Federated learning for software engineering: A case study of code clone detection and defect prediction

• Main Authors: YANG, Yanming, HU, Xing, GAO, Zhipeng, CHEN, Jinfu, NI, Chao, XIA, Xin, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Benchmark testing; Cloning; Code clone detection; Codes; Data models; Defect prediction; Federated learning; Parameter aggregation strategy; Skewed data distribution; Task analysis; Training; Numerical Analysis and Scientific Computing; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8632
• Institution: Singapore Management University

In various research domains, artificial intelligence (AI) has gained significant prominence, leading to the development of numerous learning-based models in research laboratories, which are evaluated using benchmark datasets. While the models proposed in previous studies may demonstrate satisfactory performance on benchmark datasets, translating academic findings into practical applications for industry practitioners presents challenges. This can entail either the direct adoption of trained academic models into industrial applications, leading to a performance decrease, or retraining models with industrial data, a task often hindered by insufficient data instances or skewed data distributions. Real-world industrial data is typically significantly more intricate than benchmark datasets, frequently exhibiting data-skewing issues, such as label distribution skews and quantity skews. Furthermore, accessing industrial data, particularly source code, can prove challenging for Software Engineering (SE) researchers due to privacy policies. This limitation hinders SE researchers’ ability to gain insights into industry developers’ concerns and subsequently enhance their proposed models. To bridge the divide between academic models and industrial applications, we introduce a federated learning (FL)-based framework called A lmity . Our aim is to simplify the process of implementing research findings into practical use for both SE researchers and industry developers. A lmity enhances model performance on sensitive skewed data distributions while ensuring data privacy and security. It introduces an innovative aggregation strategy that takes into account three key attributes: data scale, data balance, and minority class learnability. This strategy is employed to refine model parameters, thereby enhancing model performance on sensitive skewed datasets. In our evaluation, we employ two well-established SE tasks, i.e., code clone detection and defect prediction, as evaluation tasks. We compare the performance of Almity on both machine learning (ML) and deep learning (DL) models against two mainstream training methods, specifically the Centralized Training Method (CTM) and Vanilla Federated Learning (VFL), to validate the effectiveness and generalizability of Almity. Our experimental results demonstrate that our framework is not only feasible but also practical in real-world scenarios. Almity consistently enhances the performance of learning-based models, outperforming baseline training methods across all types of data distributions.