Unit testing could be used to validate the correctness of basic units of the software system under test. To reduce manual efforts in 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, ATLAS, a deep learning (DL) based approach, was proposed to generate assertions for a unit test based on other already written unit tests. Despite promising, the effectiveness of ATLAS is still limited. 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 IR-based assertion retrieval and the technique of retrieved-assertion adaptation. In addition, we propose an integration approach to combine our IR-based approach with a DL-based approach (e.g., ATLAS) to further improve the effectiveness. Our experimental results show that our IR-based approach outperforms the state-of-the-art DL-based approach, and integrating our IR-based approach with the DL-based approach can further achieve higher accuracy. Our results convey an important message that information retrieval could 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.

Recently, Python has adopted optional type annotation to support type checking and program documentation. However, to enjoy the benefits, developers have to manually write type annotations, which is recognized to be a time-consuming task. To alleviate human efforts on manual type annotation, machine-learning-based approaches have been proposed to recommend types based on code features. However, they suffer from the correctness problem, i.e., the recommended types cannot pass type checking. To address the correctness problem of the machine-learning-based approaches, in this paper, we present a static type recommendation approach, named Stray. Stray can recommend types correctly. We evaluate Stray by comparing it against four state-of-art type recommendation approaches, and find that Stray outperforms these baselines by over 30% absolute improvement in both precision and recall.