabling cross-project learning. In JMFM, FCM clustering determines the membership of each data sample to various clusters, which is then used in the MHA module to compute the weights of similarity between samples. By integrating the weighted sum of other samples’ data, each sample is augmented with additional information for shared learning. Simultaneously, each project is trained in a task-specific layer to retain its unique features. We calculate the joint loss by giving greater weight to projects with fewer samples to ensure that they are not overshadowed by larger projects. Experiments on 15 Android mobile applications show that JMFM outperforms existing models on metrics such as F1, MCC and AUC especially for projects with scarce data.