Domain-aware Mashup service clustering based on LDA topic model from multiple data sources

Cao, Buqing; Liu, Xiaoqing Frank; Liu, Jianxun; Tang, Mingdong

doi:10.1016/j.infsof.2017.05.001

Cited by 62 publications

(28 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Information extraction (e.g., VSM) (Nguyen et al 2012;Zhang et al 2018;Chen et al 2020;Thomas et al 2013;Fowkes et al 2016); -Classification (e.g., Support Vector Machine -SVM) (Hindle et al 2013;Le et al 2017;Liu et al 2017;Demissie et al 2020;Zhao et al 2020;Shimagaki et al 2018;Gopalakrishnan et al 2017;Thomas et al 2013); -Clustering (e.g., K-means) (Jiang et al 2019;Cao et al 2017;Liu et al 2017;Zhang et al 2016;Altarawy et al 2018;Demissie et al 2020;Gorla et al 2014); -Structured prediction (e.g., Conditional Random Field -CRF) (Ahasanuzzaman et al 2019); -Artificial neural networks (e.g., Recurrent Neural Network -RNN) (Murali et al 2017;Le et al 2017); -Evolutionary algorithms (e.g., Multi-Objective Evolutionary Algorithm -MOEA) (Blasco et al 2020;Pérez et al 2018); -Web crawling (Nabli et al 2018). Pagano and Maalej (2013) was the only study that contributed an exploration that combined LDA with another text mining technique.…”

Section: Types Of Contributionmentioning

confidence: 99%

Topic modeling in software engineering research

2021

View full text Add to dashboard Cite

Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e., word clusters) from a corpus of textual documents. In software engineering, topic modeling has been used to analyze textual data in empirical studies (e.g., to find out what developers talk about online), but also to build new techniques to support software engineering tasks (e.g., to support source code comprehension). Topic modeling needs to be applied carefully (e.g., depending on the type of textual data analyzed and modeling parameters). Our study aims at describing how topic modeling has been applied in software engineering research with a focus on four aspects: (1) which topic models and modeling techniques have been applied, (2) which textual inputs have been used for topic modeling, (3) how textual data was “prepared” (i.e., pre-processed) for topic modeling, and (4) how generated topics (i.e., word clusters) were named to give them a human-understandable meaning. We analyzed topic modeling as applied in 111 papers from ten highly-ranked software engineering venues (five journals and five conferences) published between 2009 and 2020. We found that (1) LDA and LDA-based techniques are the most frequent topic modeling techniques, (2) developer communication and bug reports have been modelled most, (3) data pre-processing and modeling parameters vary quite a bit and are often vaguely reported, and (4) manual topic naming (such as deducting names based on frequent words in a topic) is common.

show abstract

Section: Types Of Contributionmentioning

confidence: 99%

Topic modeling in software engineering research

2021

View full text Add to dashboard Cite

show abstract

“…e LDA is a well-known unsupervised machine learning technique for natural language processing [63] and is the simplest and most popular topic modelling algorithm [64,65], which has the advantage of recognizing the hidden topics and mining deep semantics of huge amounts of textual documents through an effective way. e basic idea of the LDA is that each document exhibits a mixture of latent topics wherein each topic is characterized by a distribution over the words, i.e., per-document topic distributions and per-word topic distributions [66,67].…”

Section: Topic Modellingmentioning

confidence: 99%

Understanding Service Providers’ Competency in Knowledge‐Intensive Crowdsourcing Platforms: An LDA Approach

2021

View full text Add to dashboard Cite

Knowledge-intensive crowdsourcing (KIC) is becoming one of the most promising domains of crowdsourcing by leveraging human intelligence and building a large labor-intensive service network. In this network, the service providers (SPs) constitute the backbone of the KIC platform and play an important role in connecting the platform and service requesters. The SPs are a group of distributed crowds with a complex composition and high level of uncertainty, resulting in great challenges in service quality and platform management. Understanding the SPs’ competency is an effective way for the platform to manage them. Therefore, we attempt to connect the competency analysis to the environment of KIC to investigate and identify the criteria of SPs’ competency (i.e., the competency factors and dimensions required for being competent for the SPs’ business). To this end, we leverage the Latent Dirichlet Allocation (LDA) model to explore and extract hidden competency dimensions from online interview records. We then introduce the competency theory to identify and label the competency factors and dimensions and construct the three-level KSAT competency model, which presents a comprehensive vision of the SPs’ performance standards in the context of KIC. Given the competency criteria in the KSAT competency model, we use the Best-Worst Method (BWM) to determine their weights, which reflect their importance when evaluating the SPs’ competency from the platforms’ perspective. The results show that skill and knowledge are the two most important competency factors, and customer relationship management and communication ability are the two most valuable competency dimensions when evaluating the SPs’ competency. Furthermore, the KSAT competency model can be applied to analyze the competency of individuals or organizations in many other industries as well.

show abstract

“…Mashups to derive potential topics, and they did not use FMs to model and train these potential topics to predict the probability of Mashup calling services to obtain more accurate service recommendation. In our previous work [8,27,28], we mainly address on LDA or enhanced LDA topic model for Web services clustering [27,28] and also exploit word embedding technique to enhance the accuracy of service clustering [8]. Driven by these methods, we combine FMs and word-embedded enhanced HDP for recommending mobile services to build novel Mashup application.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Cao

Liu

et al. 2019

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

Recently, Mashup is becoming a promising software development method in the mobile service computing environment, which enables software developers to compose existing mobile services to create new or value-added composite RESTful web application. Due to the rapid increment of mobile services on the Internet, it is difficult to find the most suitable services for building user-desired Mashup application. In this paper, we integrate word embeddings enhanced hierarchical Dirichlet process and factorization machines to recommend mobile services to build high-quality Mashup application. This method, first of all, extends the description documents of Mashup applications and mobile services by using Word2vec tool and derives latent topics from the extended description documents of Mashup and mobile services by exploiting the hierarchical Dirichlet process. Secondly, the factorization machine is applied to train these latent topics to predict the probability of mobile services invoked by Mashup and recommend mobile services with high-quality for Mashup development. Finally, the performance of the proposed method is comprehensively evaluated. The experimental results indicate that compared with the existing recommendation methods, the proposed method has significant improvements in MAE and RMSE.

show abstract

Domain-aware Mashup service clustering based on LDA topic model from multiple data sources

Cited by 62 publications

References 18 publications

Topic modeling in software engineering research

Topic modeling in software engineering research

Understanding Service Providers’ Competency in Knowledge‐Intensive Crowdsourcing Platforms: An LDA Approach

Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Contact Info

Product

Resources

About