Unit testing can be used to validate the correctness of basic units of the software under test. To reduce developers’ efforts of conducting unit testing, the research community has contributed with tools that automatically generate unit test cases, including test inputs and test oracles, e.g., assertions. Recently, an approach named ATLAS based on deep learning (DL) was proposed to generate assertions for a unit test based on other already written unit tests, yet with still low effectiveness. To improve the effectiveness, in this work, we make the first attempt to leverage Information Retrieval (IR) in assertion generation and propose an IR-based approach including the technique of \IRtech{} and the technique of \Adapttech{}. We also propose an integration approach for integrating our IR-based approach and a DL-based approach such as ATLAS to further improve the effectiveness. Our evaluation results show that our proposed IR-based approach outperforms ATLAS (the state-of-the-art DL-based approach), and integrating our \IRapproach{} approach and the DL-based approach can help achieve the highest accuracy. Our results convey an important message that an \IRapproach{} approach can be competitive and worthwhile to pursue for software engineering tasks such as assertion generation, and should be seriously considered by the research community given that in recent years deep learning solutions have been over-popularly adopted by the research community for software engineering tasks.