Keywords‐driven web APIs group recommendation for automatic app service creation process

Gong, Wei; Lv, Chao; Duan, Yucong; Liu, Zengguang; Khosravi, Mohammad R.; Qi, Lianyong; Dou, Wanchun

doi:10.1002/spe.2902

Cited by 16 publications

(7 citation statements)

References 33 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The future scope of the present investigation is to utilize the advanced deep learning models including 29 – 33 for other regions of the world 34 – 36 . Another important future scope of the present investigation is to develop, host, and deploy near real time environmental monitoring status based on high end APIs 37 , 38 .…”

Section: Discussionmentioning

confidence: 99%

Analysis of environmental factors using AI and ML methods

Haq

Ahmed

Khan

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological models and prediction of the spread of forest. Long Short Term Memory (LSTM) model for the time series forecasting of snow cover, temperature, and normalized difference vegetation index (NDVI) are studied in this research work. Artificial neural networks (ANN) are widely used for forecasting time series due to their adaptive computing nature. LSTM and Recurrent neural networks (RNN) are some of the several architectures provided in a class of ANN. LSTM is a kind of RNN that has the capability of learning long-term dependencies. We followed a coarse-to-fine strategy, providing reviews of various related research materials and supporting it with the LSTM analysis on the dataset of Himachal Pradesh, as gathered. Environmental factors of the Himachal Pradesh region are forecasted using the dataset, consisting of temperature, snow cover, and vegetation index as parameters from the year 2001–2017. Currently, available tools and techniques make the presented system more efficient to quickly assess, adjust, and improve the environment-related factors analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

Analysis of environmental factors using AI and ML methods

Haq

Ahmed

Khan

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Other efforts [64][65][66][67][68][69][70][71] have been done in context of recommendation projects for developers and vice-versa. For instance, Liu et al 72 recommend software features from the users' perspective for designers of the project.…”

Section: Related Workmentioning

confidence: 99%

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

2023

View full text Add to dashboard Cite

Summary Software developers must collaborate at all stages of the software life‐cycle to create successful complex software systems. To enable this collaboration, social coding platforms, for example, GitHub, include an increasing number of tools to support collaboration. However, for large projects with hundreds of dynamic developers, such as several successful open–source projects, it can be complex to find developers with the same interest and familiarity and thus, gain suitable collaborations and new insights. In this context, resources and efforts may be wasted, discouraging many developers from contributing. Moreover, it can be costly to manage many contributions, which is another challenge for the maintainer who wants to take advantage of this small, timid, but valuable contribution made by a volunteer developer in a short time. In this context, this paper presents an empirical study aiming to evaluate two strategies to recommend collaborators based on co‐changed files. Inspired in the TF–IDF (Term Frequency–Inverse Document Frequency) weighting scheme established in the Information Retrieval field, these strategies first estimate the importance of relevant files modified by developers and use these estimates to represent each developer “profile”. As a second step, they estimate the similarity between developers using the Cosine metric, providing top‐ranked developers according to this measure as recommendations. We evaluated these strategies based on an extensive survey with 102 real–world developers. We observed that developers have interest and familiarity with the co‐changed files for all strategies evaluated. These considerations are of relevance because many opportunities for contributions to the project are linked to coding. Thus, theses results may indicate one less barrier for improving collaboration among developers. Overall, the strategies present an acceptance rate of up to 81%, contributing to the discovery of further collaborators.

show abstract

“…Te GST problem has been found useful in diferent applications and has attracted much research interest in recent years. Te studies most closely related to our problem include the computation of a minimum spanning tree (MST) in a temporal graph [3,9,29], progressive GST searching in a static graph [20,[30][31][32], keyword searching in relational databases [18,33], and reconstruction of an epidemic over time [34][35][36][37][38][39][40][41].…”

Section: Related Workmentioning

confidence: 99%

“…Te efcient web APIs recommendation was studied in [34][35][36][37][38][39][40][41]. Tese works model the web APIs recommendation problem as a group Steiner tree search problem.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Dynamic Programming Algorithm for Finding Group Steiner Trees in Temporal Graphs

Ge,

Chen,

Kong

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

The computation of a group Steiner tree (GST) in various types of graph networks, such as social network and transportation network, is a fundamental graph problem in graphs, with important applications. In these graphs, time is a common and necessary dimension, for example, time information in social network can be the time when a user sends a message to another user. Graphs with time information can be called temporal graphs. However, few studies have been conducted on GST in terms of temporal graphs. This study analyzes the computation of GST for temporal graphs, i.e., the computation of temporal GST (TGST), which is shown to be an NP-hard problem. We propose an efficient solution based on a dynamic programming algorithm for our problem. This study adopts new optimization techniques, including graph simplification, state pruning, and A ∗ search, are adopted to dramatically reduce the algorithm search space. Moreover, we consider three extensions for our problem, namely the TGST with unspecified tree root, the progressive search of TGST, and the top-N search of TGST. Results of the experimental study performed on real temporal networks verify the efficiency and effectiveness of our algorithms.

show abstract

Keywords‐driven web APIs group recommendation for automatic app service creation process

Cited by 16 publications

References 33 publications

Analysis of environmental factors using AI and ML methods

Analysis of environmental factors using AI and ML methods

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

An Efficient Dynamic Programming Algorithm for Finding Group Steiner Trees in Temporal Graphs

Contact Info

Product

Resources

About