With the advancement of service computing technology, software developers tend to consume one or more web APIs, a practice that helps them avoid the behavior of reinventing the wheel. These web APIs are capable of providing services or data on the Internet; developers can use them to create feature-rich mashups. Against this backdrop, the number of web APIs across various platforms is growing rapidly, making it increasingly challenging to identify suitable ones for upcoming mashup creation. Consequently, web API recommendation has emerged as an effective means to facilitate web API discovery. We have proposed a novel web API recommendation method called R2API, which constructs the interactions between mashups and web APIs, as well as their tag usage records, into multiple homogeneous hypergraphs and then adopts HGNNs with multi-task learning to learn entity vectors for the recommendation task. While the result is encouraging, R2API’s limitation is its use of simple homogeneous hypergraphs to describe entities, failing to characterize them comprehensively and accurately. To further enhance recommendation performance, this work proposes a novel cross-view HGNNs-based web API recommendation method, namely CVH-REC. First, CVH-REC models the interactions between mashups and web APIs, as well as their tag usage records, into a multi-view knowledge graph to characterize entities more comprehensively and accurately. This knowledge graph comprises a global main hypergraph and four sub-hypergraphs from local views. Second, CVH-REC adopts a contrastive learning and multi-task learning framework to drive multiple HGNNs in jointly learning entity vectors. Third, CVH-REC leverages the SBERT model to derive the semantic vector from the mashup requirement and transfers it into the vector space of the knowledge graph with an MLP. This process enables the generation of a higher-quality requirement vector. By comparing the vector of the mashup requirement with those of web APIs, CVH-REC generates a recommendation list for mashup creation. Extensive experiments on a real-world dataset demonstrate that the proposed method outperforms baseline methods.

The Android platform provides a series of animation APIs, with which app developers can improve the implementation efficiency of UI animations—specifically, reducing the effort and time required to implement them. To assist app developers in quickly finding the suitable animation APIs, we have proposed two recommendation models called Animation2API and U-A2A. Animation2API has the capability to generate a list of available animation APIs for the UI animation task using the collaborative filtering algorithm. In contrast, U-A2A can encode both the animation API context and the UI animation task, and then predict the next animation API for the current animation implementation based on the joint encoding of the two modalities. Since U-A2A can provide real-time recommendations throughout the process of animation implementation, it is effective in assisting developers in using animation API resources. Nevertheless, U-A2A has three key limitations. First, its GRU encoder for the animation API context has difficulty in adequately capturing the long-distance dependencies and the global information. Second, its 3D CNN encoder for the UI animation task fails to effectively extract the long-distance dependencies between video frames and the spatiotemporal features at different scales. Third, U-A2A consistently treats the two modalities equally when fusing their encodings, despite the need to adaptively adjust their contribution levels according to the actual situation. To address these limitations, the paper introduces a novel animation API recommendation model named AC2Next. AC2Next adopts an encoder component based on the self-attention mechanism to encode the animation API context and the UI animation task. Specifically, it uses GRU with the self-attention mechanism as the encoder of the animation API context and applies ViViT, a Transformer architecture with self-attention mechanisms, to encode the UI animation task. Meanwhile, AC2Next utilizes its adaptive weight layer to assign appropriate weights to the animation API context and the UI animation task during the information fusion process. The experimental results show that AC2Next can outperform U-A2A in any stage of the animation implementation. When considering 1, 3, 5, and 10 animation APIs, AC2Next achieves an improvement of 31.56%, 10.01%, 5.57%, and 3.34% respectively in recommendation accuracy compared to U-A2A.

