API recommendation for event-driven Android application development

Yuan, Weizhao; Nguyen, Hoang H.; Jiang, Lingxiao; Chen, Yuting; Zhao, Jianjun; Yu, Haibo

doi:10.1016/j.infsof.2018.10.010

Cited by 38 publications

(14 citation statements)

References 16 publications

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“…However, it is difficult for us to ensure that value. Same as the previous study, 21 we optimistically assume that the relevant APIs are the top‐N APIs in our recommendation lists that match an answer from Stack Overflow for a query question from the total set of relevant APIs for the query. Then we use the following formula to calculate the value of MAP@N :

M A P @ N = \frac{\sum_{i = 1}^{false| f e a t u r e false|} A P @ N}{| f e a t u r e |} .

Experimental result…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…To make a more comprehensive evaluation of our approach, we further compare our approach with LibraryGuru, an excellent work proposed in Yuan et al 21 There are mainly three reasons for us to choose LibraryGuru as the compared object: First, it has a similar purpose as us that is recommending APIs for Android application development (especially for implementing a given functionality); secondly, LibraryGuru (published in 2019) could reflect the performances of recent studies and it gives an open website for using, which makes us can get the compared results easily; finally, the work also uses Precision@N and MAP@N as metrics for performance evaluation, so we can compare our performance with it directly.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In the experiment, we had directly compared the recommended results gained from the website of LibraryGuru with our approach for the queries in our experiments at the beginning, but we realize the comparison is not fair because the questions are related to the development of app features, which is the kind of questions we want to solve, but the LibraryGuru could not achieve as good performance as us for answering them. To alleviate the bias, we finally decide to quote the performance of LibraryGuru from Yuan et al 21 as the baseline to get the comparison results.…”

Section: Experiments and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Application programming interface recommendation according to the knowledge indexed by app feature mined from app stores

Liu

et al. 2021

J Software Evolu Process

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Application programming interfaces (APIs) play an important role in the increasingly competitive mobile application development industry, as they can greatly improve the efficiency of app development. However, finding proper APIs is often time‐consuming for the gap between the knowledge of APIs and app features. To solve this problem, we give an approach to summarize the wisdom of developers contained in the products in app stores and establish the system of API knowledge indexed by app features for the API recommendation. First, we extract features from the app descriptions and define the feature framework. Second, we parse the APK files of apps to gain the methods in code and APIs called by them and further introduce such API knowledge into the feature framework by utilizing method names as bridges. Finally, according to features in developers' queries, we locate corresponding feature nodes in the API knowledge system and recommend related API knowledge to developers. We conduct experiments based on 38,952 apps from five categories on Google Play, and the experimental results show that our approach has a good recommendation effect for the queries on app features.

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M A P @ N = \frac{\sum_{i = 1}^{false| f e a t u r e false|} A P @ N}{| f e a t u r e |} .

Experimental result…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Application programming interface recommendation according to the knowledge indexed by app feature mined from app stores

Liu

et al. 2021

J Software Evolu Process

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show abstract

“…An abstract syntax tree (AST) is a tree-like representation of the abstract syntactic structure of the source code, where each node is a construct occurring in the code [ 27 , 28 , 29 ]. The reason for the abstraction is that the abstract syntax tree does not represent every detail of the appearance of the real syntax.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer

Dai¹,

et al. 2021

Sensors

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With the development of blockchain technologies, many Ponzi schemes disguise themselves under the veil of smart contracts. The Ponzi scheme contracts cause serious financial losses, which has a bad effect on the blockchain. Existing Ponzi scheme contract detection studies have mainly focused on extracting hand-crafted features and training a machine learning classifier to detect Ponzi scheme contracts. However, the hand-crafted features cannot capture the structural and semantic feature of the source code. Therefore, in this study, we propose a Ponzi scheme contract detection method called MTCformer (Multi-channel Text Convolutional Neural Networks and Transofrmer). In order to reserve the structural information of the source code, the MTCformer first converts the Abstract Syntax Tree (AST) of the smart contract code to the specially formatted code token sequence via the Structure-Based Traversal (SBT) method. Then, the MTCformer uses multi-channel TextCNN (Text Convolutional Neural Networks) to learn local structural and semantic features from the code token sequence. Next, the MTCformer employs the Transformer to capture the long-range dependencies of code tokens. Finally, a fully connected neural network with a cost-sensitive loss function in the MTCformer is used for classification. The experimental results show that the MTCformer is superior to the state-of-the-art methods and its variants in Ponzi scheme contract detection.

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“…To evaluate the effectiveness of data-driven approach in ADM, various evaluation metrics have been widely used in ADM (Gu et al, 2016;Huang et al, 2018;Li, Jiang, et al, 2018;Raghothaman et al, 2016;Rahman et al, 2016;Yuan et al, 2019;Zhang, Niu, Keivanloo, & Zou, 2018).…”

Section: Rq5: Evaluation Metrics Used In Admmentioning

confidence: 99%

Data‐driven approach to application programming interface documentation mining: A review

Jing

Zhang

et al. 2020

WIREs Data Min & Knowl

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Application programming interface (API) is an important form of software reuse. API documentations, such as API specifications, tutorials, and online forums, are valuable learning resources for reusing the APIs. In recent years, many data-driven API documentation mining (ADM) methods have been proposed. These methods mine API documentations and return API-related information to help developers better understand and reuse APIs. These methods treat documentations as unstructured data and apply various data mining techniques to analyze the documentation data. Currently, there is no comprehensive review of the data-driven approach to API documentation mining. This review aims to fill in this gap by analyzing and discussing the state of the art ADM papers. We survey 32 representative papers published in prominent software engineering journals and conferences in recent 5 years (January 2014-July 2019). We analyze their mining tasks, mined data, problems, data mining techniques, and evaluation metrics. Based on the survey results, we point out research challenges and future research directions in this area.

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API recommendation for event-driven Android application development

Cited by 38 publications

References 16 publications

Application programming interface recommendation according to the knowledge indexed by app feature mined from app stores

Application programming interface recommendation according to the knowledge indexed by app feature mined from app stores

Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer

Data‐driven approach to application programming interface documentation mining: A review

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