Abstract Although the code completion model based on the Transformer architecture has achieved remarkable results, the computational complexity of its multi-head self-attention grows quadratically with the increase of sequence length, resulting in low efficiency. To this end, we propose a code completion model based on grouped attention, referred to as GACC. This method groups the attention heads of a representation in the query, key, or value so that the attention heads in the same group share the single attention head of other representations, thereby reducing the number of parameters and computational complexity. We conducted experiments on public datasets of Python and JavaScript, and the experimental results show that compared to models based on multi-head self-attention, GACC effectively reduces the time taken for the model to suggest the next code token while achieving comparable performance on Top-k and MRR metrics.

ABSTRACT Software development plays a crucial role in the modern mobile application domain, reflecting its significance through widespread application. With the continuous evolution and vast number of Android APIs, developers need to invest considerable effort in learning how to use various suitable APIs for their projects. Unfortunately, most current recommendation methods, when representing programs as source code sequences, abstract syntax trees, or API call paths, often focus only on contextual relationships while ignoring valuable information in API dependency relationships. Moreover, existing sequence models (such as RNN, LSTM) often fail to make correct predictions for low‐frequency API methods with high‐frequency suffixes, as these models tend to capture the most common API sequence patterns, causing these relatively low‐frequency but potentially more applicable APIs to be overlooked. To address this issue, we propose an API dependency path‐based Android API recommendation method, DPAPIRec. This approach combines program analysis with deep learning, which first extracts API methods and their data flow and control flow dependency relationships from a large number of Android APPs through program analysis techniques and then obtains a comprehensive API dependency paths repository. Finally, a deep learning method is applied to learn and represent these dependency relationships to improve API recommendation accuracy. Furthermore, to better extract dependency relationships, we employ an improved attention‐based LSTM model with a novel loss architecture, enhancing the global dependency relationships between APIs through a weighted mixed loss, thereby strengthening the weight of initial nodes and alleviating the problem of low‐frequency APIs with high‐frequency suffixes. Our experiments on the AndroZoo dataset demonstrate that DPAPIRec significantly outperforms baseline methods in Android API recommendation tasks, showing substantial improvements in both Accuracy and Mean Reciprocal Rank (MRR).

A well-performed capacitive power transfer (CPT) system highly depends on its compensation to achieve load-independent (LI) output and zero phase angle (ZPA) operation. It is particularly meaningful to maintain these objectives under various misalignments. This paper is devoted to a general and comprehensive method to study and explore all the potential high-order compensations. The coupler parameter variation is evaluated under different types of misalignment, and misalignment-insensitive (MI) parameters are properly designed for MI resonance. A general and straightforward decomposition and synthesis method is then proposed to generate the high-order resonant tanks. With the help of inverse ABCD parameters, all tank candidates are further judged by their capability for LI output and ZPA operation. Finally, two example CPT systems are built to verify the LI output and ZPA operation under misalignment.