Mashup is an application that implements specific functions by integrating one or more web APIs, which are capable of providing services or data on the Internet, thus avoiding the behavior of repeatedly building wheels. With the number of web APIs on various platforms being vast, identifying the suitable web APIs for mashups has become a challenging problem for developers. In this case, researchers propose many methods to recommend available web APIs to mashup developers according to their requirements. Given that the high-order interactions between data are crucial for the recommendation tasks, this work proposes a novel web API recommendation method called R2API. R2API constructs a series of homogeneous hypergraphs from historical data and then utilizes multiple HGNNs (Hypergraph Neural Networks) to learn the vectors for nodes in the hypergraphs. HGNN excels in capturing the high-order interactions between data while effectively mitigating the over-smoothing problem. To reduce the impact of noise and atypical features in historical data and enhance the quality of node vectors, R2API adopts a multi-task joint training strategy to train all HGNNs simultaneously. Meanwhile, R2API assigns semantic vectors to nodes in the hypergraphs during HGNN training to further improve the quality of node vectors. When faced with a specific requirement, R2API identifies its related mashup nodes in the hypergraphs and learns the requirement vector based on the vectors of these nodes, so as to complete the work of web API recommendation. Experiments conducted on the ProgrammableWeb and GitHub datasets show that R2API achieves superior performance compared to baseline methods.

UI animation is a widely adopted design element in the UI of Android apps. There are many animation APIs available for a variety of purposes, and developers can utilize them to realize the UI animations to avoid reinventing the wheel and thus improve the development efficiency. However, the number of animation APIs is as high as thousands and it is non-trivial for developers to systematically master their use. Facing such a problem, we construct a multi-modal real-time animation API recommendation model called U-A2A in this paper, which can provide the available animation API for developers of Android apps in real-time throughout the animation realization according to the multi-modal information, that is, the information of UI animation task and the animation API context of current program (i.e., the animation API sequence that has been used). The reason for considering the animation API context is that realizing a UI animation requires the use of multiple animation APIs and relevant animation APIs roughly follow a sequence. U-A2A consists of two important parts: feature extractor and predictor. The feature extractor, which is constructed based on 3D CNN and GRU, can gain the combined feature of UI animation task as well as animation API context. The predictor consists of a fully connected layer as well as a softmax layer, and it can predict and recommend the next available animation API according to the result from feature extractor. Furthermore, we use the development experience about animation APIs of existing app products as the basis to adjust the parameters of U-A2A, thereby completing the training work of recommendation model. The experimental result shows that when 1, 3, 5, and 10 animation APIs are considered, U-A2A can achieve 45.13%, 65.72%, 72.97% and 81.85% accuracy respectively, which is much higher than the baseline LUPE.

UI animations, such as card movement and menu slide in/out, provide appealing user experience and enhance the usability of mobile applications. In the process of UI animation implementation, it is difficult for developers to identify suitable APIs for the animation to be implemented from a large number of APIs. Fortunately, the huge app market contains millions of apps, and they can provide valuable data resources for solving this problem. By summarizing the API usage for the same or similar animations in apps, reusable knowledge can be mined for the API recommendation. In this paper, we propose a novel method Animation2API, which mines the knowledge about APIs from existing apps and recommends APIs for UI animations. Different from existing text-based API recommendation approaches, Animation2API takes the UI animation in GIF/video format as query input. Firstly, we construct a database containing mappings between UI animations and APIs by analyzing a broad set of apps. Then, we build a UI animation feature extractor, which can be used to gain temporal-spatial feature vectors of UI animations. By comparing the temporal-spatial feature vectors between UI animations, we identify animations that are similar to the query animation from the database. Finally, we summarize the APIs used for implementing these animations and recommend a list of APIs for developers. The empirical evaluation results show that our method can achieve 82.66%Success rateand outperform the baseline Guru by 230.77% and 184.95% in terms ofPrecisionandRecallwhen considering twenty APIs. In the user study, we take the scenarios of using web search and ChatGPT to implement animations as baselines, and the results show that participants can complete animations faster (14.54%) after using Animation2API. Furthermore, participants’ positive feedbacks on the questionnaire indicate the usefulness of Animation2API.

