Legal Judgment Prediction (LJP) seeks to predict case outcomes given available case information, offering practical value for both legal professionals and laypersons. However, a key limitation of existing LJP models is their limited adaptability to statutory revisions. Current SOTA models are neither designed nor evaluated for statutory revisions. To bridge this gap, we introduce LawShift, a benchmark dataset for evaluating LJP under statutory revisions. Covering 31 fine-grained change types, LawShift enables systematic assessment of SOTA models' ability to handle legal changes. We evaluate five representative SOTA models on LawShift, uncovering significant limitations in their response to legal updates. Our findings show that model architecture plays a critical role in adaptability, offering actionable insights and guiding future research on LJP in dynamic legal contexts.

Recommending APIs is a practical and essential feature of IDEs. Improving the accuracy of API recommendations is an effective way to improve coding efficiency. With the success of deep learning in software engineering, the state-of-the-art (SOTA) performance of API recommendation is also achieved by deep-learning-based approaches. However, existing SOTAs either only consider the API sequences in the code snippets or rely on complex operations for extracting hand-crafted features, all of which have potential risks in under-encoding the input code snippets and further resulting in sub-optimal recommendation performance. To this end, this article proposes to utilize the code understanding ability of existing general code P re- T raining M odels to fully encode the input code snippet to improve the accuracy of API Rec ommendation, namely, PTM-APIRec . To ensure that the code semantics of the input are fully understood and the API recommended actually exists, we use separate vocabularies for the input code snippet and the APIs to be predicted. The experimental results on the JDK and Android datasets show that PTM-APIRec surpasses existing approaches. Besides, an effective way to improve the performance of PTM-APIRec is to enhance the pre-trained model with more pre-training data (which is easier to obtain than API recommendation datasets).