Stack Overflow (SO) provides informal documentation for APIs in response to questions that express API related developer needs. Navigating the information available on SO and getting information related to a particular API and need is challenging due to the vast amount of questions and answers and the tag-driven structure of SO. In this paper we focus on identifying and classifying fine-grained developer needs expressed in sentences of API-related SO questions, as well as the specific information types used to express such needs, and the different roles APIs play in these questions and their answers. We derive a taxonomy, complementing existing ones, through an empirical study of 266 SO posts. We then develop and evaluate an approach for the automated identification of the fine-grained developer needs in SO threads, which takes a thread as input and outputs the corresponding developer needs, the types of information expressing them, and the roles of API elements relevant to the needs. To show a practical application of our taxonomy, we introduce and evaluate an approach for the automated retrieval of SO questions, based on these developer needs.

In this paper, we present OpenAPIDocGen2, a tool that generates on-demand class documentation based on source code and documentation analysis. For a given class, OpenAPIDocGen2 generates a combined documentation for it, which includes functionality descriptions, directives, domain concepts, usage examples, class/method roles, key methods, relevant classes/methods, characteristics and concepts classification, and usage scenarios.

This report introduces the approach that we have designed and implemented for the DeClutter challenge of Doc-Gen2, which detects non-informative code comments. The approach combines both comment based text classification and code context based prediction. Based on the approach, our "fduse" team achieved the best F1 score (0.847) in the competition.

Source code summaries are concise representations, in form of text and/or code, of complex code elements and are meant to help developers gain a quick understanding that in turns help them perform specific tasks. Generation of summaries that are task-specific is still a challenge in the automatic code summarization field. We propose an approach for generating on-demand, extrinsic hybrid summaries for API classes, relevant to a programming task, formulated as a natural language query. The summaries include the most relevant sentences extracted from the API reference documentation and the most relevant methods.

A user-centric no-cell (UCNC) framework has been proposed which enables different level coordination over a large number of TRPs. This paper presents some key technologies in UCNC framework with the focus on PoC of UL grant- free transmissions in UCNC architecture. Massive connectivity support is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant- free transmission based on SCMA (sparse code multiple access) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectiv- ity requirements. Extensive lab tests have been conducted to verify the performance matrices. Taken LTE as a baseline for comparison purpose, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.

A 5G mobile network has evolved from a cell centric radio access network to a user centric one. A user-centric no-cell (UCNC) framework has been proposed, which enables physical layer coordination over a large number of transmission and reception points for each mobile user to always experience cell-center-like communications. This paper presents some key technologies in the UCNC framework. The focus is on proof of concept of UL grant-free transmissions in the UCNC architecture. Massive connectivity supporting a huge number of devices is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant-free transmission based on sparse code multiple access (SCMA) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectivity requirements. Grant-free transmission conducted in RRC connected state can reduce signaling overhead, while in energy conserved operation (ECO) state, it can significantly reduce the user plane transmission latency. Extensive laboratory testing has been conducted in a collaborative project of Huawei and Telefonica to verify the performance matrices. Taking LTE as a baseline for comparison purposes, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with the SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.