Abstract The rapid development of Apps not only brings huge economic benefit but also causes increasingly fierce competition. In such a situation, developers are required to develop and update innovative functions to attract and retain users. Afterwards, analysing the functions of similar products can help developers formulate a well‐designed plan at the beginning of development as well as make updated strategies during the version update process. However, although there have been some methods that can be applied to extract the features from App descriptions to achieve this purpose to some extent, the features they obtained do not cover the details of App functions. Therefore, to conduct an in‐depth research on App functions, a novel method is proposed to extract App features with detailed information and an approach to integrate the gained results for further helping developers obtain the valuable knowledge better is provided. Subsequently, a series of experiments is carried out to evaluate our method. The results reveal that the proposed method can mine the features with detailed information from descriptions and integrate them effectively and also can assist developers to compare with other competitors and develop a better competitive analysis scheme.

To prevent JavaScript developers from reinventing wheels, npm ecosystem provides numerous third-party libraries for developers to realize relevant functionalities. Npm displays the tags provided by the creators for these packages to help developers find suitable ones. However, not all creators have the habit of tagging their packages, and thus npm cannot provide tag information of a lot of packages for developers to help them understand the package functionalities effectively. Considering that many tags are unrelated to the functionality of packages, we propose a method to find out the tags that are important to distinguish the functionality categories of packages and assign them to untagged packages for assisting developers in the process of retrieving the packages. Firstly, we analyze the attribute of existing tags in npm to establish category tags (functionality categories). Then, we further mine the readme of tagged packages to generate keywords for each category tag. Finally, our method identifies category tags for untagged packages by measuring the similarity between their readme and the keywords of category tags. The evaluation demonstrates that our approach has a good performance in assigning category tags to untagged packages.

Abstract With the rapid prevalence of smartphones and the dramatic proliferation of mobile applications, people tend to do everything at their fingertips, including some sensitive activities, such as bank transfers. This makes security become one important factor when recommending apps to users. However, most existing methods recommend apps only on the basis of the apps' functionalities. Even when some methods take security into account, they usually roughly group apps with functionalities and identify the products using extra permissions as risky, but this ignores a common phenomenon that these permissions may be used only to achieve the corresponding functionalities. In this paper, we propose an app recommendation method considering both functionalities and security. For functionalities, we summarized them from app descriptions and further evaluated their completion quality in different products by analyzing their related reviews. For security, we cluster apps with similar functionalities and quality and analyze the permissions of apps in a more comparable range. In this way, our method recommends apps with higher completion quality of functionalities and security degree to users according to their demands. We conducted experiments on apps collected from six categories of Google Play, and the results show that our method has a good recommendation effect.

To support the iterative development process of Apps, the goal model is not only established to describe the requirements at the early stage but also used for identifying the updating strategy in every iteration. In this process, reviews from users provide valuable information for developers to analyse the model with users sentiments. In this study, the authors combine the goal model with reviews for supporting the evolution of Apps. First, the authors introduce the reviews into the goal model as a new factor by comparing keywords. Second, the users sentiments in reviews are mined, and two kinds of information are gained by analysing the model to help developers make decisions on which goals to be improved in next version: one kind of information is about users sentiments on the goals to evaluate whether users like them; another kind is the impact of updating one goal to others. To validate the proposed approach, they conducted experiments and a survey based on the Apps in Google Play. The results show that the proposed approach can establish relationships between goals and reviews reasonably and further provide useful information for optimising the evolution strategy of the App.

Abstract Goal model is an important model in requirements engineering, and it can describe features and their relationships for supporting the development of apps. Since an app evolves continually, the goal model also needs to be updated with new requirements to guide the whole process. As the feedback of users, reviews provide an abundant resource of user requirements for updating the goal model. In this paper, we propose an approach to help developers (a) analyze reviews to gain the information of user requirements by training a classifier and defining keyword‐based linguistic rules as well as grammar‐based rules and (b) update the goal model with the extracted information, including improving existing goals and extending the model with new goals. In addition, we design a framework to represent results so that they can be understood by developers easily. According to our experiments based on the data in Google Play, the F‐measure of classifier on reviews can reach 75.76%, and the average precision for extracting requirements‐related information from reviews is 84.04%, then we can map the information to goals with the F‐measure of 70.21%. Furthermore, the survey on 22 developers shows that the information provided by us is useful for updating the goal model.