Human Activity Recognition (HAR), which aims to identify and classify human behaviors through multi-source sensing data, has become a long-standing research hotspot in ubiquitous computing, enabling a wide range of real-world applications. Focusing on Wearable HAR (WHAR), this survey provides a system-level synthesis organized around the life cycle of WHAR systems, collating fundamental theories, key technologies, and implementation details across data acquisition, model construction, and system deployment. Rather than treating sensing configuration, learning paradigm selection, evaluation protocol design, and deployment constraints as isolated topics, we emphasize their inherent interdependencies, demonstrating how early design choices propagate through the entire development pipeline to shape final system performance. We further establish a unified taxonomy of HAR paradigms based on data collection devices, clarifying the boundaries between vision-based, ambient sensor-based, and wearable sensor-based approaches to eliminate long-standing classification ambiguities. Additionally, we systematically review mainstream supervised WHAR models, emerging learning paradigms, deployment-oriented evaluation criteria, and cutting-edge directions including foundation models and Large Language Model (LLM)-based activity understanding. By integrating all aspects into a coherent life cycle framework, this survey aims to serve as a design-oriented reference for researchers and practitioners building accurate, efficient, and deployable WHAR systems.

It is well known that user feedback (i.e., reviews) plays an essential role in mobile app maintenance. Users upload their troubles, app issues, or praises, to help developers refine their apps. However, reading tremendous amounts of reviews to retrieve useful information is a challenging job. According to our manual studies, reviews are full of repetitive opinions, thus developers could stop reading reviews when no more new helpful information appears. Developers can extract useful information from partial reviews to ameliorate their app and then develop a new version. However, it is tough to have a good trade-off between getting enough useful feedback and saving more time. In this paper, we propose a novel approach, named STRE, which utilizes historical reviews to suggest the time when most of the useful information appears in reviews of a certain version. We evaluate STRE on 62 recent versions of five apps from Apple's App Store. Study results demonstrate that our approach can help developers save their time by up to 98.33% and reserve enough useful reviews before stopping to read reviews such that developers do not spend additional time in reading redundant reviews over the suggested stopping time. At the same time, STRE can complement existing review categorization approaches that categorize reviews to further assist developers. In addition, we find that the missed top-word-related reviews appearing after the suggested stopping time contain limited useful information for developers. Finally, we find that 12 out of 13 of the emerging bugs from the studied versions appear before the suggested stopping time. Our approach demonstrates the value of automatically refining information from reviews.

Although the use of APIs (Application Programming Interfaces) in software program development can effectively improve development efficiency, developers still need to spend more time in finding suitable APIs. To improve the overall development efficiency, many API recommendation approaches have been proposed. However, they could not make good use of the information in the source code, especially for the structural information. The PDG (Program Dependence Graph) of source code can contain both syntactic and structural information, which can be great representations of the source code. Based on the PDG, we propose a new approach, called JARST (Java API Recommendation combining Structural with Textual code information), which recommends the appropriate APIs by analyzing the structure information and text information of the source code. The JARST approach uses a graph neural network to learn source code structure information of PDG and uses a multi-modal approach to learn the text information in the source code. Finally, we combine the structural and textual information of the source code to implement API recommendations. We collect 625 open source Java projects from Github as our experimental objects. The experimental results show that JARST can provide accurate APIs to help software developers facilitate development activities. Moreover, it performs better than the cutting-edge studies including APIRes-CST and APIREC with higher top-k accuracy values. In detail, the improvement achieves up to 35.3%.